AU2015202200A1 - Mobile human interface robot - Google Patents

Abstract

A mobile robot (100) including a drive system (200) having a forward drive direction (F), a controller (500) in communication with the drive system, and a 5 volumetric point cloud imaging device (450) supported above the drive system and directed to be capable of obtaining a point cloud from a volume of space that includes a floor plane (5) in a direction of movement of the mobile robot. A dead zone sensor (490) has a detection field (492) arranged to detect an object in a volume of space (453) undetectable by the volumetric point cloud imaging device. The controller receives point 10 cloud signals from the imaging device and detection signals from the dead zone sensor and issues drive commands to the drive system based at least in part on the received point cloud and detection signals. 450b 4i5 150 -0a11 30 F ob/120

Description

Mobile Human Interface Robot CROSS REFERENCE TO RELATED APPLICATIONS [0001] This patent application claims priority to U.S. Provisional Application 61/428,717, filed on December 30, 2010; U.S. Provisional Application 61/428,734, filed 5 on December 30, 2010; U.S. Provisional Application 61/428,759, filed on December 30, 2010; U.S. Provisional Application 61/429,863, filed on January 5, 2011, U.S. Provisional Application 61/445,408, filed on February 22, 2011; U.S. Provisional Application 61/445,473, filed on February 22, 2011; U.S. Provisional Application 61/478,849, filed on April 25, 2011; and under 35 U.S.C. §120 to U.S. Patent Application 10 13/032,312, filed on February 22, 2011; and U.S. Patent Application 13/032,228, filed on February 22, 2011. The disclosures of these prior applications are considered part of the disclosure of this application and are hereby incorporated by reference in their entireties. TECHNICAL FIELD [0002] This disclosure relates to mobile human interface robots. 15 BACKGROUND [0003] A robot is generally an electro-mechanical machine guided by a computer or electronic programming. Mobile robots have the capability to move around in their environment and are not fixed to one physical location. An example of a mobile robot that is in common use today is an automated guided vehicle or automatic guided vehicle 20 (AGV). An AGV is generally a mobile robot that follows markers or wires in the floor, or uses a vision system or lasers for navigation. Mobile robots can be found in industry, military and security environments. They also appear as consumer products, for entertainment or to perform certain tasks like vacuum cleaning and home assistance. SUMMARY 25 [0004] One aspect of the disclosure provides a mobile robot that includes a drive system having a forward drive direction, a controller in communication with the drive 1 system, and a volumetric point cloud imaging device supported above the drive system and directed to be capable of obtaining a point cloud from a volume of space that includes a floor plane in a direction of movement of the mobile robot. A dead zone sensor has a detection field arranged to detect an object in a volume of space undetectable 5 by the volumetric point cloud imaging device. The controller receives point cloud signals from the imaging device and detection signals from the dead zone sensor and issues drive commands to the drive system based at least in part on the received point cloud and detection signals. [0005] Implementations of the disclosure may include one or more of the following 10 features. In some implementations, the dead zone sensor includes at least one of a volumetric point cloud imaging device, a sonar sensor, a camera, an ultrasonic sensor, LIDAR, LADAR, an optical sensor, and an infrared sensor. The detection field of the dead zone sensor may envelope a volume of space undetectable by the volumetric point cloud imaging device (i.e., a dead zone). In some examples, the volume of space 15 undetectable by the volumetric point cloud imaging device is defined by a first angle, a second angle and a radius (e.g., 570 x 450 x 50 cm). The detection field of the dead zone sensor may be arranged between the volumetric point cloud imaging device and a detection field of the volumetric point cloud imaging device. In some examples, the dead zone sensor has a field of view extending at least 3 meters outward from the dead zone 20 sensor. In this example, the dead zone sensor can be dual-purposed for relative short range within the dead zone and as a long range sensor for detecting objects relatively far away for path planning and obstacle avoidance. [0006] In some implementations, the robot includes an array of dead zone sensors with at least one dead zone sensor having its detection field arranged to detect an object 25 in the volume of space undetectable by the volumetric point cloud imaging device. Te array of dead zone sensors may be arranged with their fields of view along the forward drive direction or evenly disbursed about a vertical center axis defined by the robot. [0007] The imaging device, in some examples, emits light onto a scene about the robot and captures images of the scene along the drive direction of the robot. The images 30 include at least one of (a) a three-dimensional depth image, (b) an active illumination 2 image, and (c) an ambient illumination image. The controller determines a location of an object in the scene based on the images and issues drive commands to the drive system to maneuver the robot in the scene based on the object location. The imaging device may determine a time-of-flight between emitting the light and receiving reflected light from 5 the scene. The controller uses the time-of-flight for determining a distance to the reflecting surfaces of the object. [0008] In some implementations, the imaging device includes a light source for emitting light onto the scene and an imager for receiving reflections of the emitted light from the scene. The light source may emit the light in intermittent pulses, for example, at 10 a first, power saving frequency and upon receiving a sensor event emits the light pulses at a second, active frequency. The sensor event may include a sensor signal indicative of the presence of an object in the scene. The imager may include an array of light detecting pixels. [0009] The imaging device may include first and second portions (e.g., portions of 15 one sensor or first and second imaging sensors). The first portion is arranged to emit light substantially onto the ground and receive reflections of the emitted light from the ground. The second portion is arranged to emit light into a scene substantially above the ground and receive reflections of the emitted light from the scene about the robot. [0010] In some implementations, the imaging device includes a speckle emitter 20 emitting a speckle pattern of light onto a scene along a drive direction of the robot and an imager receiving reflections of the speckle pattern from an object in the scene. The controller stores reference images of the speckle pattern as reflected off a reference object in the scene. The reference images are captured at different distances from the reference object. The controller compares at least one target image of the speckle pattern as 25 reflected off a target object in the scene with the reference images for determining a distance of the reflecting surfaces of the target object. In some instances, the controller determines a primary speckle pattern on the target object and computes at least one of a respective cross-correlation and a decorrelation between the primary speckle pattern and the speckle patterns of the reference images. 3 [0011] To increase a lateral field of view, the imaging sensor may scan side-to-side with respect to the forward drive direction. Similarly, to increase a vertical field of view, the imaging sensor may scan up-and-down. [0012] In some implementations, the controller ceases use of the received point cloud 5 signals after a threshold period of time after receipt for issuing drive commands to the drive system. The controller may suspend cessation of use of the received point cloud signals upon determining the presence of an object in the volume of space undetectable by the volumetric point cloud imaging device based on the received detection signals from the dead zone sensor. Moreover, the controller may continue ceasing use of the 10 received point cloud signals after the threshold period of time after receipt upon determining that the volume of space undetectable by the volumetric point cloud imaging device is free of any objects, for example, based on the received detection signals from the dead zone sensor. [0013] Another aspect of the disclosure provides a mobile robot that includes a base 15 and a holonomic drive system supported by the base and defining a vertical axis (Z). The holonomic drive system maneuvers the robot over a work surface of a scene. The robot includes a controller in communication with the drive system, a leg extending upward from the base, and a torso supported by the leg. The torso rotates about the vertical axis with respect to the base. At least one imaging sensor (e.g., a volumetric point cloud 20 imaging device) is disposed on the torso and captures a volumetric point cloud (e.g., three-dimensional images) of the scene about the robot. The rotating torso moves the imaging sensor in a panning motion about the vertical axis providing up to a 3600 field of view about the robot. [0014] In some implementations, the at least one imaging sensor has an imaging axis 25 arranged to aim downward along a forward drive direction of the drive system. The at least one imaging sensor may include a first imaging sensor having an imaging axis arranged to aim downward along a forward drive direction of the drive system and a second imaging sensor having an imaging axis arranged to aim away from the torso parallel to or above the work surface. Moreover, the at least one imaging sensor may 4 scan side-to-side with respect to the forward drive direction to increase a lateral field of view and/or up-and-down to increase a vertical field of view of the imaging sensor. [0015] The at least one imaging sensor may include a speckle emitter emitting a speckle pattern of light onto the scene and an imager receiving reflections of the speckle 5 pattern from an object in the scene. The controller stores reference images of the speckle pattern as reflected off a reference object in the scene. The reference images are captured at different distances from the reference object. The controller compares at least one target image of the speckle pattern as reflected off a target object in the scene with the reference images for determining a distance of the reflecting surfaces of the target object. 10 The imaging sensor may capture images of the scene along a drive direction of the robot. The images include at least one of (a) a three-dimensional depth image, (b) an active illumination image, and (c) an ambient illumination image. [0016] The controller may determine a location of an object in the scene based on the image comparison and issues drive commands to the drive system to maneuver the robot 15 in the scene based on the object location. In some examples, the controller determines a primary speckle pattern on the target object and computes at least one of a respective cross-correlation and a decorrelation between the primary speckle pattern and the speckle patterns of the reference images. [0017] The imaging sensor may be a volumetric point cloud imaging device 20 positioned at a height of greater than 2 feet above the work surface and directed to be capable of obtaining a point cloud from a volume of space that includes a floor plane in a direction of movement of the robot. The imaging sensor may have a horizontal field of view of at least 45 degrees and a vertical field of view of at least 40 degrees and/or a range of between about 1 meter and about 5 meters. In some examples, the imaging 25 sensor has a latency of about 44 ms, and imaging output of the imaging sensor may receive a time stamp for compensating for latency. [0018] In some implementations, the robot includes a dead zone sensor having a detection field arranged to detect an object in a volume of space undetectable by the volumetric point cloud imaging device. The dead zone sensor may include at least one of 30 a volumetric point cloud imaging device, a sonar sensor, a camera, an ultrasonic sensor, 5 LIDAR, LADAR, an optical sensor, and an infrared sensor. The detection field of the dead zone sensor may envelope a volume of space undetectable by the volumetric point cloud imaging device (i.e., a dead zone). In some examples, the volume of space undetectable by the volumetric point cloud imaging device is defined by a first angle, a 5 second angle and a radius (e.g., 570 x 450 x 50 cm). The detection field of the dead zone sensor may be arranged between the volumetric point cloud imaging device and a detection field of the volumetric point cloud imaging device. In some examples, the dead zone sensor has a field of view extending at least 3 meters outward from the dead zone sensor. In this example, the dead zone sensor can be dual-purposed for relative short 10 range within the dead zone and as a long range sensor for detecting objects relatively far away for path planning and obstacle avoidance. [0019] In some implementations, the robot includes an array of dead zone sensors with at least one dead zone sensor having its detection field arranged to detect an object in the volume of space undetectable by the volumetric point cloud imaging device. Te 15 array of dead zone sensors may be arranged with their fields of view along the forward drive direction or evenly disbursed about a vertical center axis defined by the robot. [0020] In some implementations, the controller ceases use of received point cloud signals after a threshold period of time for issuing drive commands to the drive system. The controller may suspend cessation of use of the received point cloud signals upon 20 determining the presence of an object in the volume of space undetectable by the imaging sensor based on received detection signals from the dead zone sensor. Moreover, the controller may continue ceasing use of the received point cloud signals after the threshold period of time upon determining that the volume of space undetectable by the imaging sensor is free of any objects, for example, based on the received detection signals from 25 the dead zone sensor. [0021] The torso may rotate with respect to the leg and/or the leg may rotate with respect with the base about the vertical axis. In some examples, the leg has a variable height. [0022] In yet another aspect, a method of object detection for a mobile robot includes 30 rotating an imaging sensor about a vertical axis of the robot. The imaging sensor emits 6 light onto a scene about the robot and captures images of the scene. The images include at least one of (a) a three-dimensional depth image, (b) an active illumination image, and (c) an ambient illumination image. The method further includes determining a location of an object in the scene based on the images, assigning a confidence level for the object 5 location, and maneuvering the robot in the scene based on the object location and corresponding confidence level. [0023] In some implementations, the method includes constructing an object occupancy map of the scene. The confidence level of each object location may be degraded over time until updating the respective object location with a newly determined 10 object location. The method may include maneuvering the robot to at least one of: a) contact the object and follow along a perimeter of the object, or b) avoid the object. [0024] In some examples, the method includes detecting an object in a volume of space undetectable by the imaging sensor, such as by using a dead zone sensor having a detection field arranged to detect an object in the volume of space undetectable by the 15 imaging sensor, and ceasing degradation of the confidence level of the detected object. The method may include continuing degradation of the confidence level of the detected object upon detecting that the volume of space undetectable by the imaging sensor is free of that object. [0025] The method may include emitting the light onto the scene in intermittent 20 pulses, optionally altering a frequency of the emitted light pulses. The light pulses may be emitted at a first, power saving frequency and upon receiving a sensor event, emitted at a second, active frequency. The sensor event may include a sensor signal indicative of the presence of an object in the scene. [0026] The method may include constructing the three-dimensional depth image of 25 the scene by emitting a speckle pattern of light onto the scene, receiving reflections of the speckle pattern from the object in the scene, and storing reference images of the speckle pattern as reflected off a reference object in the scene. The reference images are captured at different distances from the reference object. The method further includes capturing at least one target image of the speckle pattern as reflected off a target object in the scene 30 and comparing the at least one target image with the reference images for determining a 7 distance of the reflecting surfaces of the target object. The method may include determining a primary speckle pattern on the target object and computing at least one of a respective cross-correlation and a decorrelation between the primary speckle pattern and the speckle patterns of the reference images. Moreover, the method may include 5 capturing frames of reflections of the emitted speckle pattern off surfaces of the target object at a frame rate, e.g., between about 10 Hz and about 90 Hz, and optionally resolving differences between speckle patterns captured in successive frames for identification of the target object. [0027] The details of one or more implementations of the disclosure are set forth in 10 the accompanying drawings and the description below. Other aspects, features, and advantages will be apparent from the description and drawings, and from the claims. DESCRIPTION OF DRAWINGS [0028] FIG. 1 is a perspective view of an exemplary mobile human interface robot. [0029] FIG. 2 is a schematic view of an exemplary mobile human interface robot. 15 [0030] FIG. 3 is an elevated perspective view of an exemplary mobile human interface robot. [0031] FIG. 4A is a front perspective view of an exemplary base for a mobile human interface robot. [0032] FIG. 4B is a rear perspective view of the base shown in FIG. 4A. 20 [0033] FIG. 4C is a top view of the base shown in FIG. 4A. [0034] FIG. 5A is a front schematic view of an exemplary base for a mobile human interface robot. [0035] FIG. 5B is a top schematic view of an exemplary base for a mobile human interface robot. 25 [0036] FIG. 6 is a front perspective view of an exemplary torso for a mobile human interface robot. [0037] FIG. 7 is a front perspective view of an exemplary neck for a mobile human interface robot. 8 [0038] FIGS. 8A-8G are schematic views of exemplary circuitry for a mobile human interface robot. [0039] FIG. 9 is a schematic view of an exemplary mobile human interface robot. [0040] FIG. 10A is a perspective view of an exemplary mobile human interface robot 5 having multiple sensors pointed toward the ground. [0041] FIG. 10B is a perspective view of an exemplary mobile robot having multiple sensors pointed parallel with the ground. [0042] FIG. 11 is a schematic view of an exemplary imaging sensor sensing an object in a scene. 10 [0043] FIG. 12 is a schematic view of an exemplary arrangement of operations for operating an imaging sensor. [0044] FIG. 13 is a schematic view of an exemplary three-dimensional (3D) speckle camera sensing an object in a scene. [0045] FIG. 14 is a schematic view of an exemplary arrangement of operations for 15 operating a 3D speckle camera. [0046] FIG. 15 is a schematic view of an exemplary 3D time-of-flight (TOF) camera sensing an object in a scene. [0047] FIG. 16 is a schematic view of an exemplary arrangement of operations for operating a 3D TOF camera. 20 [0048] FIG. 17A is a schematic view of an exemplary occupancy map. [0049] FIG. 17B is a schematic view of a mobile robot having a field of view of a scene in a working area. [0050] FIG. 18 is a schematic view of a dead zone of an imaging sensor. [0051] FIG. 19 is a perspective view of an exemplary mobile robot having a first 25 imaging sensor arranged to point downward along a forward drive direction and a second imaging sensor arranged to point outward above the ground. [0052] FIG. 20 is a top view of an exemplary mobile robot having a torso rotating with respect to its base. [0053] FIG. 21 is a schematic view of an exemplary imaging sensor having a dead 30 zone and a dead zone sensor having a field of view enveloping the dead zone. 9 [0054] FIG. 22 is a top view of an exemplary mobile robot having a dead zone sensor arranged to detect objects in a dead zone of an imaging sensor. [0055] FIG. 23 is a top view of an exemplary mobile robot having an array of dead zone sensors. 5 [0056] FIG. 24 is a top view of an exemplary mobile robot having long range sensors arranged about a vertical axis of the robot. [0057] FIG. 25 is a schematic view of an exemplary control system executed by a controller of a mobile human interface robot. [0058] FIG. 26A provides an exemplary schematic view of the local perceptual space 10 of a mobile human interface robot while stationary. [0059] FIG. 26B provides an exemplary schematic view of the local perceptual space of a mobile human interface robot while moving. [0060] FIG. 26C provides an exemplary schematic view of the local perceptual space of a mobile human interface robot while stationary. 15 [0061] FIG. 26D provides an exemplary schematic view of the local perceptual space of a mobile human interface robot while moving. [0062] Like reference symbols in the various drawings indicate like elements. DETAILED DESCRIPTION [0063] Mobile robots can interact or interface with humans to provide a number of 20 services that range from home assistance to commercial assistance and more. In the example of home assistance, a mobile robot can assist elderly people with everyday tasks, including, but not limited to, maintaining a medication regime, mobility assistance, communication assistance (e.g., video conferencing, telecommunications, Internet access, etc.), home or site monitoring (inside and/or outside), person monitoring, and/or 25 providing a personal emergency response system (PERS). For commercial assistance, the mobile robot can provide videoconferencing (e.g., in a hospital setting), a point of sale terminal, interactive information/marketing terminal, etc. [0064] Referring to FIGS. 1-2, in some implementations, a mobile robot 100 includes a robot body 110 (or chassis) that defines a forward drive direction F. The robot 100 also 10 includes a drive system 200, an interfacing module 300, and a sensor system 400, each supported by the robot body 110 and in communication with a controller 500 that coordinates operation and movement of the robot 100. A power source 105 (e.g., battery or batteries) can be carried by the robot body 110 and in electrical communication with, 5 and deliver power to, each of these components, as necessary. For example, the controller 500 may include a computer capable of > 1000 MIPS (million instructions per second) and the power source 1058 provides a battery sufficient to power the computer for more than three hours. [0065] The robot body 110, in the examples shown, includes a base 120, at least one 10 leg 130 extending upwardly from the base 120, and a torso 140 supported by the at least one leg 130. The base 120 may support at least portions of the drive system 200. The robot body 110 also includes a neck 150 supported by the torso 140. The neck 150 supports a head 160, which supports at least a portion of the interfacing module 300. The base 120 includes enough weight (e.g., by supporting the power source 105 (batteries) to 15 maintain a low center of gravity CGB of the base 120 and a low overall center of gravity CGR of the robot 100 for maintaining mechanical stability. [0066] Referring to FIGS. 3 and 4A-4C, in some implementations, the base 120 defines a trilaterally symmetric shape (e.g., a triangular shape from the top view). For example, the base 120 may include a base chassis 122 that supports a base body 124 20 having first, second, and third base body portions 124a, 124b, 124c corresponding to each leg of the trilaterally shaped base 120 (see e.g., FIG. 4A). Each base body portion 124a, 124b, 124c can be movably supported by the base chassis 122 so as to move independently with respect to the base chassis 122 in response to contact with an object. The trilaterally symmetric shape of the base 120 allows bump detection 3600 around the 25 robot 100. Each base body portion 124a, 124b, 124c can have an associated contact sensor e.g., capacitive sensor, read switch, etc.) that detects movement of the corresponding base body portion 124a, 124b, 124c with respect to the base chassis 122. [0067] In some implementations, the drive system 200 provides omni-directional and/or holonomic motion control of the robot 100. As used herein the term "omni 30 directional" refers to the ability to move in substantially any planar direction, i.e., side-to 11 side (lateral), forward/back, and rotational. These directions are generally referred to herein as x, y, and Oz, respectively. Furthermore, the term "holonomic" is used in a manner substantially consistent with the literature use of the term and refers to the ability to move in a planar direction with three planar degrees of freedom, i.e., two translations 5 and one rotation. Hence, a holonomic robot has the ability to move in a planar direction at a velocity made up of substantially any proportion of the three planar velocities (forward/back, lateral, and rotational), as well as the ability to change these proportions in a substantially continuous manner. [0068] The robot 100 can operate in human environments (e.g., environments 10 typically designed for bipedal, walking occupants) using wheeled mobility. In some implementations, the drive system 200 includes first, second, and third drive wheels 210a, 210b, 210c equally spaced (i.e., trilaterally symmetric) about the vertical axis Z (e.g., 120 degrees apart); however, other arrangements are possible as well, such a four wheel holonomic drive system. Referring to FIGS. 5A and 5B, the drive wheels 210a, 15 210b, 210c may define a transverse arcuate rolling surface (i.e., a curved profile in a direction transverse or perpendicular to the rolling direction DR), which may aid maneuverability of the holonomic drive system 200. Each drive wheel 210a, 210b, 210c is coupled to a respective drive motor 220a, 220b, 220c that can drive the drive wheel 210a, 210b, 210c in forward and/or reverse directions independently of the other drive 20 motors 220a, 220b, 220c. Each drive motor 220a-c can have a respective encoder 212 (FIG. 8C), which provides wheel rotation feedback to the controller 500. In some examples, each drive wheels 210a, 210b, 210c is mounted on or near one of the three points of an equilateral triangle and having a drive direction (forward and reverse directions) that is perpendicular to an angle bisector of the respective triangle end. 25 Driving the trilaterally symmetric holonomic base 120 with a forward driving direction F, allows the robot 100 to transition into non forward drive directions for autonomous escape from confinement or clutter and then rotating and/or translating to drive along the forward drive direction F after the escape has been resolved. [0069] In the examples shown in FIGS. 3-5B, the first drive wheel 210a is arranged 30 as a leading drive wheel along the forward drive direction F with the remaining two drive 12 wheels 210b, 210c trailing behind. In this arrangement, to drive forward, the controller 500 may issue a drive command that causes the second and third drive wheels 210b, 210c to drive in a forward rolling direction at an equal rate while the first drive wheel 210a slips along the forward drive direction F. Moreover, this drive wheel arrangement allows 5 the robot 100 to stop short (e.g., incur a rapid negative acceleration against the forward drive direction F). This is due to the natural dynamic instability of the three wheeled design. If the forward drive direction F were along an angle bisector between two forward drive wheels, stopping short would create a torque that would force the robot 100 to fall, pivoting over its two "front" wheels. Instead, travelling with one drive wheel 10 210a forward naturally supports or prevents the robot 100 from toppling over forward, if there is need to come to a quick stop. When accelerating from a stop, however, the controller 500 may take into account a moment of inertia I of the robot 100 from its overall center of gravity CGR [0070] In some implementations of the drive system 200, each drive wheel 210a, 15 210b, 210 has a rolling direction DR radially aligned with a vertical axis Z, which is orthogonal to X and Y axes of the robot 100. The first drive wheel 210a can be arranged as a leading drive wheel along the forward drive direction F with the remaining two drive wheels 210b, 210c trailing behind. In this arrangement, to drive forward, the controller 500 may issue a drive command that causes the first drive wheel 210a to drive in a 20 forward rolling direction and the second and third drive wheels 210b, 210c to drive at an equal rate as the first drive wheel 210a, but in a reverse direction. [0071] In other implementations, the drive system 200 can be arranged to have the first and second drive wheels 210a, 210b positioned such that an angle bisector of an angle between the two drive wheels 210a, 210b is aligned with the forward drive 25 direction F of the robot 100. In this arrangement, to drive forward, the controller 500 may issue a drive command that causes the first and second drive wheels 210a, 210b to drive in a forward rolling direction and an equal rate, while the third drive wheel 210c drives in a reverse direction or remains idle and is dragged behind the first and second drive wheels 210a, 210b. To turn left or right while driving forward, the controller 500 30 may issue a command that causes the corresponding first or second drive wheel 210a, 13 210b to drive at relatively quicker/slower rate. Other drive system 200 arrangements can be used as well. The drive wheels 210a, 210b, 210c may define a cylindrical, circular, elliptical, or polygonal profile. [0072] Referring again to FIGS. 1-3, the base 120 supports at least one leg 130 5 extending upward in the Z direction from the base 120. The leg(s) 130 may be configured to have a variable height for raising and lowering the torso 140 with respect to the base 120. In some implementations, each leg 130 includes first and second leg portions 132, 134 that move with respect to each other (e.g., telescopic, linear, and/or angular movement). Rather than having extrusions of successively smaller diameter 10 telescopically moving in and out of each other and out of a relatively larger base extrusion, the second leg portion 134, in the examples shown, moves telescopically over the first leg portion 132, thus allowing other components to be placed along the second leg portion 134 and potentially move with the second leg portion 134 to a relatively close proximity of the base 120. The leg 130 may include an actuator assembly 136 (FIG. 8C) 15 for moving the second leg portion 134 with respect to the first leg portion 132. The actuator assembly 136 may include a motor driver 138a in communication with a lift motor 138b and an encoder 138c, which provides position feedback to the controller 500. [0073] Generally, telescopic arrangements include successively smaller diameter extrusions telescopically moving up and out of relatively larger extrusions at the base 120 20 in order to keep a center of gravity CGL of the entire leg 130 as low as possible. Moreover, stronger and/or larger components can be placed at the bottom to deal with the greater torques that will be experienced at the base 120 when the leg 130 is fully extended. This approach, however, offers two problems. First, when the relatively smaller components are placed at the top of the leg 130, any rain, dust, or other 25 particulate will tend to run or fall down the extrusions, infiltrating a space between the extrusions, thus obstructing nesting of the extrusions. This creates a very difficult sealing problem while still trying to maintain full mobility/articulation of the leg 130. Second, it may be desirable to mount payloads or accessories on the robot 100. One common place to mount accessories is at the top of the torso 140. If the second leg portion 134 moves 30 telescopically in and out of the first leg portion, accessories and components could only 14 be mounted above the entire second leg portion 134, if they need to move with the torso 140. Otherwise, any components mounted on the second leg portion 134 would limit the telescopic movement of the leg 130. [0074] By having the second leg portion 134 move telescopically over the first leg 5 portion 132, the second leg portion 134 provides additional payload attachment points that can move vertically with respect to the base 120. This type of arrangement causes water or airborne particulate to run down the torso 140 on the outside of every leg portion 132, 134 (e.g., extrusion) without entering a space between the leg portions 132, 134. This greatly simplifies sealing any joints of the leg 130. Moreover, payload/accessory 10 mounting features of the torso 140 and/or second leg portion 134 are always exposed and available no matter how the leg 130 is extended. [0075] Referring to FIGS. 3 and 6, the leg(s) 130 support the torso 140, which may have a shoulder 142 extending over and above the base 120. In the example shown, the torso 140 has a downward facing or bottom surface 144 (e.g., toward the base) forming at 15 least part of the shoulder 142 and an opposite upward facing or top surface 146, with a side surface 148 extending therebetween. The torso 140 may define various shapes or geometries, such as a circular or an elliptical shape having a central portion 141 supported by the leg(s) 130 and a peripheral free portion 143 that extends laterally beyond a lateral extent of the leg(s) 130, thus providing an overhanging portion that 20 defines the downward facing surface 144. In some examples, the torso 140 defines a polygonal or other complex shape that defines a shoulder, which provides an overhanging portion that extends beyond the leg(s) 130 over the base 120. [0076] The robot 100 may include one or more accessory ports 170 (e.g., mechanical and/or electrical interconnect points) for receiving payloads. The accessory ports 170 can 25 be located so that received payloads do not occlude or obstruct sensors of the sensor system 400 (e.g., on the bottom surface 144 and/or top surface 146 of the torso 140, etc.). In some implementations, as shown in FIG. 6, the torso 140 includes one or more accessory ports 170 on a rearward portion 149 of the torso 140 for receiving a payload in the basket 360, for example, and so as not to obstruct sensors on a forward portion 147 of 30 the torso 140 or other portions of the robot body 110. 15 [0077] Referring again to FIGS. 1-3 and 7, the torso 140 supports the neck 150, which provides panning and tilting of the head 160 with respect to the torso 140. In the examples shown, the neck 150 includes a rotator 152 and a tilter 154. The rotator 152 may provide a range of angular movement OR (e.g., about the Z axis) of between about 5 900 and about 3600. Other ranges are possible as well. Moreover, in some examples, the rotator 152 includes electrical connectors or contacts that allow continuous 3600 rotation of the head 160 with respect to the torso 140 in an unlimited number of rotations while maintaining electrical communication between the head 160 and the remainder of the robot 100. The tilter 154 may include the same or similar electrical connectors or 10 contacts allow rotation of the head 160 with respect to the torso 140 while maintaining electrical communication between the head 160 and the remainder of the robot 100. The rotator 152 may include a rotator motor 152m coupled to or engaging a ring 153 (e.g., a toothed ring rack). The tilter 154 may move the head at an angle OT (e.g., about the Y axis) with respect to the torso 140 independently of the rotator 152. In some examples 15 that tilter 154 includes a tilter motor 155, which moves the head 160 between an angle OT of ± 900 with respect to Z-axis. Other ranges are possible as well, such as ± 450, etc. The robot 100 may be configured so that the leg(s) 130, the torso 140, the neck 150, and the head 160 stay within a perimeter of the base 120 for maintaining stable mobility of the robot 100. In the exemplary circuit schematic shown in FIG. 8F, the neck 150 includes a 20 pan-tilt assembly 151 that includes the rotator 152 and a tilter 154 along with corresponding motor drivers 156a, 156b and encoders 158a, 158b. [0078] FIGS. 8A-8G provide exemplary schematics of circuitry for the robot 100. FIGS. 8A-8C provide exemplary schematics of circuitry for the base 120, which may house the proximity sensors, such as the sonar proximity sensors 410 and the cliff 25 proximity sensors 420, contact sensors 430, the laser scanner 440, the sonar scanner 460, and the drive system 200. The base 120 may also house the controller 500, the power source 105, and the leg actuator assembly 136. The torso 140 may house a microcontroller 145, the microphone(s) 330, the speaker(s) 340, an imaging sensor 450 (such as a scanning 3-D image sensor 450a), and a torso touch sensor system 480, which 30 allows the controller 500 to receive and respond to user contact or touches (e.g., as by 16 moving the torso 140 with respect to the base 120, panning and/or tilting the neck 150, and/or issuing commands to the drive system 200 in response thereto). The neck 150 may house a pan-tilt assembly 151 that may include a pan motor 152 having a corresponding motor driver 156a and encoder 158a, and a tilt motor 154 having a 5 corresponding motor driver 156b and encoder 158b. The head 160 may house one or more web pads 310 (e.g., capable of being a remote computing device in communication with the robot 100) and a camera 320. [0079] The web pad 310 may executes a software application (e.g., a tablet-based UI component/application) that allows a remote user to visualize an environment or scene 10 10 about the robot 100 and remotely control the robot 100. The software application may use hardware such as an Apple iPad 2 and/or a Motorola Xoom for a web pad 310 and a PrimeSensor camera (available from PrimeSense, 28 Habarzel St., 4th floor, Tel-Aviv, 69710, Israel) for the imaging sensor 450 or any other suitable hardware. The software application may use Apple iOS 4.x, Android 3.0 (a.k.a. Honeycomb), OpenGL ES 2.0., or 15 any other suitable operating system or program. [0080] Referring to FIGS. 1-4C and 9, to achieve reliable and robust autonomous movement, the sensor system 400 may include several different types of sensors which can be used in conjunction with one another to create a perception of the robot's environment sufficient to allow the robot 100 to make intelligent decisions about actions 20 to take in that environment. The sensor system 400 may include one or more types of sensors supported by the robot body 110, which may include obstacle detection obstacle avoidance (ODOA) sensors, communication sensors, navigation sensors, etc. For example, these sensors may include, but not limited to, proximity sensors, contact sensors, three-dimensional (3D) imaging / depth map sensors, a camera (e.g., visible light 25 and/or infrared camera), sonar, radar, LIDAR (Light Detection And Ranging, which can entail optical remote sensing that measures properties of scattered light to find range and/or other information of a distant target), LADAR (Laser Detection and Ranging), etc. In some implementations, the sensor system 400 includes ranging sonar sensors 410 (e.g., nine about a perimeter of the base 120), proximity cliff detectors 420, contact sensors 17 430, a laser scanner 440, one or more 3-D imaging/depth sensors 450, and an imaging sonar 450. [0081] There are several challenges involved in placing sensors on a robotic platform. First, the sensors need to be placed such that they have maximum coverage of areas of 5 interest around the robot 100. Second, the sensors may need to be placed in such a way that the robot 100 itself causes an absolute minimum of occlusion to the sensors; in essence, the sensors cannot be placed such that they are "blinded" by the robot itself. Third, the placement and mounting of the sensors should not be intrusive to the rest of the industrial design of the platform. In terms of aesthetics, it can be assumed that a robot 10 with sensors mounted inconspicuously is more "attractive" than otherwise. In terms of utility, sensors should be mounted in a manner so as not to interfere with normal robot operation (snagging on obstacles, etc.). [0082] In some implementations, the sensor system 400 includes a set or an array of proximity sensors 410, 420 in communication with the controller 500 and arranged in one 15 or more zones or portions of the robot 100 (e.g., disposed on or near the base body portion 124a, 124b, 124c of the robot body 110) for detecting any nearby or intruding obstacles. The proximity sensors 410, 420 may be converging infrared (IR) emitter sensor elements, sonar sensors, ultrasonic sensors, and/or imaging sensors (e.g., 3D depth map image sensors) that provide a signal to the controller 500 when an object is within a 20 given range of the robot 100. [0083] In the example shown in FIGS. 4A-4C, the robot 100 includes an array of sonar-type proximity sensors 410 disposed (e.g., substantially equidistant) around the base body 120 and arranged with an upward field of view. First, second, and third sonar proximity sensors 410a, 410b, 410c are disposed on or near the first (forward) base body 25 portion 124a, with at least one of the sonar proximity sensors near a radially outer-most edge 125a of the first base body 124a. Fourth, fifth, and sixth sonar proximity sensors 410d, 410e, 410f are disposed on or near the second (right) base body portion 124b, with at least one of the sonar proximity sensors near a radially outer-most edge 125b of the second base body 124b. Seventh, eighth, and ninth sonar proximity sensors 410g, 410h, 30 410i are disposed on or near the third (right) base body portion 124c, with at least one of 18 the sonar proximity sensors near a radially outer-most edge 125c of the third base body 124c. This configuration provides at least three zones of detection. [0084] In some examples, the set of sonar proximity sensors 410 (e.g., 410a-410i) disposed around the base body 120 are arranged to point upward (e.g., substantially in the 5 Z direction) and optionally angled outward away from the Z axis, thus creating a detection curtain 412 around the robot 100. Each sonar proximity sensor 410a-410i may have a shroud or emission guide 414 that guides the sonar emission upward or at least not toward the other portions of the robot body 110 (e.g., so as not to detect movement of the robot body 110 with respect to itself). The emission guide 414 may define a shell or half 10 shell shape. In the example shown, the base body 120 extends laterally beyond the leg 130, and the sonar proximity sensors 410 (e.g., 410a-410i) are disposed on the base body 120 (e.g., substantially along a perimeter of the base body 120) around the leg 130. Moreover, the upward pointing sonar proximity sensors 410 are spaced to create a continuous or substantially continuous sonar detection curtain 412 around the leg 130. 15 The sonar detection curtain 412 can be used to detect obstacles having elevated lateral protruding portions, such as table tops, shelves, etc. [0085] The upward looking sonar proximity sensors 410 provide the ability to see objects that are primarily in the horizontal plane, such as table tops. These objects, due to their aspect ratio, may be missed by other sensors of the sensor system, such as the laser 20 scanner 440 or imaging sensors 450, and as such, can pose a problem to the robot 100. The upward viewing sonar proximity sensors 410 arranged around the perimeter of the base 120 provide a means for seeing or detecting those type of objects/obstacles. Moreover, the sonar proximity sensors 410 can be placed around the widest points of the base perimeter and angled slightly outwards, so as not to be occluded or obstructed by the 25 torso 140 or head 160 of the robot 100, thus not resulting in false positives for sensing portions of the robot 100 itself. In some implementations, the sonar proximity sensors 410 are arranged (upward and outward) to leave a volume about the torso 140 outside of a field of view of the sonar proximity sensors 410 and thus free to receive mounted payloads or accessories, such as the basket 460. The sonar proximity sensors 410 can be 19 recessed into the base body 124 to provide visual concealment and no external features to snag on or hit obstacles. [0086] The sensor system 400 may include or more sonar proximity sensors 410 (e.g., a rear proximity sensor 410j) directed rearward (e.g., opposite to the forward drive 5 direction F) for detecting obstacles while backing up. The rear sonar proximity sensor 410j may include an emission guide 414 to direct its sonar detection field 412. Moreover, the rear sonar proximity sensor 410j can be used for ranging to determine a distance between the robot 100 and a detected object in the field of view of the rear sonar proximity sensor 410j (e.g., as "back-up alert"). In some examples, the rear sonar 10 proximity sensor 410j is mounted recessed within the base body 120 so as to not provide any visual or functional irregularity in the housing form. [0087] Referring to FIGS. 3 and 4B, in some implementations, the robot 100 includes cliff proximity sensors 420 arranged near or about the drive wheels 210a, 210b, 210c, so as to allow cliff detection before the drive wheels 210a, 210b, 210c encounter a cliff (e.g., 15 stairs). For example, a cliff proximity sensors 420 can be located at or near each of the radially outer-most edges 125a-c of the base bodies 124a-c and in locations therebetween. In some cases, cliff sensing is implemented using infrared (IR) proximity or actual range sensing, using an infrared emitter 422 and an infrared detector 424 angled toward each other so as to have an overlapping emission and detection fields, and hence a detection 20 zone, at a location where a floor should be expected. IR proximity sensing can have a relatively narrow field of view, may depend on surface albedo for reliability, and can have varying range accuracy from surface to surface. As a result, multiple discrete sensors can be placed about the perimeter of the robot 100 to adequately detect cliffs from multiple points on the robot 100. Moreover, IR proximity based sensors typically 25 cannot discriminate between a cliff and a safe event, such as just after the robot 100 climbs a threshold. [0088] The cliff proximity sensors 420 can detect when the robot 100 has encountered a falling edge of the floor, such as when it encounters a set of stairs. The controller 500 (executing a control system) may execute behaviors that cause the robot 30 100 to take an action, such as changing its direction of travel, when an edge is detected. 20 In some implementations, the sensor system 400 includes one or more secondary cliff sensors (e.g., other sensors configured for cliff sensing and optionally other types of sensing). The cliff detecting proximity sensors 420 can be arranged to provide early detection of cliffs, provide data for discriminating between actual cliffs and safe events 5 (such as climbing over thresholds), and be positioned down and out so that their field of view includes at least part of the robot body 110 and an area away from the robot body 110. In some implementations, the controller 500 executes cliff detection routine that identifies and detects an edge of the supporting work surface (e.g., floor), an increase in distance past the edge of the work surface, and/or an increase in distance between the 10 robot body 110 and the work surface. This implementation allows: 1) early detection of potential cliffs (which may allow faster mobility speeds in unknown environments); 2) increased reliability of autonomous mobility since the controller 500 receives cliff imaging information from the cliff detecting proximity sensors 420 to know if a cliff event is truly unsafe or if it can be safely traversed (e.g., such as climbing up and over a 15 threshold); 3) a reduction in false positives of cliffs (e.g., due to the use of edge detection versus the multiple discrete IR proximity sensors with a narrow field of view). Additional sensors arranged as "wheel drop" sensors can be used for redundancy and for detecting situations where a range-sensing camera cannot reliably detect a certain type of cliff. 20 [0089] Threshold and step detection allows the robot 100 to effectively plan for either traversing a climb-able threshold or avoiding a step that is too tall. This can be the same for random objects on the work surface that the robot 100 may or may not be able to safely traverse. For those obstacles or thresholds that the robot 100 determines it can climb, knowing their heights allows the robot 100 to slow down appropriately, if deemed 25 needed, to allow for a smooth transition in order to maximize smoothness and minimize any instability due to sudden accelerations. In some implementations, threshold and step detection is based on object height above the work surface along with geometry recognition (e.g., discerning between a threshold or an electrical cable versus a blob, such as a sock). Thresholds may be recognized by edge detection. The controller 500 may 30 receive imaging data from the cliff detecting proximity sensors 420 (or another imaging 21 sensor on the robot 100), execute an edge detection routine, and issue a drive command based on results of the edge detection routine. The controller 500 may use pattern recognition to identify objects as well. Threshold detection allows the robot 100 to change its orientation with respect to the threshold to maximize smooth step climbing 5 ability. [0090] The proximity sensors 410, 420 may function alone, or as an alternative, may function in combination with one or more contact sensors 430 (e.g., bump switches) for redundancy. For example, one or more contact or bump sensors 430 on the robot body 110 can detect if the robot 100 physically encounters an obstacle. Such sensors may use 10 a physical property such as capacitance or physical displacement within the robot 100 to determine when it has encountered an obstacle. In some implementations, each base body portion 124a, 124b, 124c of the base 120 has an associated contact sensor 430 (e.g., capacitive sensor, read switch, etc.) that detects movement of the corresponding base body portion 124a, 124b, 124c with respect to the base chassis 122 (see e.g., FIG. 4A). 15 For example, each base body 124a-c may move radially with respect to the Z axis of the base chassis 122, so as to provide 3-way bump detection. [0091] Referring to FIGS. 1-4C, 9 and 10A, in some implementations, the sensor system 400 includes a laser scanner 440 mounted on a forward portion of the robot body 110 and in communication with the controller 500. In the examples shown, the laser 20 scanner 440 is mounted on the base body 120 facing forward (e.g., having a field of view along the forward drive direction F) on or above the first base body 124a (e.g., to have maximum imaging coverage along the drive direction F of the robot). Moreover, the placement of the laser scanner on or near the front tip of the triangular base 120 means that the external angle of the robotic base (e.g., 300 degrees) is greater than a field of 25 view 442 of the laser scanner 440 (e.g., -285 degrees), thus preventing the base 120 from occluding or obstructing the detection field of view 442 of the laser scanner 440. The laser scanner 440 can be mounted recessed within the base body 124 as much as possible without occluding its fields of view, to minimize any portion of the laser scanner sticking out past the base body 124 (e.g., for aesthetics and to minimize snagging on obstacles). 22 [0092] The laser scanner 440 scans an area about the robot 100 and the controller 500, using signals received from the laser scanner 440, creates an environment map or object map of the scanned area. The controller 500 may use the object map for navigation, obstacle detection, and obstacle avoidance. Moreover, the controller 500 may 5 use sensory inputs from other sensors of the sensor system 400 for creating object map and/or for navigation. [0093] In some examples, the laser scanner 440 is a scanning LIDAR, which may use a laser that quickly scans an area in one dimension, as a "main" scan line, and a time-of flight imaging element that uses a phase difference or similar technique to assign a depth 10 to each pixel generated in the line (returning a two dimensional depth line in the plane of scanning). In order to generate a three dimensional map, the LIDAR can perform an 'auxiliary" scan in a second direction (for example, by "nodding" the scanner). This mechanical scanning technique can be complemented, if not supplemented, by technologies such as the "Flash" LIDAR/LADAR and "Swiss Ranger" type focal plane 15 imaging element sensors, techniques which use semiconductor stacks to permit time of flight calculations for a full 2-D matrix of pixels to provide a depth at each pixel, or even a series of depths at each pixel (with an encoded illuminator or illuminating laser). [0094] The sensor system 400 may include one or more three-dimensional (3-D) image sensors 450 in communication with the controller 500. If the 3-D image sensor 20 450 has a limited field of view, the controller 500 or the sensor system 400 can actuate the 3-D image sensor 450a in a side-to-side scanning manner to create a relatively wider field of view to perform robust ODOA. [0095] Referring again to FIG. 2 and 4A-4C, the sensor system 400 may include an inertial measurement unit (IMU) 470 in communication with the controller 500 to 25 measure and monitor a moment of inertia of the robot 100 with respect to the overall center of gravity CGR of the robot 100. [0096] The controller 500 may monitor any deviation in feedback from the IMU 470 from a threshold signal corresponding to normal unencumbered operation. For example, if the robot begins to pitch away from an upright position, it may be "clothes lined" or 30 otherwise impeded, or someone may have suddenly added a heavy payload. In these 23 instances, it may be necessary to take urgent action (including, but not limited to, evasive maneuvers, recalibration, and/or issuing an audio/visual warning) in order to assure safe operation of the robot 100. [0097] Since robot 100 may operate in a human environment, it may interact with 5 humans and operate in spaces designed for humans (and without regard for robot constraints). The robot 100 can limit its drive speeds and accelerations when in a congested, constrained, or highly dynamic environment, such as at a cocktail party or busy hospital. However, the robot 100 may encounter situations where it is safe to drive relatively fast, as in a long empty corridor, but yet be able to decelerate suddenly, as 10 when something crosses the robots' motion path. [0098] When accelerating from a stop, the controller 500 may take into account a moment of inertia of the robot 100 from its overall center of gravity CGR to prevent robot tipping. The controller 500 may use a model of its pose, including its current moment of inertia. When payloads are supported, the controller 500 may measure a load impact on 15 the overall center of gravity CGR and monitor movement of the robot moment of inertia. For example, the torso 140 and/or neck 150 may include strain gauges to measure strain. If this is not possible, the controller 500 may apply a test torque command to the drive wheels 210 and measure actual linear and angular acceleration of the robot using the IMU 470, in order to experimentally determine safe limits. 20 [0099] During a sudden deceleration, a commanded load on the second and third drive wheels 210b, 210c (the rear wheels) is reduced, while the first drive wheel 210a (the front wheel) slips in the forward drive direction and supports the robot 100. If the loading of the second and third drive wheels 210b, 210c (the rear wheels) is asymmetrical, the robot 100 may "yaw" which will reduce dynamic stability. The IMU 25 470 (e.g., a gyro) can be used to detect this yaw and command the second and third drive wheels 210b, 210c to reorient the robot 100. [00100] Referring to FIGS. 1-3, 9 and 10A, in some implementations, the robot 100 includes a scanning 3-D image sensor 450a mounted on a forward portion of the robot body 110 with a field of view along the forward drive direction F (e.g., to have maximum 30 imaging coverage along the drive direction F of the robot). The scanning 3-D image 24 sensor 450a can be used primarily for obstacle detection/obstacle avoidance (ODOA). In the example shown, the scanning 3-D image sensor 450a is mounted on the torso 140 underneath the shoulder 142 or on the bottom surface 144 and recessed within the torso 140 (e.g., flush or past the bottom surface 144), as shown in FIG. 3, for example, to 5 prevent user contact with the scanning 3-D image sensor 450a. The scanning 3-D image sensor 450 can be arranged to aim substantially downward and away from the robot body 110, so as to have a downward field of view 452 in front of the robot 100 for obstacle detection and obstacle avoidance (ODOA) (e.g., with obstruction by the base 120 or other portions of the robot body 110). Placement of the scanning 3-D image sensor 450a on or 10 near a forward edge of the torso 140 allows the field of view of the 3-D image sensor 450 (e.g., -285 degrees) to be less than an external surface angle of the torso 140 (e.g., 300 degrees) with respect to the 3-D image sensor 450, thus preventing the torso 140 from occluding or obstructing the detection field of view 452 of the scanning 3-D image sensor 450a. Moreover, the scanning 3-D image sensor 450a (and associated actuator) can be 15 mounted recessed within the torso 140 as much as possible without occluding its fields of view (e.g., also for aesthetics and to minimize snagging on obstacles). The distracting scanning motion of the scanning 3-D image sensor 450a is not visible to a user, creating a less distracting interaction experience. Unlike a protruding sensor or feature, the recessed scanning 3-D image sensor 450a will not tend to have unintended interactions with the 20 environment (snagging on people, obstacles, etc.), especially when moving or scanning, as virtually no moving part extends beyond the envelope of the torso 140. [00101] In some implementations, the sensor system 400 includes additional 3-D image sensors 450 disposed on the base body 120, the leg 130, the neck 150, and/or the head 160. In the example shown in FIG. 1, the robot 100 includes 3-D image sensors 450 25 on the base body 120, the torso 140, and the head 160. In the example shown in FIG. 2, the robot 100 includes 3-D image sensors 450 on the base body 120, the torso 140, and the head 160. In the example shown in FIG. 9, the robot 100 includes 3-D image sensors 450 on the leg 130, the torso 140, and the neck 150. Other configurations are possible as well. One 3-D image sensor 450 (e.g., on the neck 150 and over the head 160) can be 30 used for people recognition, gesture recognition, and/or videoconferencing, while another 25 3-D image sensor 450 (e.g., on the base 120 and/or the leg 130) can be used for navigation and/or obstacle detection and obstacle avoidance. [00102] A forward facing 3-D image sensor 450 disposed on the neck 150 and/or the head 160 can be used for person, face, and/or gesture recognition of people about the 5 robot 100. For example, using signal inputs from the 3-D image sensor 450 on the head 160, the controller 500 may recognize a user by creating a three-dimensional map of the viewed/captured user's face and comparing the created three-dimensional map with known 3-D images of people's faces and determining a match with one of the known 3-D facial images. Facial recognition may be used for validating users as allowable users of 10 the robot 100. Moreover, one or more of the 3-D image sensors 450 can be used for determining gestures of person viewed by the robot 100, and optionally reacting based on the determined gesture(s) (e.g., hand pointing, waving, and or hand signals). For example, the controller 500 may issue a drive command in response to a recognized hand point in a particular direction. 15 [00103] FIG. 10B provides a schematic view of a robot 900 having a camera 910, sonar sensors 920, and a laser range finder 930 all mounted on a robot body 905 and each having a field of view parallel or substantially parallel to the ground G. This arrangement allows detection of objects at a distance. In the example, a laser range finder 930 detects objects close to the ground G, a ring of ultrasonic sensors (sonars) 920 detect objects 20 further above the ground G, and the camera 910 captures a large portion of the scene from a high vantage point. The key feature of this design is that the sensors 910, 920, 930 are all oriented parallel to the ground G. One advantage of this arrangement is that computation can be simplified, in the sense that a distance to an object determined by the using one or more of the sensors 910, 920, 930 is also the distance the robot 900 can 25 travel before it contacts an object in a corresponding given direction. A drawback of this arrangement is that to get good coverage of the robot's surroundings, many levels of sensing are needed. This can be prohibitive from a cost or computation perspective, which often leads to large gaps in a sensory field of view of all the sensors 910, 920, 930 of the robot 900. 26 [00104] In some implementations, the robot includes a sonar scanner 460 for acoustic imaging of an area surrounding the robot 100. In the examples shown in FIGS. 1 and 3, the sonar scanner 460 is disposed on a forward portion of the base body 120. [00105] Referring to FIGS. 1, 3B and 10A, in some implementations, the robot 100 5 uses the laser scanner or laser range finder 440 for redundant sensing, as well as a rear facing sonar proximity sensor 410j for safety, both of which are oriented parallel to the ground G. The robot 100 may include first and second 3-D image sensors 450a, 450b (depth cameras) to provide robust sensing of the environment around the robot 100. The first 3-D image sensor 450a is mounted on the torso 140 and pointed downward at a fixed 10 angle to the ground G. By angling the first 3-D image sensor 450a downward, the robot 100 receives dense sensor coverage in an area immediately forward or adjacent to the robot 100, which is relevant for short-term travel of the robot 100 in the forward direction. The rear-facing sonar 410j provides object detection when the robot travels backward. If backward travel is typical for the robot 100, the robot 100 may include a 15 third 3D image sensor 450 facing downward and backward to provide dense sensor coverage in an area immediately rearward or adjacent to the robot 100. [00106] The second 3-D image sensor 450b is mounted on the head 160, which can pan and tilt via the neck 150. The second 3-D image sensor 450b can be useful for remote driving since it allows a human operator to see where the robot 100 is going. The 20 neck 150 enables the operator tilt and/or pan the second 3-D image sensor 450b to see both close and distant objects. Panning the second 3-D image sensor 450b increases an associated horizontal field of view. During fast travel, the robot 100 may tilt the second 3-D image sensor 450b downward slightly to increase a total or combined field of view of both 3-D image sensors 450a, 450b, and to give sufficient time for the robot 100 to 25 avoid an obstacle (since higher speeds generally mean less time to react to obstacles). At slower speeds, the robot 100 may tilt the second 3-D image sensor 450b upward or substantially parallel to the ground G to track a person that the robot 100 is meant to follow. Moreover, while driving at relatively low speeds, the robot 100 can pan the second 3-D image sensor 450b to increase its field of view around the robot 100. The 27 first 3-D image sensor 450a can stay fixed (e.g., not moved with respect to the base 120) when the robot is driving to expand the robot's perceptual range. [00107] The 3-D image sensors 450 may be capable of producing the following types of data: (i) a depth map, (ii) a reflectivity based intensity image, and/or (iii) a regular 5 intensity image. The 3-D image sensors 450 may obtain such data by image pattern matching, measuring the flight time and/or phase delay shift for light emitted from a source and reflected off of a target. [00108] In some implementations, reasoning or control software, executable on a processor (e.g., of the robot controller 500), uses a combination of algorithms executed 10 using various data types generated by the sensor system 400. The reasoning software processes the data collected from the sensor system 400 and outputs data for making navigational decisions on where the robot 100 can move without colliding with an obstacle, for example. By accumulating imaging data over time of the robot's surroundings, the reasoning software can in turn apply effective methods to selected 15 segments of the sensed image(s) to improve depth measurements of the 3-D image sensors 450. This may include using appropriate temporal and spatial averaging techniques. [00109] The reliability of executing robot collision free moves may be based on: (i) a confidence level built by high level reasoning over time and (ii) a depth-perceptive sensor 20 that accumulates three major types of data for analysis - (a) a depth image, (b) an active illumination image and (c) an ambient illumination image. Algorithms cognizant of the different types of data can be executed on each of the images obtained by the depth perceptive imaging sensor 450. The aggregate data may improve the confidence level a compared to a system using only one of the kinds of data. 25 [00110] The 3-D image sensors 450 may obtain images containing depth and brightness data from a scene about the robot 100 (e.g., a sensor view portion of a room or work area ) that contains one or more objects. The controller 500 may be configured to determine occupancy data for the object based on the captured reflected light from the scene. Moreover, the controller 500, in some examples, issues a drive command to the 30 drive system 200 based at least in part on the occupancy data to circumnavigate obstacles 28 (i.e., the object in the scene). The 3-D image sensors 450 may repeatedly capture scene depth images for real-time decision making by the controller 500 to navigate the robot 100 about the scene without colliding into any objects in the scene. For example, the speed or frequency in which the depth image data is obtained by the 3-D image sensors 5 450 may be controlled by a shutter speed of the 3-D image sensors 450. In addition, the controller 500 may receive an event trigger (e.g., from another sensor component of the sensor system 400, such as proximity sensor 410, 420, notifying the controller 500 of a nearby object or hazard. The controller 500, in response to the event trigger, can cause the 3-D image sensors 450 to increase a frequency at which depth images are captured 10 and occupancy information is obtained. [00111] Referring to FIG. 11, in some implementations, the 3-D imaging sensor 450 includes a light source 1172 that emits light onto a scene 10, such as the area around the robot 100 (e.g., a room). The imaging sensor 450 may also include an imager 1174 (e.g., an array of light-sensitive pixels 1174p) which captures reflected light from the scene 10, 15 including reflected light that originated from the light source 1172 (e.g., as a scene depth image). In some examples, the imaging sensor 450 includes a light source lens 1176 and/or a detector lens 1178 for manipulating (e.g., speckling or focusing) the emitted and received reflected light, respectively. The robot controller 500 or a sensor controller (not shown) in communication with the robot controller 500 receives light signals from the 20 imager 1174 (e.g., the pixels 1174p) to determine depth information for an object 12 in the scene 10 based on image pattern matching and/or a time-of-flight characteristic of the reflected light captured by the imager 1174. [00112] FIG. 12 provides an exemplary arrangement 1200 of operations for operating the imaging sensor 450. With additional reference to FIG. 10A, the operations include 25 emitting 1202 light onto a scene 10 about the robot 100 and receiving 1204 reflections of the emitted light from the scene 10 on an imager (e.g., array of light-sensitive pixels). The operations further include the controller 500 receiving 1206 light detection signals from the imager, detecting 1208 one or more features of an object 12 in the scene 10 using image data derived from the light detection signals, and tracking 1210 a position of 30 the detected feature(s) of the object 12 in the scene 10 using image depth data derived 29 from the light detection signals. The operations may include repeating 1212 the operations of emitting 1202 light, receiving 1204 light reflections, receiving 1206 light detection signals, detecting 1208 object feature(s), and tracking 12010 a position of the object feature(s) to increase a resolution of the image data or image depth data, and/or to 5 provide a confidence level. [00113] The repeating 1212 operation can be performed at a relatively slow rate (e.g., slow frame rate) for relatively high resolution, an intermediate rate, or a high rate with a relatively low resolution. The frequency of the repeating 1212 operation may be adjustable by the robot controller 500. In some implementations, the controller 500 may 10 raise or lower the frequency of the repeating 1212 operation upon receiving an event trigger. For example, a sensed item in the scene may trigger an event that causes an increased frequency of the repeating 1212 operation to sense an possibly eminent object 12 (e.g., doorway, threshold, or cliff) in the scene 10. In additional examples, a lapsed time event between detected objects 12 may cause the frequency of the repeating 1212 15 operation to slow down or stop for a period of time (e.g., go to sleep until awakened by another event). In some examples, the operation of detecting 1208 one or more features of an object 12 in the scene 10 triggers a feature detection event causing a relatively greater frequency of the repeating operation 1212 for increasing the rate at which image depth data is obtained. A relatively greater acquisition rate of image depth data can allow 20 for relatively more reliable feature tracking within the scene. [00114] The operations also include outputting 1214 navigation data for circumnavigating the object 12 in the scene 10. In some implementations, the controller 500 uses the outputted navigation data to issue drive commands to the drive system 200 to move the robot 100 in a manner that avoids a collision with the object 12. 25 [00115] In some implementations, the sensor system 400 detects multiple objects 12 within the scene 10 about the robot 100 and the controller 500 tracks the positions of each of the detected objects 12. The controller 500 may create an occupancy map of objects 12 in an area about the robot 100, such as the bounded area of a room. The controller 500 may use the image depth data of the sensor system 400 to match a scene 10 with a 30 portion of the occupancy map and update the occupancy map with the location of tracked objects 12. [00116] Referring to FIG. 13, in some implementations, the 3-D image sensor 450 includes a three-dimensional (3D) speckle camera 1300, which allows image mapping 5 through speckle decorrelation. The speckle camera 1300 includes a speckle emitter 1310 (e.g., of infrared, ultraviolet, and/or visible light) that emits a speckle pattern into the scene 10 (as a target region) and an imager 1320 that captures images of the speckle pattern on surfaces of an object 12 in the scene 10. [00117] The speckle emitter 1310 may include a light source 1312, such as a laser, 10 emitting a beam of light into a diffuser 1314 and onto a reflector 1316 for reflection, and hence projection, as a speckle pattern into the scene 10. The imager 1320 may include objective optics 1322, which focus the image onto an image sensor 1324 having an array of light detectors 1326, such as a CCD or CMOS-based image sensor. Although the optical axes of the speckle emitter 1310 and the imager 1320 are shown as being 15 collinear, in a decorrelation mode for example, the optical axes of the speckle emitter 1310 and the imager 1320 may also be non-collinear, while in a cross-correlation mode for example, such that an imaging axis is displaced from an emission axis. [00118] The speckle emitter 1310 emits a speckle pattern into the scene 10 and the imager 1320 captures reference images of the speckle pattern in the scene 10 at a range of 20 different object distances Z,, from the speckle emitter 1310 (e.g., where the Z-axis can be defined by the optical axis of imager 1320). In the example shown, reference images of the projected speckle pattern are captured at a succession of planes at different, respective distances from the origin, such as at the fiducial locations marked Z 1 , Z 2 , Z 3 , and so on. The distance between reference images, AZ, can be set at a threshold distance (e.g., 5 25 mm) or adjustable by the controller 500 (e.g., in response to triggered events). The speckle camera 1300 archives and indexes the captured reference images to the respective emission distances to allow decorrelation of the speckle pattern with distance from the speckle emitter 1310 to perform distance ranging of objects 12 captured in subsequent images. Assuming AZ to be roughly equal to the distance between adjacent fiducial 30 distances Z 1 , Z 2 , Z 3 , ... , the speckle pattern on the object 12 at location ZA can be 31 correlated with the reference image of the speckle pattern captured at Z 2 , for example. On the other hand, the speckle pattern on the object 12 at ZB can be correlated with the reference image at Z 3 , for example. These correlation measurements give the approximate distance of the object 12 from the origin. To map the object 12 in three 5 dimensions, the speckle camera 1300 or the controller 500 receiving information from the speckle camera 1300 can use local cross-correlation with the reference image that gave the closest match. [00119] Other details and features on 3D image mapping using speckle ranging, via speckle cross-correlation using triangulation or decorrelation, for example, which may 10 combinable with those described herein, can be found in PCT Patent Application PCT/IL2006/000335; the contents of which is hereby incorporated by reference in its entirety. [00120] FIG. 14 provides an exemplary arrangement 1400 of operations for operating the speckle camera 1300. The operations include emitting 1402 a speckle pattern into the 15 scene 10 and capturing 1404 reference images (e.g., of a reference object 12) at different distances from the speckle emitter 1310. The operations further include emitting 1406 a speckle pattern onto a target object 12 in the scene 10 and capturing 1408 target images of the speckle pattern on the object 12. The operations further include comparing 1410 the target images (of the speckled object) with different reference images to identify a 20 reference pattern that correlates most strongly with the speckle pattern on the target object 12 and determining 1412 an estimated distance range of the target object 12 within the scene 10. This may include determining a primary speckle pattern on the object 12 and finding a reference image having speckle pattern that correlates most strongly with the primary speckle pattern on the object 12. The distance range can be determined from 25 the corresponding distance of the reference image. [00121] The operations optionally include constructing 1414 a 3D map of the surface of the object 12 by local cross-correlation between the speckle pattern on the object 12 and the identified reference pattern, for example, to determine a location of the object 12 in the scene. This may include determining a primary speckle pattern on the object 12 30 and finding respective offsets between the primary speckle pattern on multiple areas of 32 the object 12 in the target image and the primary speckle pattern in the identified reference image so as to derive a three-dimensional (3D) map of the object. The use of solid state components for 3D mapping of a scene provides a relatively inexpensive solution for robot navigational systems. 5 [00122] Typically, at least some of the different, respective distances are separated axially by more than an axial length of the primary speckle pattern at the respective distances. Comparing the target image to the reference images may include computing a respective cross-correlation between the target image and each of at least some of the reference images, and selecting the reference image having the greatest respective cross 10 correlation with the target image. [00123] The operations may include repeating 1416 operations 1402-1412 or operations 1406-1412, and optionally operation 1414, (e.g., continuously) to track motion of the object 12 within the scene 10. For example, the speckle camera 1300 may capture a succession of target images while the object 12 is moving for comparison with the 15 reference images. [00124] Other details and features on 3D image mapping using speckle ranging, which may combinable with those described herein, can be found in U.S. Patent 7,433,024; U.S. Patent Application Publication No. 2008/0106746, entitled "Depth-varying light fields for three dimensional sensing"; U.S. Patent Application Publication No. 2010/0118123, 20 entitled "Depth Mapping Using Projected Patterns"; U.S. Patent Application Publication No. 2010/0034457, Entitled "Modeling Of Humanoid Forms From Depth Maps"; U.S. Patent Application Publication No. 2010/0020078, Entitled "Depth Mapping Using Multi-Beam Illumination"; U.S. Patent Application Publication No. 2009/0185274, Entitled "Optical Designs For Zero Order Reduction"; U.S. Patent Application 25 Publication No. 2009/0096783, Entitled "Three-Dimensional Sensing Using Speckle Patterns"; U.S. Patent Application Publication No. 2008/0240502, Entitled "Depth Mapping Using Projected Patterns"; and U.S. Patent Application Publication No. 2008/0106746, Entitled "Depth-Varying Light Fields For Three Dimensional Sensing"; the contents of which are hereby incorporated by reference in their entireties. 33 [00125] Referring to FIG. 15, in some implementations, the 3-D imaging sensor 450 includes a 3D time-of-flight (TOF) camera 1500 for obtaining depth image data. The 3D TOF camera 1500 includes a light source 1510, a complementary metal oxide semiconductor (CMOS) sensor 1520 (or charge-coupled device (CCD)), a lens 1530, and 5 control logic or a camera controller 1540 having processing resources (and/or the robot controller 500) in communication with the light source 1510 and the CMOS sensor 1520. The light source 1510 may be a laser or light-emitting diode (LED) with an intensity that is modulated by a periodic signal of high frequency. In some examples, the light source 1510 includes a focusing lens 1512. The CMOS sensor 1520 may include an array of 10 pixel detectors 1522, or other arrangement of pixel detectors 1522, where each pixel detector 1522 is capable of detecting the intensity and phase of photonic energy impinging upon it. In some examples, each pixel detector 1522 has dedicated detector circuitry 1524 for processing detection charge output of the associated pixel detector 1522. The lens 1530 focuses light reflected from a scene 10, containing one or more 15 objects 12 of interest, onto the CMOS sensor 1520. The camera controller 1540 provides a sequence of operations that formats pixel data obtained by the CMOS sensor 1520 into a depth map and a brightness image. In some examples, the 3D TOF camera 1500 also includes inputs / outputs (IO) 1550 (e.g., in communication with the robot controller 500), memory 1560, and/or a clock 1570 in communication with the camera controller 20 1540 and/or the pixel detectors 1522 (e.g., the detector circuitry 1524). [00126] FIG. 16 provides an exemplary arrangement 1600 of operations for operating the 3D TOF camera 1500. The operations include emitting 1602 a light pulse (e.g., infrared, ultraviolet, and/or visible light) into the scene 10 and commencing 1604 timing of the flight time of the light pulse (e.g., by counting clock pulses of the clock 1570). 25 The operations include receiving 1606 reflections of the emitted light off one or more surfaces of an object 12 in the scene 10. The reflections may be off surfaces of the object 12 that are at different distances Z,, from the light source 1510. The reflections are received though the lens 1530 and onto pixel detectors 1522 of the CMOS sensor 1520. The operations include receiving 1608 time-of-flight for each light pulse reflection 30 received on each corresponding pixel detector 1522 of the CMOS sensor 1520. During 34 the roundtrip time of flight (TOF) of a light pulse, a counter of the detector circuitry 1523 of each respective pixel detector 1522 accumulates clock pulses. A larger number of accumulated clock pulses represents a longer TOF, and hence a greater distance between a light reflecting point on the imaged object 12 and the light source 1510. The operations 5 further include determining 1610 a distance between the reflecting surface of the object 12 for each received light pulse reflection and optionally constructing 1612 a three dimensional object surface. In some implementations, the operations include repeating 1614 operations 1602-1610 and optionally 1612 for tracking movement of the object 12 in the scene 10. 10 [00127] Other details and features on 3D time-of-flight imaging, which may combinable with those described herein, can be found in U.S. Patent No. 6,323,942, entitled "CMOS Compatible 3-D Image Sensor"; U.S. Patent No. 6,515,740, entitled "Methods for CMOS-Compatible Three-Dimensional Image Sensing Using Quantum Efficiency Modulation"; and PCT Patent Application PCT/US02/16621, entitled "Method 15 and System to Enhance Dynamic Range Conversion Usable with CMOS Three Dimensional Imaging", the contents of which are hereby incorporated by reference in their entireties. [00128] In some implementations, the 3-D imaging sensor 450 provides three types of information: (1) depth information (e.g., from each pixel detector 1522 of the CMOS 20 sensor 1520 to a corresponding location on the scene 12); (2) ambient light intensity at each pixel detector location; and (3) the active illumination intensity at each pixel detector location. The depth information enables the position of the detected object 12 to be tracked over time, particularly in relation to the object's proximity to the site of robot deployment. The active illumination intensity and ambient light intensity are different 25 types of brightness images. The active illumination intensity is captured from reflections of an active light (such as provided by the light source 1510) reflected off of the target object 12. The ambient light image is of ambient light reflected off of the target object 12. The two images together provide additional robustness, particularly when lighting conditions are poor (e.g., too dark or excessive ambient lighting). 35 [00129] Image segmentation and classification algorithms may be used to classify and detect the position of objects 12 in the scene 10. Information provided by these algorithms, as well as the distance measurement information obtained from the imaging sensor 450, can be used by the robot controller 500 or other processing resources. The 5 imaging sensor 450 can operate on the principle of time-of-flight, and more specifically, on detectable phase delays in a modulated light pattern reflected from the scene 10, including techniques for modulating the sensitivity of photodiodes for filtering ambient light. [00130] The robot 100 may use the imaging sensor 450 for 1) mapping, localization & 10 navigation; 2) object detection & object avoidance (ODOA); 3) object hunting (e.g., to find a person); 4) gesture recognition (e.g., for companion robots); 5) people & face detection; 6) people tracking; 7) monitoring manipulation of objects by the robot 100; and other suitable applications for autonomous operation of the robot 100. [00131] In some implementations, at least one of 3-D image sensors 450 can be a 15 volumetric point cloud imaging device (such as a speckle or time-of-flight camera) positioned on the robot 100 at a height of greater than 1 or 2 feet above the ground and directed to be capable of obtaining a point cloud from a volume of space including a floor plane in a direction of movement of the robot (via the omni-directional drive system 200). In the examples shown in FIGS. 1 and 3, the first 3-D image sensor 450a can be 20 positioned on the base 120 at height of greater than 1 or 2 feet above the ground (or at a height of about 1 or 2 feet above the ground) and aimed along the forward drive direction F to capture images (e.g., volumetric point cloud) of a volume including the floor while driving (e.g., for obstacle detection and obstacle avoidance). The second 3-D image sensor 450b is shown mounted on the head 160 (e.g., at a height greater than about 3 or 4 25 feet above the ground), so as to be capable of obtaining skeletal recognition and definition point clouds from a volume of space adjacent the robot 100. The controller 500 may execute skeletal/digital recognition software to analyze data of the captured volumetric point clouds. [00132] Properly sensing objects 12 using the imaging sensor 450, despite ambient 30 light conditions can be important. In many environments the lighting conditions cover a 36 broad range from direct sunlight to bright fluorescent lighting to dim shadows, and can result in large variations in surface texture and basic reflectance of objects 12. Lighting can vary within a given location and from scene 10 to scene 10 as well. In some implementations, the imaging sensor 450 can be used for identifying and resolving 5 people and objects 12 in all situations with relatively little impact from ambient light conditions (e.g., ambient light rejection). [00133] In some implementations, VGA resolution of the imaging sensor 450 is 640 horizontal by 480 vertical pixels; however, other resolutions are possible as well, such. 320 x 240 (e.g., for short range sensors). 10 [00134] The imaging sensor 450 may include a pulse laser and camera iris to act as a bandpass filter in the time domain to look at objects 12 only within a specific range. A varying iris of the imaging sensor 450 can be used to detect objects 12 a different distances. Moreover, a pulsing higher power laser can be used for outdoor applications. [00135] Table 1 and Table 2 (below) provide exemplary features, parameters, and/or 15 specifications of imaging sensors 450 for various applications. Sensor 1 can be used as a general purpose imaging sensor 450. Sensors 2 and 3 could be used on a human interaction robot, and sensors 4 and 5 could be used on a coverage or cleaning robot. 37 Unit Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Long Short Long Short Range Range Range Range Dimensions Width cm 18 <=18< 14 < 14<= 6 <= 6 < 6 Height cm 2.5 <=2.5< 4 < 4<= 1.2 <= 1.2 <= 1.2 Depth cm 3.5 <=3.5< 5 < 5<= .6 <= .6 <= .6 Operating Temp Minimum OC 5 5 5 5 5 Maximum IC 140 1 4 0 140 140 140 Comm Port [Data interface USB 2.0 USB 2.0 USB 2.0 SPI SPI Field-of-View Horizontal deg 57.5 >=57.5 >70 >70 >70 Vertical deg 45 >=45 >=45 >=45 >40 Diagonal deg 69 Spatial Resolution 640 x Depth image size 480 640 x 480 @15cm mm @20cm mm @40cm mm @80cm mm @lm mm 1.7 1.7 @2m mm 3.4 3.4 @3m mm 5.1 5.1 @3.5m mm 6 6 Downsampling QVGA pixels 320x240 320x240 320x240 320x240 320x240 QQVGA pixels 160x120 160x120 160x120 160x120 160x120 Table 1 38 Unit Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 Long Short Long Short Range Range Range Range Depth Resolution @lm cm 0.57 @2m cm 2.31 @3m cm 5.23 @3.5m cm 7.14 Minimum Object Size @lm cm 2.4 <=2.4 0.2 @2m cm 4.8 <=4.8 @3m cm 7.2 <=7.2 @3.5m cm 8.4 <=8.4 Throughput Frame rate fps 30 30 30 30 30 VGA depth image ms 44 <=44 <=44 <=44 <=44 QVGA depth image ms 41 <=41 <=41 <=41 <=41 Range 0.25 - 0.25 - 0.15 In Spec. range m 0.8 - 3.5 0.8 - 3.5 1.50 1.50 1.0 0.15- 0.15- 0.10 Observed range m 0.3 - 5 0.3 - 5 2.00 2.00 1.5 Color Image Color camera CMOS N/R N/R N/R N/R 1280 x 1024 Audio Built-in microphones 2 N/R N/R N/R N/R Data format 16 Sample rate 17746 External digital audio inputs 4 Power Power supply USB 2.0 USB 2.0 USB 2.0 Current consumption 0.45 Max power consumption 2.25 0.5 39 Table 2 [00136] Minimal sensor latency assures that objects 12 can be seen quickly enough to be avoided when the robot 100 is moving. Latency of the imaging sensor 450 can be a factor in reacting in real time to detected and recognized user gestures. In some 5 examples, the imaging sensor 450 has a latency of about 44 ms. Images captured by the imaging sensor 450 can have an attributed time stamp, which can be used for determining at what robot pose an image was taken while translating or rotating in space. [00137] A Serial Peripheral Interface Bus (SPI) in communication with the controller 500 may be used for communicating with the imaging sensor 450. Using an SPI 10 interface for the imaging sensor 450 does not limit its use for multi-node distributed sensor/actuator systems, and allows connection with an Ethernet enabled device such as a microprocessor or a field-programmable gate array (FPGA), which can then make data available over Ethernet and an EtherlO system, as described in U.S. Patent Application Serial No. 61/305,069, filed on February 16, 2010 and titled "Mobile Robot 15 Communication System," which is hereby incorporate by reference in its entirety. [00138] Since SPI is a limited protocol, an interrupt pin may be available on the interface to the imaging sensor 450 that would strobe or transition when an image capture is executed. The interrupt pin allows communication to the controller 500 of when a frame is captured. This allows the controller 500 to know that data is ready to be read. 20 Additionally, the interrupt pin can be used by the controller 500 to capture a timestamp which indicates when the image was taken. Imaging output of the imaging sensor 450 can be time stamped (e.g., by a global clock of the controller 500), which can be referenced to compensate for latency. Moreover, the time stamped imaging output from multiple imaging sensors 450 (e.g., of different portions of the scene 10) can be 25 synchronized and combined (e.g., stitched together). Over an EtherlO system, an interrupt time (on the interrupt pin) can be captured and made available to higher level devices and software on the EtherlO system. The robot 100 may include a multi-node distributed sensor/actuator systems that implements a clock synchronization strategy, such as IEEE1588, which we can be applied to data captured from the imaging sensor 30 450. 40 [00139] Both the SPI interface and EtherlO can be memory-address driven interfaces. Data in the form of bytes/words/double-words, for example, can be read from the imaging sensor 450 over the SPI interface, and made available in a memory space of the EtherlO system. For example, local registers and memory, such as direct memory access 5 (DMA) memory, in an FPGA, can be used to control an EtherlO node of the EtherlO system. [00140] In some cases, the robot 100 may need to scan the imaging sensor 450 from side to side and/or up and down (e.g., to view an object 12 or around an occlusion 16 (FIG. 17A)). For a differentially steered robot 100, this may involve rotating the robot 10 100 in place with the drive system 200; or rotating a mirror, prism, variable angle micro mirror, or MEMS mirror array associated with the imaging sensor 450. [00141] The field of view 452 of the imaging sensor 450 having a view angle ev less than 360 can be enlarged to 360 degrees by optics, such as omni-directional, fisheye, catadioptric (e.g., parabolic mirror, telecentric lens), panamorph mirrors and lenses. 15 Since the controller 500 may use the imaging sensor 450 for distance ranging, inter alia, but not necessarily for human-viewable images or video (e.g., for human communications), distortion (e.g., warping) of the illumination of the light source 1172 and/or the image capturing by the imager 1174 (FIG. 11) through optics is acceptable for distance ranging (e.g., as with the 3D speckle camera 1300 and/or the 3D TOF camera 20 1500). [00142] In some instances, the imaging sensor 450 may have difficulties recognizing and ranging black objects 12, surfaces of varied albedo, highly reflective objects 12, strong 3D structures, self-similar or periodic structures, or objects at or just beyond the field of view 452 (e.g., at or outside horizontal and vertical viewing field angles). In such 25 instances, other sensors of the sensor system 400 can be used to supplement or act as redundancies to the imaging sensor 450. [00143] In some implementations, the light source 1172 (e.g., of the 3D speckle camera 1300 and/or the 3D TOF camera 1500) includes an infrared (IR) laser, IR pattern illuminator, or other IR illuminator. A black object, especially black fabric or carpet, 30 may absorb IR and fail to return a strong enough reflection for recognition by the imager 41 1174. In this case, either a secondary mode of sensing (such as sonar) or a technique for self calibrating for surface albedo differences may be necessary to improve recognition of black objects. [00144] A highly reflective object 12 or an object 12 with significant specular 5 highlights (e.g., cylindrical or spherical) may make distance ranging difficult for the imaging sensor 450. Similarly, objects 12 that are extremely absorptive in the wavelength of light for which the imaging sensor 450 is sensing, can pose problems as well. Objects 12, such as doors and window, which are made of glass can be highly reflective and, when ranged, either appear as if they are free space (infinite range) or else 10 range as the reflection to the first non-specularly-reflective surface. This may cause the robot 100 to not see the object 12 as an obstacle, and, as a result, may collide with the window or door, possibly causing damage to the robot or to the object 12. In order to avoid this, the controller 500 may execute one or more algorithms that look for discontinuities in surfaces matching the size and shape (rectilinear) of a typical window 15 pane or doorway. These surfaces can then be inferred as being obstacles and not free space. Another implementation for detecting reflective objects in the path of the robot includes using a reflection sensor that detects its own reflection. Upon careful approach of the obstacle or object 12, the reflection sensor can be used determine whether there is a specularly reflective object ahead, or if the robot can safely occupy the space. 20 [00145] In the case of the 3D speckle camera 1300, the light source 1310 may fail to form a pattern recognizable on the surface of a highly reflective object 12 or the imager 1320 may fail to recognize a speckle reflection from the highly reflective object 12. In the case of the 3D TOF camera 1500, the highly reflective object 12 may create a multi path situation where the 3D TOF camera 1500 obtains a range to another object 12 25 reflected in the object 12 (rather than to the object itself). To remedy IR failure modes, the sensor system 400 may employ acoustic time of flight, millimeter wave radar, stereo or other vision techniques able to use even small reflections in the scene 10. [00146] Mesh objects 12 may make distance ranging difficult for the imaging sensor 450. If there are no objects 12 immediately behind mesh of a particular porosity, the 30 mesh will appear as a solid obstacle 12. If an object 12 transits behind the mesh, 42 however, and, in the case of the 3D speckle camera 1300, the speckles are able to reflect off the object 12 behind the mesh, the object will appear in the depth map instead of the mesh, even though it is behind it. If information is available about the points that had previously contributed to the identification of the mesh (before an object 12 transited 5 behind it), such information could be used to register the position of the mesh in future occupancy maps. By receiving information about the probabilistic correlation of the received speckle map at various distances, the controller 500 may determine the locations of multiple porous or mesh-like objects 12 in line with the imaging sensor 450. [00147] The controller 500 may use imaging data from the imaging sensor 450 for 10 color/size/dimension blob matching. Identification of discrete objects 12 in the scene 10 allows the robot 100 to not only avoid collisions, but also to search for objects 12. The human interface robot 100 may need to identify humans and target objects 12 against the background of a home or office environment. The controller 500 may execute one or more color map blob-finding algorithms on the depth map(s) derived from the imaging 15 data of the imaging sensor 450 as if the maps were simple grayscale maps and search for the same "color" (that is, continuity in depth) to yield continuous objects 12 in the scene 10. Using color maps to augment the decision of how to segment objects 12 would further amplify object matching, by allowing segmentation in the color space as well as in the depth space. The controller 500 may first detect objects 12 by depth, and then 20 further segment the objects 12 by color. This allows the robot 100 to distinguish between two objects 12 close to or resting against one another with differing optical qualities. [00148] In implementations where the sensor system 400 includes only one imaging sensor 450 (e.g., camera) for object detection, the imaging sensor 450 may have problems imaging surfaces in the absence of scene texture and may not be able to resolve the scale 25 of the scene. Moreover, mirror and/or specular highlights of an object 12 can cause saturation in a group of pixels 1174p of the imager 1174 (e.g., saturating a corresponding portion of a captured image); and in color images, the specular highlights can appear differently from different viewpoints, thereby hampering image matching, as for the speckle camera 1300. 43 [00149] Using or aggregating two or more sensors for object detection can provide a relatively more robust and redundant sensor system 400. For example, although flash LADARs generally have low dynamic range and rotating scanners generally have long inspection times, these types of sensor can be useful for object detection. In some 5 implementations, the sensor system 400 include a flash LADAR and/or a rotating scanner in addition to the imaging sensor 450 (e.g., the 3D speckle camera 1300 and/or the 3D TOF camera 1500) in communication with the controller 500. The controller 500 may use detection signals from the imaging sensor 450 and the flash ladar and/or a rotating scanner to identify objects 12, determine a distance of objects 12 from the robot 100, 10 construct a 3D map of surfaces of objects 12, and/or construct or update an occupancy map 1700. The 3D speckle camera 1300 and/or the 3D TOF camera 1500 can be used to address any color or stereo camera weaknesses by initializing a distance range, filling in areas of low texture, detecting depth discontinuities, and/or anchoring scale. [00150] In examples using the 3D speckle camera 1300, the speckle pattern emitted by 15 the speckle emitter 1310 may be rotation-invariant with respect to the imager 1320. Moreover, an additional camera 1300 (e.g., color or stereo camera) co-registered with the 3D speckle camera 1300 and/or the 3D TOF camera 1500 may employ a feature detector that is some or fully scale-rotation-affine invariant to handle ego rotation, tilt, perspective, and/or scale (distance). Scale-invariant feature transform (or SIFT) is an 20 algorithm for detecting and/or describing local features in images. SIFT can be used by the controller 500 (with data from the sensor system 400) for object recognition, robotic mapping and navigation, 3D modeling, gesture recognition, video tracking, and match moving. SIFT, as a scale-invariant, rotation-invariant transform, allows placement of a signature on features in the scene 10 and can help reacquire identified features in the 25 scene 10 even if they are farther away or rotated. For example, the application of SIFT on ordinary images allows recognition of a moved object 12 (e.g., a face or a button or some text) be identifying that the object 12 has the same luminance or color pattern, just bigger or smaller or rotated. Other of transforms may be employed that are affine invariant and can account for skew or distortion for identifying objects 12 from an angle. 30 The sensor system 400 and/or the controller 500 may provide scale-invariant feature 44 recognition (e.g., with a color or stereo camera) by employing SIFT, RIFT, Affine SIFT, RIFT, G-RIF, SURF, PCA-SIFT, GLOH. PCA-SIFT, SIFT w/FAST corner detector and/or Scalable Vocabulary Tree, and/or SIFT w/ Irregular Orientation Histogram Binning. 5 [00151] In some implementations, the controller 500 executes a program or routine that employs SIFT and/or other transforms for object detection and/or identification. The controller 500 may receive image data from an image sensor 450, such as a color, black and white, or IR camera. In some examples, the image sensor 450 is a 3D speckle IR camera that can provide image data without the speckle illumination to identify features 10 without the benefit of speckle ranging. The controller 500 can identify or tag features or objects 12 previously mapped in the 3D scene from the speckle ranging. The depth map can be used to filter and improve the recognition rate of SIFT applied to features imaged with a camera, and/or simplify scale invariance (because both motion and change in range are known and can be related to scale). SIFT-like transforms may be useful with 15 depth map data normalized and/or shifted for position variation from frame to frame, which robots with inertial tracking, odometry, proprioception, and/or beacon reference may be able to track. For example, a transform applied for scale and rotation invariance may still be effective to recognize a localized feature in the depth map if the depth map is indexed by the amount of movement in the direction of the feature. 20 [00152] Other details and features on SIFT-like or other feature descriptors to 3D data, which may combinable with those described herein, can be found in Se, S.; Lowe, David G.; Little, J. (2001). "Vision-based mobile robot localization and mapping using scale invariant features". Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). 2. pp. 2051; or Rothganger, F; S. Lazebnik, C. Schmid, and J. Ponce: 25 2004. 3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints, ICCV; or Iryna Gordon and David G. Lowe, "What and where: 3D object recognition with accurate pose," Toward Category Level Object Recognition, (Springer-Verlag, 2006), pp. 67-82; the contents of which are hereby incorporated by reference in their entireties. 45 [00153] Other details and features on techniques suitable for 3D SIFT in human action recognition, including falling, can be found in Laptev, Ivan and Lindeberg, Tony (2004). "Local descriptors for spatio-temporal recognition". ECCV04 Workshop on Spatial Coherence for Visual Motion Analysis, Springer Lecture Notes in Computer Science, 5 Volume 3667. pp. 91-103; Ivan Laptev, Barbara Caputo, Christian Schuldt and Tony Lindeberg (2007). "Local velocity-adapted motion events for spatio-temporal recognition". Computer Vision and Image Understanding 108: 207-229; Scovanner, Paul; Ali, S; Shah, M (2007). "A 3-dimensional sift descriptor and its application to action recognition". Proceedings of the 15th International Conference on Multimedia. pp. 10 357-360; Niebles, J. C. Wang, H. and Li, Fei-Fei (2006). "Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words". Proceedings of the British Machine Vision Conference (BMVC). Edinburgh; the contents of which are hereby incorporated by reference in their entireties. [00154] The controller 500 may use the imaging sensor 450 (e.g., a depth map sensor) 15 when constructing a 3D map of the surface of and object 12 to fill in holes from depth discontinuities and to anchor a metric scale of a 3D model. Structure-from-motion, augmented with depth map sensor range data, may be used to estimate sensor poses. A typical structure-from-motion pipeline may include viewpoint-invariant feature estimation, inter-camera feature matching, and a bundle adjustment. 20 [00155] A software solution combining features of color/stereo cameras with the imaging sensor 450 (e.g., the 3D speckle camera 1300, and/or the TOF camera 1500) may include (1) sensor pose estimation, (2) depth map estimation, and (3) 3D mesh estimation. In sensor pose estimation, the position and attitude of the sensor package of each image capture is determined. In depth map estimation, a high-resolution depth map 25 is obtained for each image. In 3D mesh estimation, sensor pose estimates and depth maps can be used to identify objects of interest. [00156] In some implementations, a color or stereo camera 320 (FIG. 9) and the 3D speckle 1300 or the 3D TOF camera 1500 may be co-registered. A stand-off distance of 1 meter and 45-degree field of view 452 may give a reasonable circuit time and overlap 30 between views. If at least two pixels are needed for 50-percent detection, at least a 1 46 mega pixel resolution color camera may be used with a lens with a 45-degree field of view 452, with proportionately larger resolution for a 60 degree or wider field of view 452. [00157] Although a depth map sensor may have relatively low resolution and range 5 accuracy, it can reliably assign collections of pixels from the color/stereo image to a correct surface. This allows reduction of stereo vision errors due to lack of texture, and also, by bounding range to, e.g., a 5 cm interval, can reduce the disparity search range, and computational cost. [00158] Referring again to FIG. 10A, the first and second 3-D image sensors 450a, 10 450b can be used to improve mapping of the robot's environment to create a robot map, as the first 3-D image sensor 450a can be used to map out nearby objects and the second 3-D image sensor 450b can be used to map out distant objects. [00159] Referring to FIGS. 17A and 17B, in some circumstances, the robot 100 receives an occupancy map 1700 of objects 12 in a scene 10 and/or work area 5, or the 15 robot controller 500 produces (and may update) the occupancy map 1700 based on image data and/or image depth data received from an imaging sensor 450 (e.g., the second 3-D image sensor 450b) over time. In addition to localization of the robot 100 in the scene 10 (e.g., the environment about the robot 100), the robot 100 may travel to other points in a connected space (e.g., the work area 5) using the sensor system 400. The robot 100 may 20 include a short range type of imaging sensor 450a (e.g., mounted on the underside of the torso 140, as shown in FIGS. 1 and 3) for mapping a nearby area about the robot 110 and discerning relatively close objects 12, and a long range type of imaging sensor 450b (e.g., mounted on the head 160, as shown in FIGS. 1 and 3) for mapping a relatively larger area about the robot 100 and discerning relatively far away objects 12. The robot 100 can use 25 the occupancy map 1700 to identify known objects 12 in the scene 10 as well as occlusions 16 (e.g., where an object 12 should or should not be, but cannot be confirmed from the current vantage point). The robot 100 can register an occlusion 16 or new object 12 in the scene 10 and attempt to circumnavigate the occlusion 16 or new object 12 to verify the location of new object 12 or any objects 12 in the occlusion 16. 30 Moreover, using the occupancy map 1700, the robot 100 can determine and track 47 movement of an object 12 in the scene 10. For example, the imaging sensor 450, 450a, 450b may detect a new position 12' of the object 12 in the scene 10 while not detecting a mapped position of the object 12 in the scene 10. The robot 100 can register the position of the old object 12 as an occlusion 16 and try to circumnavigate the occlusion 16 to 5 verify the location of the object 12. The robot 100 may compare new image depth data with previous image depth data (e.g., the map 1700) and assign a confidence level of the location of the object 12 in the scene 10. The location confidence level of objects 12 within the scene 10 can time out or degrade after a threshold period of time. The sensor system 400 can update location confidence levels of each object 12 after each imaging 10 cycle of the sensor system 400. In some examples, a detected new occlusion 16 (e.g., a missing object 12 from the occupancy map 1700) within an occlusion detection period (e.g., less than ten seconds), may signify a "live" object 12 (e.g., a moving object 12) in the scene 10. [00160] In some implementations, a second object 12b of interest, located behind a 15 detected first object 12a in the scene 10, may be initially undetected as an occlusion 16 in the scene 10. An occlusion 16 can be area in the scene 10 that is not readily detectable or viewable by the imaging sensor 450, 450a, 450b. In the example shown, the sensor system 400 (e.g., or a portion thereof, such as imaging sensor 450, 450a, 450b) of the robot 100 has a field of view 452 with a viewing angle Ev (which can be any angle 20 between 0 degrees and 360 degrees) to view the scene 10. In some examples, the imaging sensor 450 includes omni-directional optics for a 360 degree viewing angle Ev; while in other examples, the imaging sensor 450, 450a, 450b has a viewing angle Ev of less than 360 degrees (e.g., between about 45 degrees and 180 degrees). In examples, where the viewing angle Ev is less than 360 degrees, the imaging sensor 450, 450a, 450b 25 (or components thereof) may rotate with respect to the robot body 110 to achieve a viewing angle Ev of 360 degrees. The imaging sensor 450, 450a, 450b may have a vertical viewing angle 8v-v the same as or different from a horizontal viewing angle EV-H For example, the imaging sensor 450, 450a, 450b may have a a horizontal field of view Ov-H of at least 45 degrees and a vertical field of view 8v-v of at least 40 degrees. In some 30 implementations, the imaging sensor 450, 450a, 450b or portions thereof, can move with 48 respect to the robot body 110 and/or drive system 200. Moreover, in order to detect the second object 12b, the robot 100 may move the imaging sensor 450, 450a, 450b by driving about the scene 10 in one or more directions (e.g., by translating and/or rotating on the work surface 5) to obtain a vantage point that allows detection of the second object 5 10b. Robot movement or independent movement of the imaging sensor 450, 450a, 450b, or portions thereof, may resolve monocular difficulties as well. [00161] A confidence level may be assigned to detected locations or tracked movements of objects 12 in the working area 5. For example, upon producing or updating the occupancy map 1700, the controller 500 may assign a confidence level for 10 each object 12 on the map 1700. The confidence level can be directly proportional to a probability that the object 12 actually located in the working area 5 as indicated on the map 1700. The confidence level may be determined by a number of factors, such as the number and type of sensors used to detect the object 12. For example, the contact sensor 430 may provide the highest level of confidence, as the contact sensor 430 senses actual 15 contact with the object 12 by the robot 100. The imaging sensor 450 may provide a different level of confidence, which may be higher than the proximity sensor 430. Data received from more than one sensor of the sensor system 400 can be aggregated or accumulated for providing a relatively higher level of confidence over any single sensor. [00162] Odometry is the use of data from the movement of actuators to estimate 20 change in position over time (distance traveled). In some examples, an encoder is disposed on the drive system 200 for measuring wheel revolutions, therefore a distance traveled by the robot 100. The controller 500 may use odometry in assessing a confidence level for an object location. In some implementations, the sensor system 400 includes an odometer and/or an angular rate sensor (e.g., gyroscope or the IMU 470) for 25 sensing a distance traveled by the robot 100. A gyroscope is a device for measuring or maintaining orientation, based on the principles of conservation of angular momentum. The controller 500 may use odometry and/or gyro signals received from the odometer and/or angular rate sensor, respectively, to determine a location of the robot 100 in a working area 5 and/or on an occupancy map 1700. In some examples, the controller 500 30 uses dead reckoning. Dead reckoning is the process of estimating a current position 49 based upon a previously determined position, and advancing that position based upon known or estimated speeds over elapsed time, and course. By knowing a robot location in the working area 5 (e.g., via odometry, gyroscope, etc.) as well as a sensed location of one or more objects 12 in the working area 5 (via the sensor system 400), the controller 5 500 can assess a relatively higher confidence level of a location or movement of an object 12 on the occupancy map 1700 and in the working area 5 (versus without the use of odometry or a gyroscope). [00163] Odometry based on wheel motion can be electrically noisy. The controller 500 may receive image data from the imaging sensor 450 of the environment or scene 10 10 about the robot 100 for computing robot motion, independently of wheel based odometry of the drive system 200, through visual odometry. Visual odometry may entail using optical flow to determine the motion of the imaging sensor 450. The controller 500 can use the calculated motion based on imaging data of the imaging sensor 450 for correcting any errors in the wheel based odometry, thus allowing for improved mapping and motion 15 control. Visual odometry may have limitations with low-texture or low-light scenes 10, if the imaging sensor 450 cannot track features within the captured image(s). [00164] Other details and features on odometry and imaging systems, which may combinable with those described herein, can be found in U.S. Patent 7,158,317 (describing a "depth-of field" imaging system), and U.S. Patent 7,115,849 (describing 20 wavefront coding interference contrast imaging systems), the contents of which are hereby incorporated by reference in their entireties. [00165] Referring to FIG. 18, in some implementations, the imaging sensor 450 has an imaging dead zone 453, which is a volume of space about the imaging sensor 450 in which objects are not detected. In some examples, the imaging dead zone 453 includes 25 volume of space defined by a first angle a by a second angle f and by a radius Rs of about 570 x 450 x 50 cm, respectively, immediately proximate the imaging sensor 450 and centered about an imaging axis 455. The dead zone 453 is positioned between the imaging sensor 450 and a detection field 457 of the imaging sensor 450 within the field of view 452. 50 [00166] In the example shown in FIG. 19, the robot 100 includes a first and second imaging sensors 450a, 450b (e.g., 3D depth imaging sensors) disposed on the torso 140. Both imaging sensors 450a, 450b are arranged to have field of view 452 along the forward drive direction F. The first imaging sensor 450a is arranged to aim its imaging 5 axis 455 substantially downward and away from the robot 100 (e.g., to view an area on the ground and/or about a lower portion of the robot) to detect objects before contact with the base 120 or leg 130. By angling the first imaging sensor 450a downward, the robot 100 receives dense sensor coverage in an area immediately forward or adjacent to the robot 100, which is relevant for short-term travel of the robot 100 in the forward 10 direction. The second imaging sensor 450b is arranged with its imaging axis 455 pointing substantially parallel with the ground along the forward drive direction F (e.g., to detect objects approaching a mid and/or upper portion of the robot 100). In other examples, the second imaging sensor 450b is arranged with its imaging axis 455 pointing above the ground or even upward away from the ground. 15 [00167] If the imaging sensors 450a, 450b have dead zones 453, there is a possibility of failing to detect an object proximate or adjacent the robot 100. In the example shown in FIG. 10A, the robot 100 includes an imaging sensor 450 mounted on the head 160, which can pan and tilt via the neck 150. As a result, the robot 100 can move the imaging sensor 450 on the head to view the dead zones 453 of the other imaging sensors 450a, 20 450b, thus providing complete or substantially complete fields of view 452 about the robot 100 for object detection. When placement of an imaging sensor 450 on the head 160 is not possible or if an imaging sensor 450 cannot be moved to view the dead zones 453, other techniques may be employed to view the dead zones 453. In addition to dead zones 453, some objects within the field of view 452 of the imaging sensor 450 can be 25 difficult to detect, due to size, shape, reflectivity, and/or color. For example, sometimes highly reflective or specular objects can be difficult to detect. In other examples, very dark or black objects can be difficult to detect. Moreover, slender objects (i.e., having a very thin profile) may be difficult to detect. Hard to detect objects may be become relatively more detectable when viewed from multiple angles or sensed from multiple 30 sensors. 51 [00168] In the example shown in FIGS. 1, 4C and 10A, the robot includes one or more sonar proximity sensors 410 (e.g., 410a-410i) disposed around the base body 120 are arranged to point upward (e.g., substantially in the Z direction) and optionally angled outward away from the Z axis, thus creating a detection curtain 412 around the robot 100. 5 The sonar proximity sensors 410 can be arranged and aimed to sense objects within the dead zone 453 of each imaging sensor 450. [00169] In some implementations, the robot 100 (via the controller 500 or the sensor system 400) moves or pans the imaging sensors 450, 450a, 450b to gain view-ability of the corresponding dead zones 453. An imaging sensor 450 can be pointed in any 10 direction 3600 (+/- 1800) by moving its associated imaging axis 455. [00170] In some examples, the robot 100 maneuvers itself on the ground to move the imaging axis 455 and corresponding field of view 452 of each imaging sensor 450 to gain perception of the volume of space once in a dead zone 453. For example, the robot 100 may pivot in place, holonomically move laterally, move forward or backward, or a 15 combination thereof. In additional examples, if the imaging sensor 450 has a limited field of view 452 and/or detection field 457, the controller 500 or the sensor system 400 can actuate the imaging sensor 450 in a side-to-side and/or up and down scanning manner to create a relatively wider and/or taller field of view to perform robust ODOA. Panning the imaging sensor 450 (by moving the imaging axis 455) increases an associated 20 horizontal and/or vertical field of view, which may allow the imaging sensor 450 to view not only all or a portion of its dead zone 453, but the dead zone 453 of another imaging sensor 450 on the robot 100. [00171] In some examples, each imaging sensor 450 may have an associated actuator (not shown) moving the imaging sensor 450 in the scanning motion. In additional 25 examples, the imaging sensor 450 includes an associated rotating a mirror, prism, variable angle micro-mirror, or MEMS mirror array to increase the field of view 452 and/or detection field 457 of the imaging sensor 450. [00172] In the example shown in FIG. 20, the torso 140 pivots about the Z-axis on the leg 130, allowing the robot 100 to move an imaging sensor 450 disposed on the torso 140 30 with respect to the forward drive direction F defined by the base 120. In some examples, 52 the leg 130 pivots about the Z-axis, thus moving the torso 140 about the Z-axis. In either example, an actuator 138 (such as a rotary actuator) in communication wit the controller 500 rotates the torso 140 with respect to the base 120 (e.g., by either rotating the torso 140 with respect to the leg 130 and/or rotating the leg 130 with respect to the base 120). 5 The rotating torso 140 moves the imaging sensor 450 in a panning motion about the Z axis providing up to a 3600 field of view 452 about the robot 100. The robot 100 may pivot the torso 140 in a continuous 360' or +/- an angle 180' with respect to the forward drive direction F. [00173] Referring to FIG. 21, in some implementations, the robot 100 includes a dead 10 zone sensor 490 associated with each imaging sensor 450 and arranged to sense objects within the dead zone 453 of the associated imaging sensor 450. The dead zone sensor 490 may be a sonar sensor, camera, ultrasonic sensor, LIDAR, LADAR, optical sensor, infrared sensor, etc. In the example shown, the dead zone sensor 490 is arranged to have field of view 492 enveloping or substantially enveloping the dead zone 453. FIG. 22 15 provides a top of view of a robot 100 having a dead zone sensor 490 disposed on the torso 140 adjacent the imaging sensor 450 and arranged to have its field view 492 extending into the dead zone 453. In the example shown the dead zone field of view 492 is substantially centered within the dead zone 453; however, other arrangements are possible as well (e.g., off-center). 20 [00174] FIG. 23 illustrates an exemplary robot 100 having an array of dead zone sensors 490 disposed on a forward portion 147 of the torso 140. The array of dead zone sensors 490 not only provide coverage of the dead zone 453 shown, but also additional areas about the robot 100 not previously within the field of view of a sensor (e.g., the areas on each side of the field of view 452 of the imaging sensor 450). This allows the 25 robot 100 to sense nearby objects before moving or turning into them. [00175] In the example shown in FIG. 24, the robot 100 includes at least one long range sensor 2190 arranged and configured to detect an object 12 relatively far away from the robot 100 (e.g., > 3 meters). The long range sensor 2190 may be an imaging sensor 450 (e.g., having optics or a zoom lens configured for relatively long range 30 detection). In additional examples, the long range sensor 2190 is a camera (e.g., with a 53 zoom lens), a laser range finder, LIDAR, RADAR, etc. In the example shown, the robot 100 includes four long range sensors 2190 arranged with corresponding fields of view 2192 along forward, aft, right, and left drive directions. Other arrangements are possible as well. 5 [00176] Detection of far off objects allows the robot 100 (via the controller 500) to execute navigational routines to avoid the object, if viewed as an obstacle, or approach the object, if viewed as a destination (e.g., for approaching a person for executing a video conferencing session). Awareness of objects outside of the field of view of the imaging sensor(s) 450 on the robot 100, allows the controller 500 to avoid movements that may 10 place the detected object 12 in a dead zone 453. Moreover, in person following routines, when a person moves out of the field of view of an imaging sensor 450, the long range sensor 2190 may detect the person and allow the robot 100 to maneuver to regain perception of the person in the field of view 452 of the imaging sensor 450. [00177] Referring to FIG. 25, in some implementations, the controller 500 executes a 15 control system 510, which includes a control arbitration system 5 1Oa and a behavior system 510b in communication with each other. The control arbitration system 510a allows applications 520 to be dynamically added and removed from the control system 510, and facilitates allowing applications 520 to each control the robot 100 without needing to know about any other applications 520. In other words, the control arbitration 20 system 5 10a provides a simple prioritized control mechanism between applications 520 and resources 530 of the robot 100. The resources 530 may include the drive system 200, the sensor system 400, and/or any payloads or controllable devices in communication with the controller 500. [00178] The applications 520 can be stored in memory of or communicated to the 25 robot 100, to run concurrently on (e.g., a processor) and simultaneously control the robot 100. The applications 520 may access behaviors 600 of the behavior system 510b. The independently deployed applications 520 are combined dynamically at runtime and to share robot resources 530 (e.g., drive system 200, arm(s), head(s), etc.) of the robot 100. A low-level policy is implemented for dynamically sharing the robot resources 530 30 among the applications 520 at run-time. The policy determines which application 520 54 has control of the robot resources 530 required by that application 520 (e.g. a priority hierarchy among the applications 520). Applications 520 can start and stop dynamically and run completely independently of each other. The control system 510 also allows for complex behaviors 600 which can be combined together to assist each other. 5 [00179] The control arbitration system 5 1Oa includes one or more resource controllers 540, a robot manager 550, and one or more control arbiters 560. These components do not need to be in a common process or computer, and do not need to be started in any particular order. The resource controller 540 component provides an interface to the control arbitration system 510a for applications 520. There is an instance of this 10 component for every application 520. The resource controller 540 abstracts and encapsulates away the complexities of authentication, distributed resource control arbiters, command buffering, and the like. The robot manager 550 coordinates the prioritization of applications 520, by controlling which application 520 has exclusive control of any of the robot resources 530 at any particular time. Since this is the central 15 coordinator of information, there is only one instance of the robot manager 550 per robot. The robot manager 550 implements a priority policy, which has a linear prioritized order of the resource controllers 540, and keeps track of the resource control arbiters 560 that provide hardware control. The control arbiter 560 receives the commands from every application 520 and generates a single command based on the applications' priorities and 20 publishes it for its associated resources 530. The control arbiter 560 also receives state feedback from its associated resources 530 and sends it back up to the applications 520. The robot resources 530 may be a network of functional modules (e.g. actuators, drive systems, and groups thereof) with one or more hardware controllers. The commands of the control arbiter 560 are specific to the resource 530 to carry out specific actions. 25 [00180] A dynamics model 570 executable on the controller 500 can be configured to compute the center for gravity (CG), moments of inertia, and cross products of inertia of various portions of the robot 100 for the assessing a current robot state. The dynamics model 570 may also model the shapes, weight, and/or moments of inertia of these components. In some examples, the dynamics model 570 communicates with an inertial 30 moment unit 470 (IMU) or portions of one (e.g., accelerometers and/or gyros) disposed 55 on the robot 100 and in communication with the controller 500 for calculating the various center of gravities of the robot 100. The dynamics model 570 can be used by the controller 500, along with other programs 520 or behaviors 600 to determine operating envelopes of the robot 100 and its components. 5 [00181] Each application 520 has an action selection engine 580 and a resource controller 540, one or more behaviors 600 connected to the action selection engine 580, and one or more action models 590 connected to action selection engine 580. The behavior system 510b provides predictive modeling and allows the behaviors 600 to collaboratively decide on the robot's actions by evaluating possible outcomes of robot 10 actions. In some examples, a behavior 600 is a plug-in component that provides a hierarchical, state-full evaluation function that couples sensory feedback from multiple sources with a-priori limits and information into evaluation feedback on the allowable actions of the robot. Since the behaviors 600 are pluggable into the application 520 (e.g., residing inside or outside of the application 520), they can be removed and added without 15 having to modify the application 520 or any other part of the control system 510. Each behavior 600 is a standalone policy. To make behaviors 600 more powerful, it is possible to attach the output of multiple behaviors 600 together into the input of another so that you can have complex combination functions. The behaviors 600 are intended to implement manageable portions of the total cognizance of the robot 100. 20 [00182] The action selection engine 580 is the coordinating element of the control system 510 and runs a fast, optimized action selection cycle (prediction/correction cycle) searching for the best action given the inputs of all the behaviors 600. The action selection engine 580 has three phases: nomination, action selection search, and completion. In the nomination phase, each behavior 600 is notified that the action 25 selection cycle has started and is provided with the cycle start time, the current state, and limits of the robot actuator space. Based on internal policy or external input, each behavior 600 decides whether or not it wants to participate in this action selection cycle. During this phase, a list of active behavior primitives is generated whose input will affect the selection of the commands to be executed on the robot 100. 56 [00183] In the action selection search phase, the action selection engine 580 generates feasible outcomes from the space of available actions, also referred to as the action space. The action selection engine 580 uses the action models 590 to provide a pool of feasible commands (within limits) and corresponding outcomes as a result of simulating the 5 action of each command at different time steps with a time horizon in the future. The action selection engine 580 calculates a preferred outcome, based on the outcome evaluations of the behaviors 600, and sends the corresponding command to the control arbitration system 510a and notifies the action model 590 of the chosen command as feedback. 10 [00184] In the completion phase, the commands that correspond to a collaborative best scored outcome are combined together as an overall command, which is presented to the resource controller 540 for execution on the robot resources 530. The best outcome is provided as feedback to the active behaviors 600, to be used in future evaluation cycles. [00185] Received sensor signals from the sensor system 400 can cause interactions 15 with one or more behaviors 600 to execute actions. For example, using the control system 510, the controller 500 selects an action (or move command) for each robotic component (e.g., motor or actuator) from a corresponding action space (e.g., a collection of possible actions or moves for that particular component) to effectuate a coordinated move of each robotic component in an efficient manner that avoids collisions with itself 20 and any objects about the robot 100, which the robot 100 is aware of. The controller 500 can issue a coordinated command over robot network, such as the EtherlO network. [00186] The control system 510 may provide adaptive speed/acceleration of the drive system 200 (e.g., via one or more behaviors 600) in order to maximize stability of the robot 100 in different configurations/positions as the robot 100 maneuvers about an area. 25 [00187] In some implementations, the controller 500 issues commands to the drive system 200 that propels the robot 100 according to a heading setting and a speed setting. One or behaviors 600 may use signals received from the sensor system 400 to evaluate predicted outcomes of feasible commands, one of which may be elected for execution (alone or in combination with other commands as an overall robot command) to deal with 30 obstacles. For example, signals from the proximity sensors 410 may cause the control 57 system 510 to change the commanded speed or heading of the robot 100. For instance, a signal from a proximity sensor 410 due to a nearby wall may result in the control system 510 issuing a command to slow down. In another instance, a collision signal from the contact sensor(s) due to an encounter with a chair may cause the control system 510 to 5 issue a command to change heading. In other instances, the speed setting of the robot 100 may not be reduced in response to the contact sensor; and/or the heading setting of the robot 100 may not be altered in response to the proximity sensor 410. [00188] The behavior system 51Ob may include a mapping behavior 600a for producing an occupancy map 1700, an object detection obstacle avoidance (ODOA) 10 behavior 600b, a speed behavior 600c (e.g., a behavioral routine executable on a processor) configured to adjust the speed setting of the robot 100 and a heading behavior 600d configured to alter the heading setting of the robot 100. The speed and heading behaviors 600c, 600d may be configured to execute concurrently and mutually independently. For example, the speed behavior 600c may be configured to poll one of 15 the sensors (e.g., the set(s) of proximity sensors 410, 420), and the heading behavior 600d may be configured to poll another sensor (e.g., the kinetic bump sensor). [00189] Referring to FIGS. 25 and 26A-26D, in some implementations, to navigate to a destination location, the robot 100 may rely on its ability to discern its local perceptual space 2100 (i.e., the space around the robot 100 as perceived through the sensor system 20 400) and execute an object detection obstacle avoidance (ODOA) strategy. The sensor system 400 may provide sensor data including three-dimensional depth image data provided by a volumetric point cloud imaging device 450 positioned on the robot 100 to be capable of obtaining a point cloud from a volume of space adjacent the robot 100. For example, the volumetric point cloud imaging device 450 may be positioned on the robot 25 100 at a height of greater than 2 feet above the ground and directed to be capable of obtaining a point cloud from a volume of space that includes a floor plane G in a direction of movement of the robot 100. [00190] The robot 100 (e.g., the control system 510 shown in FIG. 25) may classify its local perceptual space 2100 into three categories: obstacles (black) 2102, unknown (gray) 30 2104, and known free (white) 2106. Obstacles 2102 are observed (i.e., sensed) points 58 above the ground G that are below a height of the robot 100 and observed points below the ground G (e.g., holes, steps down, etc.). Known free 2106 corresponds to areas where the 3-D image sensors 450 can see the ground G. Data from some or all sensors in the sensor system 400 can be combined into a discretized 3-D voxel grid. The 3-D grid can 5 then be analyzed and converted into a 2-D grid 2101 with the three local perceptual space classifications. FIG. 26A provides an exemplary schematic view of the local perceptual space 2100 of the robot 100 while stationary. The information in the 3-D voxel grid has persistence, but decays over time if it is not reinforced (e.g., by fresh sensor data). When the robot 100 is moving, it has more known free area 2106 to navigate in because of 10 persistence. The volumetric point cloud data of the 3-D imaging sensor 450 may time out after a threshold period of time, such as milliseconds to seconds, so that transient or slightly older objects in the environment (e.g., people walking, sensor artifacts, etc.) are not used for local path planning. [00191] When the 3-D imaging sensor 450 has a dead zone 453 (FIGS. 20-24) and the 15 robot 100 maneuvers itself immediately next to a non-transient object 12 entirely within the dead zone 453 (FIG. 26A), the control system 510 may allow the sensor data associated with that non-transient object 12 to time-out and hence no longer recognize the object 12 as an obstacle 2102 for navigation purposes. Although other sensors of the sensor system 400, such as the dead zone sensor(s) 490, may be able to detect the object 20 12, the control system 510 may execute an ODOA strategy that suspends the data time out for sensor data associated with that object 12 in the dead zone 453. [00192] For example, the control system 510 may suspend the data time-out for sensor data, which normally times out after a threshold period of time and is associated with obstacles 2102 (e.g., objects 12) in the local perceptual 2100, when the obstacle 2102 is 25 perceived as residing in the dead zone 453 or an area immediately adjacent the robot 100. The control system 510 may determine the presence of an object 12 corresponding to the obstacle 2102 in the dead zone 453, using one or more dead zone sensors 490 or other sensor(s) of the sensor system 400 as near sensors. The control system 510 may allow the sensor data associated with that object 12 to decay or time-out again only after the 30 robot 100 has moved away from that location and/or the dead zone sensor(s) 490 detect 59 that the object 12 has moved out of the dead zone 453. This allows the control system 510 to execute object detection obstacle avoidance (ODOA) navigation strategies that consider the possibility of an obstacle in the dead zone 453 of the robot 100. [00193] An object detection obstacle avoidance (ODOA) navigation strategy for the 5 control system 510 may include either accepting or rejecting potential robot positions that would result from commands. Potential robot paths 2110 can be generated many levels deep with different commands and resulting robot positions at each level. FIG. 26B provides an exemplary schematic view of the local perceptual space 2100 of the robot 100 while moving. An ODOA behavior 600b (FIG. 25) can evaluate each predicted 10 robot path 2110. These evaluations can be used by the action selection engine 580 to determine a preferred outcome and a corresponding robot command. For example, for each robot position 2120 in the robot path 2110, the ODOA behavior 600b can execute a method for object detection and obstacle avoidance that includes identifying each cell 2103 in the grid 2101 that is in a bounding shape 2107 (e.g., collision box , triangle, or 15 circle) around a corresponding position 2120 of the robot 100, receiving a classification of each cell 2103. For each cell 2103 classified as an obstacle 2102 or unknown 2104, retrieving a grid point 2105 corresponding to the cell 2103 and executing a collision check by determining if the grid point 2105 is within a bounding shape 2107 (e.g., collision circle) about a location 2120 of the robot 100. If the grid point 2105 is within 20 the bounding shape 2107, the method further includes executing a triangle test of whether the grid point 2105 is within a bounding shape 2107 shaped as a triangle (e.g., the robot 100 can be modeled as triangle). If the grid point 2105 is within the collision triangle 2107, the method includes rejecting the grid point 2105. If the robot position 2120 is inside of a sensor system field of view 405 of parent grid points 2105 on the robot path 25 2110, then the "unknown" grid points 2105 are ignored because it is assumed that by the time the robot 100 reaches those grid points 2105, they will be known. [00194] The method may include determining whether any obstacle collisions are present within a robot path area (e.g., as modeled by a rectangle) between successive robot positions 2120 in the robot path 2110, to prevent robot collisions during the 30 transition from one robot position 2120 to the next. 60 [00195] FIG. 26C provides a schematic view of the local perceptual space 2100 of the robot 100 and a sensor system field of view 405 (the control system 510 may use only certain sensor, such as the first and second 3-D image sensors 450a, 450b, for robot path determination). Taking advantage of the holonomic mobility of the drive system 200, the 5 robot 100 can use the persistence of the known ground G to allow it to drive in directions where the sensor system field of view 405 does not actively cover. For example, if the robot 100 has been sitting still with the first and second 3-D image sensors 450a, 450b pointing forward, although the robot 100 is capable of driving sideways, the control system 510 will reject the proposed move, because the robot 100 does not know what is 10 to its side, as illustrated in the example shown in FIG. 26C, which shows an unknown classified area to the side of the robot 100. If the robot 100 is driving forward with the first and second 3-D image sensors 450a, 450b pointing forward, then the ground G next to the robot 100 may be classified as known free 2106, because both the first and second 3-D image sensors 450a, 450b can view the ground G as free as the robot 100 drives 15 forward and persistence of the classification has not decayed yet. (See e.g., FIG. 26B.) In such situations the robot 100 can drive sideways. [00196] Referring to FIG. 26D, in some examples, given a large number of possible trajectories with holonomic mobility, the ODOA behavior 600b may cause robot to choose trajectories where it will (although not currently) see where it is going. For 20 example, the robot 100 can anticipate the sensor field of view orientations that will allow the control system 510 to detect objects. Since the robot can rotate while translating, the robot can increase the sensor field of view 405 while driving. [00197] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs 25 (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and 30 instructions to, a storage system, at least one input device, and at least one output device. 61 [00198] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable 5 medium" and "computer-readable medium" refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to 10 any signal used to provide machine instructions and/or data to a programmable processor. [00199] Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. 15 Embodiments of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, 20 a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. The term "data processing apparatus" encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution 25 environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus. 62 [00200] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand alone program or as a module, component, subroutine, or other unit 5 suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of 10 code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. [00201] The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to 15 perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). [00202] Processors suitable for the execution of a computer program include, by way 20 of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also 25 include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just 30 a few. Computer readable media suitable for storing computer program instructions and 63 data include all forms of non volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can 5 be supplemented by, or incorporated in, special purpose logic circuitry. [00203] Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface 10 or a web browser through which a user can interact with an implementation of the subject matter described is this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area 15 network ("LAN") and a wide area network ("WAN"), e.g., the Internet. [00204] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 20 [00205] While this specification contains many specifics, these should not be construed as limitations on the scope of the invention or of what may be claimed, but rather as descriptions of features specific to particular implementations of the invention. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. 25 Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable sub combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed 30 combination may be directed to a sub-combination or variation of a sub-combination. 64 [00206] Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multi-tasking and 5 parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 10 [00207] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. 15 65

Claims (28)

1. A mobile robot comprising: a drive system having a forward drive direction; 5 a controller in communication with the drive system; a volumetric point cloud imaging device supported above the drive system and directed to be capable of obtaining a point cloud from a volume of space that includes a floor plane in a direction of movement of the mobile robot; and proximity sensors with an upward field of view for detecting objects that are in a 10 horizontal plane; wherein the controller receives point cloud signals from the imaging device and detection signals from the proximity sensors and issues drive commands to the drive system based at least in part on the received point cloud and detection signals. 15

2. The mobile robot of claim 1, wherein an array of the proximity sensors is disposed around a base of the mobile robot and arranged with the upward field of view.

3. The mobile robot of claim 2, wherein the array of the proximity sensors creates a detection curtain around the mobile robot, the detection curtain detecting 20 obstacles having elevated lateral protruding portions.

4. The mobile robot of any preceding claim, wherein the proximity sensors provide an ability to see objects that are table tops. 25

5. The mobile robot of any preceding claim, wherein the controller issues drive commands to the drive system so as to change commanded speed or heading of the mobile robot, based on the detection signals from the proximity sensors. 66

6. The mobile robot of claim 5, wherein the controller issues the drive commands to the drive system so as to slow down the mobile robot, based on the detection signals from the proximity sensors due to a nearby wall. 5

7. The mobile robot of any preceding claim, further comprising a speed behavior configured to adjust a speed setting of the mobile robot, the speed behavior configured to poll the proximity sensors.

8. The mobile robot of any preceding claim, further comprising a dead zone 10 sensor having a detection field arranged to detect an object in a volume of space undetectable by the volumetric point cloud imaging device.

9. The mobile robot of claim 8., wherein the dead zone sensor comprises at least one of a sonar sensor, a camera, an ultrasonic sensor, LIDAR, LADAR, an optical 15 sensor, or an infrared sensor.

10. The mobile robot of claim 8 or 9, wherein the detection field of the dead zone sensor envelopes a volume of space undetectable by the volumetric point cloud imaging device. 20

11. The mobile robot of claim 10, wherein the volume of space undetectable by the volumetric point cloud imaging device is defined by a first angle by a second angle and by a radius immediately proximate the volumetric point cloud imaging device and centered about an imaging axis. 25

12. The mobile robot of any of claims 8-11, wherein the detection field of the dead zone sensor is arranged between the volumetric point cloud imaging device and a detection field of the volumetric point cloud imaging device. 67

13. The mobile robot of any of claims 8-12, wherein the dead zone sensor has a field of view extending at least three meters outward from the dead zone sensor.

14. The mobile robot of any of claims 8-13, further comprising an array of 5 dead zone sensors with at least one dead zone sensor having its detection field arranged to detect an object in the volume of space undetectable by the volumetric point cloud imaging device, the array of dead zone sensors arranged with their fields of view along the forward drive direction or evenly disbursed about a vertical center axis defined by the robot. 10

15. The mobile robot of any of the preceding claims, wherein the imaging device comprises first and second portions, the first portion arranged to emit light substantially onto the ground and receive reflections of the emitted light from the ground, and 15 the second portion arranged to emit light into a scene substantially above the ground and receive reflections of the emitted light from the scene about the robot.

16. A method of object detection for a mobile robot, the method comprising: rotating an imaging sensor about a vertical axis of the robot, the imaging sensor 20 emitting light onto a scene about the robot and capturing images of the scene, the images comprising at least one of (a) a three-dimensional depth image, (b) an active illumination image, and (c) an ambient illumination image; determining a location of an object in the scene based on the images; assigning a confidence level for the object location; 25 maneuvering the robot in the scene based on the object location and corresponding confidence level ; detecting objects that are in a horizontal plane by using proximity sensors with an upward field of view; and changing commanded speed or heading of the robot based on the detected objects. 30 68

17. The method of claim 16, further comprising constructing an object occupancy map of the scene.

18. The method of claim 16 or 17, further comprising degrading the 5 confidence level of each object location over time until updating the respective object location with a newly determined object location.

19. The method of claim 18, further comprising: detecting an object in a volume of space undetectable by the imaging sensor using 10 a dead zone sensor having a detection field arranged to detect an object in the volume of space undetectable by the imaging sensor; and ceasing degradation of the confidence level of the detected object.

20. The method of claim 19, further comprising continuing degradation of the 15 confidence level of the detected object upon detecting that the volume of space undetectable by the imaging sensor is free of that object.

21. The method of any of claims 16-20, further comprising maneuvering the robot to at least one of: 20 a) contact the object and follow along a perimeter of the object, or b) avoid the object.

22. The method of any of claims 16-21, further comprising emitting the light onto the scene in intermittent pulses, optionally altering a frequency of the emitted light 25 pulses, preferably emitting the light pulses at a first, power saving frequency and upon receiving a sensor event emitting the light pulses at a second, active frequency, the sensor event preferably comprising a sensor signal indicative of the presence of an object in the scene. 69

23. The method of any of claims 16-22, further comprising constructing the three-dimensional depth image of the scene by: emitting a speckle pattern of light onto the scene; receiving reflections of the speckle pattern from the object in the scene; 5 storing reference images of the speckle pattern as reflected off a reference object in the scene, the reference images captured at different distances from the reference object; capturing at least one target image of the speckle pattern as reflected off a target object in the scene; and 10 comparing the at least one target image with the reference images for determining a distance of the reflecting surfaces of the target object.

24. The method of claim 23, further comprising determining a primary speckle pattern on the target object and computing at least one of a respective cross 15 correlation and a decorrelation between the primary speckle pattern and the speckle patterns of the reference images.

25. The method of any of claims 23 or 24, further comprising capturing frames of reflections of the emitted speckle pattern off surfaces of the target object at a 20 frame rate, preferably between about 10 Hz and about 90 Hz, and optionally resolving differences between speckle patterns captured in successive frames for identification of the target object.

26. The method of claim 16, wherein the proximity sensors comprise an array 25 of the proximity sensors disposed around a base of the mobile robot and arranged with the upward field of view.

27. The method of claim 26, wherein the array of the proximity sensors creates a detection curtain around the mobile robot, the detection curtain detecting 30 obstacles having elevated lateral protruding portions. 70

28. The method of claim 26 or 27, wherein the proximity sensors provide an ability to see objects that are table tops. 71