Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
  • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
  • B25J9/00—Programme-controlled manipulators
  • B25J9/16—Programme controls
  • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
  • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
  • B25J9/1666—Avoiding collision or forbidden zones
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
  • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
  • G05D1/0088—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/35—Nc in input of data, input till input file format
  • G05B2219/35148—Geometric modeling for swept volume of moving solids
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/37—Measurements
  • G05B2219/37425—Distance, range
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/40—Robotics, robotics mapping to robotics vision
  • G05B2219/40447—Bitmap based
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/40—Robotics, robotics mapping to robotics vision
  • G05B2219/40474—Using potential fields
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/40—Robotics, robotics mapping to robotics vision
  • G05B2219/40475—In presence of moving obstacles, dynamic environment

Definitions

• the present disclosure relates to a system and method of robot path planning with obstacle avoidance.
• the present disclosure is related to a method for planning movements of a robotic arm while avoiding static and dynamic obstacles in an uncertain environment.
• Robotic path planning methods try to find a trajectory of robot motion from an initial position to a goal position.
• the degrees of freedom (DOF) in the motion equals to the number of joints. Therefore, there are as many possible moves as the number of joints for each movement.
• DOF degrees of freedom
• Existing path planning methods may be categorized into two types: grid- based path planning and artificial potential field-based path planning.
• Grid-based path planning is usually a global path planning method which uses priori environment information to create the best possible path
• artificial potential field-based path planning is usually a local path planning method which recalculates a path to replace the initial plan in order to avoid obstacles whose locations are changing relative to the robot over time. Such obstacles may be called dynamic obstacles in the sequel.
• grid-based path planning methods the continuous space of joint angles may be discretized as a high-dimensional grid. The path planning may be performed in the grid of the joint angle space.
• the minimum Euclidean distances between robot and obstacles in the Cartesian space may be determined with Gilbert-Johnson-Keerthi (GJK) algorithm.
• GJK Gilbert-Johnson-Keerthi
• a repulsive speed may be generated by processing the Euclidean distances and used to activate the robotic arm to avoid collision with the obstacles.
• Grid-based path planning methods such as the A* algorithm explore all possible moves in the grid by minimizing a cost function.
• the generated path is unique.
• the planning process may be slow, since all neighbors in the grid may need to be explored. Therefore, such planning methods may not guarantee real time adjustment of trajectory to avoid collision with a moving obstacle if the obstacle is already close to the robotic arm and the motion is fast.
• Artificial potential field-based path planning methods explore all obstacles that are within a safety distance margin from the robotic arm and generate a repulsive speed to move away from the obstacles based on the minimum distance from each obstacle. The speed generation process is fast.
• the robot may be trapped in the middle of obstacles, since the individual repulsive speed for each obstacle may cancel each other, resulting a combined null repulsive speed, i.e., no motion of robot.
• the teachings disclosed herein relate to methods, systems, and programming for robot path planning.
• One aspect of the present disclosure provides for a method implemented on a machine having at least one processor, storage, and a communication platform capable of connecting to a network for robot path planning.
• the method comprises the steps of: obtaining, at a first time instant, depth information of a plurality of obstacles in an environment of a robot; generating a static distance map based on the depth information of the plurality of obstacles; computing, in accordance with a model, a path for the robot based on the static distance map; obtaining, at a second time instant, depth information of one or more obstacles in the environment of a robot; generating a dynamic distance map based on the one or more obstacles; for each of the one or more obstacles that satisfy a condition computing a vibration range of the obstacle based on a position of the obstacle and the static distance map, and classifying the obstacle as one of a dynamic obstacle and a static obstacle based on a criterion associated with the vibration range; and calculating a repulsive speed of the robot based on the dynamic distance map, the repulsive speed corresponding to a speed at which the robot deviates from the path to avoid each of the dynamic obstacles.
• a system for robot path planning comprising a processor configured to: obtain, at a first time instant, depth information of a plurality of obstacles in an environment of a robot; generate a static distance map based on the depth information of the plurality of obstacles; compute, in accordance with a model, a path for the robot based on the static distance map; obtain, at a second time instant, depth information of one or more obstacles in the environment of a robot; generate a dynamic distance map based on the one or more obstacles; for each of the one or more obstacles that satisfy a condition compute a vibration range of the obstacle based on a position of the obstacle and the static distance map, and classify the obstacle as one of a dynamic obstacle and a static obstacle based on a criterion associated with the vibration range; and calculate a repulsive speed of the robot based on the dynamic distance map, the repulsive speed corresponding to a speed at which the robot deviates from the path to avoid each of the
• a software product in accord with this concept, includes at least one machine-readable non- transitory medium and information carried by the medium.
• the information carried by the medium may be executable program code data, parameters in association with the executable program code, and/or information related to a user, a request, content, or other additional information.
• a non-transitory machine-readable medium having information recorded thereon for robot path planning, wherein the information, when read by a machine, causes the machine to perform the steps of: obtaining, at a first time instant, depth information of a plurality of obstacles in an environment of a robot; generating a static distance map based on the depth information of the plurality of obstacles; computing, in accordance with a model, a path for the robot based on the static distance map; obtaining, at a second time instant, depth information of one or more obstacles in the environment of a robot; generating a dynamic distance map based on the one or more obstacles; for each of the one or more obstacles that satisfy a condition computing a vibration range of the obstacle based on a position of the obstacle and the static distance map, and classifying the obstacle as one of a dynamic obstacle and a static obstacle based on a criterion associated with the vibration range; and calculating a repulsive speed of the robot based on the dynamic
• Fig. 1 depicts an exemplary system diagram for robot path planning and collision avoidance
• Fig. 2 depicts an exemplary flow diagram for robot path planning and collision avoidance
• FIG. 3 depicts an exemplary flow diagram for static distance map-based robot path planning
• FIG. 4 depicts an example of finding robot representative points
• FIG. 5 depicts an exemplary flow diagram for generating repulsive speed
• Fig. 6 depicts an exemplary flow diagram for generating escape speed
• Fig. 7 depicts an exemplary flow diagram for switching control between path planning and repulsive speed generation
• Fig. 8 depicts an exemplary flow diagram for classifying dynamic obstacle points
• Fig. 9 depicts an architecture of a computer which can be used to implement a specialized system incorporating the present teaching.
• the present disclosure is directed to a method and system for robot path planning while avoiding obstacles.
• the present disclosure is directed to a system and method of planning a path for a robotic arm having multiple joints while avoiding obstacles in an uncertain environment.
• uncertain environment it is meant that objects in the environment may change their locations. There may be new objects moving into the environment or existing objects moving out of the environment.
• a robotic arm as referred to herein is an arm of a robot having a plurality of segments.
• a pose (i.e., position and orientation) of the robotic arm may be determined by a plurality of values (e.g., angle values) each of which corresponds to an angle formed between consecutive operable segments of the robotic arm.
• Fig. 1 shows an exemplary system diagram 100 for facilitating robot path planning, i.e., robotic arm path planning, according to an embodiment of the present teaching.
• the system 100 includes a depth camera 102, a depth acquisition unit 104, a goal pose 106, an initial pose 108, a static distance map generation unit 110, a static distance map-based path planning unit 112, a dynamic distance map generation unit 114, a dynamic distance map-based repulsive speed generation unit 116, an obstacle position-based trap escape unit 118, an obstacle vibration-based control switch unit 120, and a robot joint speed generation unit 122.
• the output of the joint speed generation unit is the real time joint speed 124 of the robot.
• a robotic arm may have multiple joints.
• the robot’s arm may include multiple segments or links, wherein a connection between adjacent segments or links is referred to as a joint.
• Typical robots may have 6 or 7 joints, meaning that the robot may have 6 or 7 degrees of freedom (DOF) in movement.
• the links of a robot are rigid bodies.
• the representation of a robot arm may include, but not limit to, Computed Aided Design (CAD) model, primitive shapes (i.e. cylinders, circles), and finite number of points on the robot surface.
• CAD Computed Aided Design
• primitive shapes i.e. cylinders, circles
• finite number of points on the robot surface The surface points used to represent a robot arm are referred to as the robot representative points.
• the robot’s initial pose (i.e., position and orientation) 108 and goal pose 106 may be characterized by angles formed between the joints (referred to herein as joint angles). It must be appreciated that the robot’s arm may include an end-effector (i.e., a device or tool connected at an end of the robot’s arm). The goal pose 106 in joint angles may be computed from the desired end-effector’s pose (position and orientation) based on robot inverse kinematics. Please note that the initial pose may not necessarily only refer to a fixed starting pose of the robot. It may also refer to any position during the robot motion according to a planned path when a dynamic moving obstacle is detected and a re-planning of robot motion is needed.
• the robot position at which the re-planning will be made will be treated as the new initial pose, since it is the starting position for a new trajectory planning.
• the same may apply to the goal pose.
• the goal pose may refer to any new goal position when the target is detected to be moving and thus a re-planning is needed.
• the structural information of a robot working environment may be perceived by a depth measurement device 102, including, but not limited to, depth cameras, stereo vision systems, laser range sensors.
• the structure information in the form of depth data may be acquired by a depth acquisition unit 104.
• the objects in the environment may be perceived as finite number of points which have a numerical value associated with them, that number being the distance or “depth” from the origin of the depth measurement device along the view direction (z-direction).
• the depth acquisition unit may use the depth data to compute the complete 3-Dimensional (3D) coordinates (x-y-z) of a point in the coordinate system of the depth measurement device. After proper calibration, the 3D measurements may be transformed into the robot coordinate system.
• the 3D information may be used to generate a static distance map by the static distance map generation unit 110 and a dynamic distance map by the dynamic distance map generation unit 114.
• a static distance map may store the minimum distance from any 3D point in the robot space to static objects (called static obstacles), while a dynamic distance map may store the minimum distance from any 3D point in the robot space to moving obstacles (which may also be called dynamic obstacles below).
• the static distance map may be generated at the beginning of a path-planning e.g., based on depth information acquired at a first instance, while dynamic distance maps may be generated at a high frequency, i.e., once every second e.g., in a continuous manner in subsequent time instances.
• vibration Obstacles with fixed locations or small range of back-and-forth motion (here referred to as vibration ) may be perceived as static obstacles, while other moving obstacles may be perceived as dynamic obstacles.
• the vibration range of an obstacle refers to the range of location change. For example, a patient lying on a surgical bed may be considered as being vibrating (or moving) within certain range due to breathing. Due to the small range of motion, such a patient may be perceived as a static obstacle rather than a dynamic obstacle. The details of classifying obstacles into the two types will be described later in the disclosure.
• the static distance map-based path planning unit 112 may plan for a collision-free robot trajectory from initial pose 108 to goal pose 106.
• Robot path planning may usually be performed in a discretized space of joint angles.
• a joint angle configuration may also be referred to as a node in the grid of joint angles.
• the initial pose may thus be referred to as the initial node and the goal pose as the goal node.
• the path planning problem becomes that of finding a path starting from the initial node in the grid to the goal node in the grid while satisfying certain criteria.
• the static distance map-based path planning unit 112 may be based on a general grid-based path planning algorithm such as RRT and A*.
• the found path may be further smoothed by a path smoothing algorithm to generate a smooth trajectory.
• the path smoothing algorithm may be an interpolation-based or optimization-based algorithm.
• the static distance map-based path planning unit 112 may be referred to as path planning unit in short.
• the dynamic distance map-based repulsive speed generation unit 116 may generate a repulsive speed corresponding to a robot’s current pose when a dynamic obstacle is detected. As stated previously, the current pose may become a new initial pose when a re-planning is made. In what follows, the dynamic distance map-based repulsive speed generation unit 116 may be referred to as repulsive speed generation unit in short.
• the obstacle position-based trap escape unit 118 may generate an escape speed on top of repulsive speed to help robot escape from being trapped by the obstacles.
• the obstacle vibration-based control switch unit 120 may determine the vibration range of obstacle points and switch between path planning unit 112 and repulsive speed generation unit 116.
• the robot joint speed generation unit 122 may generate real time robot joint speed based on whether the input is a joint space trajectory (output of 112) or repulsive speed (output of 116).
• Fig. 2 illustrates an exemplary flow diagram for robot path planning and collision avoidance, according to one embodiment of the present teaching.
• the initial pose and goal pose of the robot may be obtained.
• the depth information of the environment may be obtained by a depth camera 102. The depth information may be converted to the 3D structural information of the environment.
• the static distance map may be generated for static obstacles based on the depth information. This may correspond to a time instance at the start.
• a collision-free robotic trajectory may be obtained by applying a path planning algorithm based on the static distance map.
• a dynamic distance map may be generated.
• the dynamic distance map may be generated at a rate proportional to the speed of motion for dynamic obstacles, such that the dynamic distance map may capture the distance information between the robot and the dynamic obstacles at an acceptable speed.
• a repulsive speed of the robot may be computed by using minimum distances between the robot and dynamic obstacle points based on the dynamic distance map.
• the relative positions between robot and obstacles may be used to compute an escape speed to help the robot escape from being trapped by obstacles. The robot is said to be trapped by obstacles when the robot is unable to move away from or escape from the obstacles to avoid a collision because the summation of repulsive speeds generated by individual obstacle points is zero.
• the vibration range of obstacle points may be determined and used by the obstacle vibration-based control switch unit 120 to switch between the path planning unit and the repulsive speed generation unit.
• the robot real time joint speed may be generated based on trajectory planning algorithms. Trajectory planning is to find a continuous trajectory of motion instead of discreate positions obtained by path planning.
• the input of the robot joint speed generation unit 122 may be a combination of joint space trajectory and repulsive joint speed.
• the trajectory planning algorithm may be a polynomial-based or spline-based algorithm.
• Fig. 3 illustrates an exemplary flow diagram of robot path planning of step
• the initial node may be set as current node.
• the neighbor nodes N ⁇ N t , iV 2 , , N K ] of the current node may be obtained, where K is the number of neighboring nodes.
• the current node may be set as the parent node of the neighbor nodes and the neighbor nodes may be set as the children nodes of the current node.
• the parent node of the initial node and the children nodes of the goal node may be set as empty.
• the path planning search starts with the first neighbor node N .
• the number of robot representative points may be calculated based on the minimum distance between the parent node and the obstacles. Intuitively, the use of dynamic number of representative points may be interpreted as follows. When the robot is away from the obstacles, the number of robot representative points may be smaller so that the collision detection may be performed on a smaller amount of points on the robot to reduce the planning time.
• the number of robot representative points may be increased so that the collision detection may become more accurate to allow the robot to go through narrow paths in between-obstacles.
• the representative points may be equally distributed along the robot surface or follow a specific distribution pattern. The details for obtaining robot representative points based on the point number are explained later.
• the number of representative points may be set as a pre-defined maximum value n max .
• the maximum value n max may be obtained by adding the maximum number of representative points for each robot link.
• An exemplar embodiment of the linear function may take the following form l
• s is the resolution of the robot link which may be determined based on the application. Intuitively, the resolution may be small if the robot workspace is surrounded by obstacles so that the representative points of the robot are dense enough to perform accurate collision detection and path planning.
• d p is the minimum distance from robot representative points at the parent node to the obstacles
• w is a function of d p .
• the distance d p may be defined as the Euclidian distance in the Cartesian space
• the function w(c n ) may be a linear or parabolic function of the distance d p .
• An exemplar embodiment of the linear function may take the following form dmax dp w ⁇ d v ) n max d max
• n max is the maximum number of robot representative points and d ma x is the robot maximum reachability, i.e., the maximum reachable distance.
• the minimum distance between each representative point and the obstacles may be obtained from static distance map.
• T( d) is a function of d.
• the function T(cZ) may be a linear or parabolic function of the distance d.
• An exemplar embodiment of the linear function may take the following form d
• d max is described above.
• step 312 it is checked that if the minimum distance obtained is below the threshold t. Intuitively, when the threshold is small, the robot may be very close to the obstacle surface. Thus, if the minimum distance is greater than the threshold, that neighbor node may be put into the neighbor list at step 314 for further examination later. Otherwise, the current node may be discarded (meaning a high risk of collision), and the neighbor index may be incremented by 1 to go to the next neighbor node at step 316.
• step 318 it is checked if the current node has reached the last neighboring node N K . If not, the above process may be repeated. Otherwise, it is checked if the goal node is in the neighbor list at step 320. If the goal node is not in the neighbor list, a cost may be calculated based on a cost function for each neighbor node in the list. The neighbor node of the minimum cost may be selected from the neighbor list and set as the current node at step 322 to repeat the path planning process. By one embodiment, the details of the cost function are described below. Suppose a trajectory includes N nodes, 1,2, ... ., N.
• the cost from the starting node to the «-th node may be denoted by g(n).
• the cost g(n) may be defined as the Euclidian distance between the joint angles (i.e., configuration of the end-effector) at the initial node and those at the «-th node.
• Another cost that provides an estimate of the cost from the «-th node to the goal node(N) may be denoted by h(n).
• the cost h(n) may be defined as the Euclidian distance between the joint angles at the «-th node and those at node N (the goal node).
• the robot trajectory may be obtained by backtracking from the goal node through parent nodes to the initial node at step 324.
• Fig. 4 illustrates one example of finding robot’s representative points (step 308).
• the cylinder represents a link of the robot arm.
• the number of representative points is twelve and they are equally distributed.
• One embodiment of the distribution is to put one third of the representative points on the top surface, one third on the bottom surface, and one third on the side surface of the link, respectively.
• On the top surface of the link there may be four representative points pl(402), p2(404), p3(406), p4(408) equally distributed at 90° interval.
• point p5(410) corresponds to point pl(402).
• the representative points on the side surface may be determined by dividing the length of the cylinder by a positive number (here two). As a result, there may be 4 points on the side surface (P8, P9, P10 and one behind the cylinder not visible). All the above points may be used as the robot representative points.
• Fig. 5 illustrates an exemplary flow of step 212 for generating the repulsive speed based on the dynamic distance map.
• the robot representative points may be obtained in the same way as described above.
• the first robot representative point may be selected.
• the minimum distance between the first robot representative point and the obstacles may be obtained from the dynamic distance map.
• the obstacles in the dynamic distance map may be based on a classification of dynamic obstacles described in Figure 8 of the present disclosure.
• it may be checked if the minimum distance is smaller than a safety margin s. If not, the robot representative point index may be incremented by 1 to go to the next point at step 518.
• a repulsive speed at the current representative point may be calculated based on the Artificial Potential Field (APF) algorithm.
• APF Artificial Potential Field
• s is a safety margin (the minimum distance allowed between the robot and obstacles)
• d is the Euclidean distance between p r and p 0 .
• the representative points that are within the safety margin s will be called the critical points of the robot.
• the closest point on the obstacle to a robot critical point is called the critical point of obstacle.
• the robot repulsive joint speed may be computed from the above repulsive speed as
• V(v) is a function of v.
• An exemplar embodiment of the function V ( v ) may take the following form
• V( ) /-
• J is the partial Jacobian matrix of the robot at the current representative point.
• the robot repulsive j oint speed may be reduced if it is larger than the maximum safety speed v s .
• the safety speed of a robot arm may be referred to as the maximum speed of the robot arm that is allowed for an application.
• the element with largest magnitude in the matrix Q may be determined.
• the joint speed may be re-computed as a function of the magnitude and the safety speed v s as
• v max is the largest magnitude
• R is a function of v max and v s .
• the function R(v max , v s ) may be a linear or parabolic function of the magnitude v max and safety speed v s .
• An exemplar embodiment of the function R(v max , v s ) may take the following form n v max A Jx - o
• the joint speed may be saved in a list L.
• the robot representative point index may be incremented by 1 to go to the next point.
• it is checked if the next point has reached the final point. If not, the above process may be repeated. Otherwise, the final robot joint speed is calculated by adding the repulsive joint speeds in the list L at step 522.
• the robot final joint speed may be expressed as a function of individual joint speeds in the list L as
• n is the total number of joint speeds in the list L.
• step 524 it is checked if the final robot joint speed 0 rep is zero. If not, the joint speed may be sent to robot joint speed generation unit 122 at step 528. Otherwise, an escape joint speed may be calculated at step 526, so that the robot may be able to escape from the trap of obstacles.
• Fig. 6 illustrates an exemplary flow for step 526 for computing the escape speed.
• the obstacle trap may occur when the addition of all joint speeds in list L is zero. In such cases, the robot may be trapped in the middle of multiple obstacles without being able to avoid a possible collision with them.
• the safety margin s may be increased to include more obstacles near the robot. The amount of increment may be determined empirically.
• the obstacles within the safety margin may be identified.
• the repulsive speed for avoiding the new obstacles may be calculated. This will serve as the escape speed for escaping the obstacle trap.
• the escape joint speed may be calculated from the repulsive speed as explained before.
• it may be checked if the escape joint speed is zero.
• Fig. 7 illustrates an exemplary flow of step 216 for control switch based on obstacle vibration range.
• the obstacle points within robot safety margin may be obtained based on the dynamic distance map and classified as static obstacle points or dynamic obstacle points based on their vibration range. The details for the classification will be described later.
• the path planning unit may generate robot trajectory at step 706.
• the robot may follow the planned trajectory.
• it may be checked if the robot is at the goal pose.
• the robot may stop at step 712. Otherwise, it may be checked again if any dynamic obstacles are within robot safety margin. Please note that the existence of dynamic objects is time-dependent, meaning that new dynamic objects may appear at any time. If any dynamic obstacles exist, the repulsive speed may be generated at step 714. At step 716, the robot may move away with the repulsive speed so that the dynamic obstacles are out of safety margin. At step 718, it is checked if the speed of dynamic obstacles is zero. If not, the process of checking for existence of dynamic obstacles may be repeated. Otherwise, the repulsive speed may be set to zero at step 720 until the dynamic obstacle moves again. The speed of dynamic obstacles may be checked through the dynamic distance map. If the minimum distance from the dynamic obstacles does not change or only change within a small range, the speed of dynamic obstacles may be considered zero.
• Fig. 8 illustrates an exemplary flow of step 702 for classifying obstacle points within the robot safety margin.
• the dynamic distance map may be updated.
• the maximum number of robot representative points may be generated as described before.
• the detection of dynamic obstacle points may start from the first robot representative point (the order may be random).
• the obstacle points that are within the robot safety margin may be obtained. This may be performed by reading the values from the dynamic distance map at the locations of the robot representative points and checking the values against the safety margin.
• the first obstacle point is picked.
• the minimum distance in the static distance map may be obtained at the location of the obstacle point. That minimum distance may be considered as the vibration range of the obstacle point.
• this minimum distance reflects the possible range of motion for vibrating obstacles.
• the vibration range is smaller than a threshold.
• the threshold may be the maximum possible vibration range of a static obstacle. The maximum vibration range of static obstacles may be determined based on experiments. If the vibration range is larger than the threshold, the obstacle point may be classified as a dynamic obstacle point and put into a dynamic point list at step 816. Otherwise, it is checked if the obstacle point has reached the last obstacle point at step 818. If not, the obstacle point index may be incremented by 1 at step 820 and the above process may be repeated.
• the representative point has reached the last representative point. If not, the robot representative point index may be incremented by 1 at step 824 and the above process may be repeated. Otherwise, the dynamic point list may be obtained at step 826. If the list is not empty, it may be used to generate the robot repulsive speed as depicted in Figure 7.
• the classification may be interpreted as follows. If an obstacle point is vibrating within a small range at a fixed position, such as human breathing, it may be perceived as a static obstacle point instead of a dynamic obstacle point, so that no repulsive speed may be generated to move the robot away from the obstacle. To make the robot move according to the patient breathing may cause negative impacts on the stability of a procedure.
• Fig. 9 is an illustrative diagram of an exemplary computer system architecture, in accordance with various embodiments of the present teaching.
• Such a specialized system incorporating the present teaching has a functional block diagram illustration of a hardware platform which includes user interface elements.
• Computer 900 may be a general- purpose computer or a special purpose computer. Both can be used to implement a specialized system for the present teaching.
• Computer 900 may be used to implement any component(s) described herein.
• the present teaching may be implemented on a computer such as computer 900 via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown, for convenience, the computer functions relating to the present teaching as described herein may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load.
• Computer 900 may include communication ports 950 connected to and from a network connected thereto to facilitate data communications.
• Computer 900 also includes a central processing unit (CPU) 920, in the form of one or more processors, for executing program instructions.
• the exemplary computer platform may also include an internal communication bus 910, program storage and data storage of different forms (e.g., disk 970, read only memory (ROM) 930, or random access memory (RAM) 940), for various data files to be processed and/or communicated by computer 900, as well as possibly program instructions to be executed by CPU 920.
• Computer 900 may also include an I/O component 960 supporting input/output flows between the computer and other components therein such as user interface elements 980.
• Computer 900 may also receive programming and data via network communications.
• aspects of the present teaching(s) as outlined above may be embodied in programming.
• Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and/or associated data that is carried on or embodied in a type of machine readable medium.
• Tangible non-transitory “storage” type media include any or all of the memory or other storage for the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide storage at any time for the software programming.
• All or portions of the software may at times be communicated through a network such as the Internet or various other telecommunication networks.
• Such communications may enable loading of the software from one computer or processor into another, for example, from a server or host computer of the robot’s motion planning system into the hardware platform(s) of a computing environment or other system implementing a computing environment or similar functionalities in connection with path planning.
• another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links.
• the physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software.
• terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.
• a machine-readable medium may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium.
• Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, which may be used to implement the system or any of its components as shown in the drawings.
• Volatile storage media include dynamic memory, such as a main memory of such a computer platform.
• Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that form a bus within a computer system.
• Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications.
• RF radio frequency
• IR infrared
• Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data.
• Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a physical processor for execution.

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• 2020
  • 2020-05-26 EP EP20937802.5A patent/EP4157589A4/de active Pending
  • 2020-05-26 CN CN202080041809.2A patent/CN114072254A/zh active Pending
  • 2020-05-26 WO PCT/US2020/034493 patent/WO2021242215A1/en unknown

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