JP2007200070A - Mobile robot - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve an abnormality detection capability by preventing erroneous determination on a slope or the like. <P>SOLUTION: This mobile robot 1 travels in a prescribed environment, a self position detection part 9 detects a self position of the mobile robot 1, and a sensor 11 generates sensed data on a measured object in an environment. A storage part 15 stores a group of sensed data obtained from past traveling as a group of reference data of abnormality decision. A reference data selection part 81 selects as similar reference data reference data correspond to a current self position and resemble current sensed data. An abnormality determination part 83 determines an abnormality in the environment by comparing the sensed data with the similar reference data. The reference data selection part 81 extracts the plurality of reference data corresponding to peripheral positions including the self position from among the group of reference data and selects one most similar to the sensed data from among the plurality of reference data. Similarity is determined on the basis of the number of coincident points where distances to the measured objects coincide with one another. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a mobile robot that travels in a predetermined environment, and more particularly to improvement of abnormality detection performance.

  In recent years, mobile robots that detect surrounding objects while moving along a predetermined route have been provided. This mobile robot is provided with a detection sensor that irradiates various exploration signals such as laser beams, visible rays, ultrasonic waves, infrared rays, etc. in the traveling direction and surroundings of the robot and receives the reflected regression signal from the object as a detection signal. A device that detects an object in the vicinity of a route by the input of the sensor is known.

An example of such a mobile robot is the automatic guided vehicle disclosed in Patent Document 1. This automatic guided vehicle includes a laser distance measuring sensor as a detection sensor. The laser distance sensor scans the laser beam and receives the laser beam reflected from the object surface. The relative position of the object is calculated from the time until light reception and the irradiation direction. The automatic guided vehicle of the same document stores an existing object map in which an existing object is set in advance, and is configured to detect an object in a detection area excluding the existing object as an obstacle. The existing object is prevented from being detected as an obstacle, and obstacles other than the existing object are detected.
Japanese Patent Laid-Open No. 9-6433 (FIG. 1 etc.)

  However, in the configuration in which abnormality detection is performed using the existing object map of the conventional literature, erroneous determination may occur in the following example. Here, as an example, a monitoring mobile robot that detects an abnormality while visiting a predetermined monitoring space is considered. In such a mobile robot, it is required not only to detect an intruding object into the monitoring space but also to perform a lost object determination to detect that an object originally present in the monitoring space has been taken away. However, when the existing object map is used, for example, an error may occur in the lost object determination in the following situation.

  FIG. 1 shows a case where there is a slope on the traveling route of the mobile robot. If there is a slope on the patrol route, the values measured by the laser sensor are different between the middle of the hill, before and after the hill, and on the forward and return passes even at the same location. Another example in which the measured values of the laser sensor are different at the same place is a case where there is a fence such as a wire mesh in the circuit route. A mobile robot scans a laser at a specific period (for example, 30 msec) while moving and measures the same object many times. Therefore, the laser beam is reflected by or passed through a metal mesh due to the tilt of the mobile robot. As a result, the measured values are different even in the same place.

  As in these examples, if the measured value of the laser sensor varies depending on the situation even in the same place, the measurement point obtained at the time of learning is stored in the existing object map. An event that cannot be obtained may occur. When such an event occurs, the mobile robot determines that the original one has been taken away, that is, has disappeared, and makes a false alarm.

  The specific example of FIG. 1 will be described in more detail. FIG. 1A shows a case where the mobile robot 201 reciprocates on a patrol route. The mobile robot 201 detects a horizontal object with a laser sensor. On the path, there is a hill 202 having a height difference larger than the installation height of the laser sensor. In this case, an image of the slope 202 can be seen by the forward mobile robot 201. Therefore, when the mobile robot 201 learns in the forward travel and creates an existing object map using the measurement points measured by the laser sensor, the existing object map is displayed on the route as shown in FIG. In addition to the wall 203 along, the line 204 of the slope 202 will be included.

  However, the line 204 of the slope 202 is visible when the mobile robot 201 travels on the forward path. When the mobile robot 201 travels on the return path, the image of the line 204 on the slope 202 is not visible to the mobile robot 201. Therefore, when the existing object map as described above is used, the mobile robot 201 determines that the originally existing object has been taken away when traveling on the return road, and erroneously performs the lost object determination.

  Another misjudgment factor when using an existing object map is the self-position detection accuracy of the mobile robot. The detection of the self-position involves some variation. Due to the influence of this variation, the position of the object detected by the laser sensor deviates from the existing object on the existing object map, so that the detection accuracy of the intruding object and the disappearing object is lowered, and erroneous determination may occur. It is desirable to avoid such erroneous determination as much as possible.

  The present invention has been made under the above-described background, and an object thereof is to provide a mobile robot that can avoid erroneous determination as in the above-described example and can improve abnormality detection capability.

  The mobile robot of the present invention travels in a predetermined environment by travel means. The mobile robot includes a position detection unit that detects a self-position of the mobile robot in the environment, a detection unit that detects a measurement object in the environment and generates detection data, A storage unit that stores the detection data obtained when traveling as a reference data group for abnormality determination in association with the self-position when each detection data is obtained, and is detected by the position detection unit during current traveling A reference data selection unit that selects reference data similar to the detection data generated by the detection unit as similar reference data from the reference data group of the storage unit, and the current running An abnormality determination unit that determines the abnormality in the environment by comparing the detection data detected in step 1 with the similarity reference data.

  According to the above invention, the reference data corresponding to the current self-position and similar to the current detection data is selected from the reference data group obtained during the past travel, and the selected reference data is used. Abnormality judgment is performed. As a result, even in the situation where the detection result may differ depending on the condition even on the same place such as a slope, past reference data under the same condition is selected and used for abnormality determination. Therefore, erroneous determination can be reduced and abnormality detection accuracy can be improved.

  The reference data selection unit extracts a plurality of reference data acquired at peripheral positions including the self position from the reference data group, and the detection data acquired by the detection unit from the plurality of reference data May be selected as the similar reference data, whereby the reference data corresponding to the current self-position and similar to the detected data is selected. According to such processing, even if the self-position detection varies, the reference data created at the same position as the self-position is selected and used for abnormality determination by the judgment processing based on similarity. Therefore, erroneous determination can be reduced and abnormality detection accuracy can be improved.

  Further, the detection unit scans the environment as the detection data and measures distances at a plurality of measurement points to the object to be measured, and the reference data selection unit uses the reference data and the detection data. The similar reference data may be selected from the reference data group based on the number of measurement points having the same distance to the object to be measured.

  As described above, appropriate reference data similar to the detection data is selected by the process based on the coincidence point. Preferably, the reference data that maximizes the number of matching points is selected as the similar reference data. By comparing the number of matching points, when selecting similar reference data, it is possible to determine the level of similarity of the area excluding abnormal parts such as intruding objects, and to select appropriate reference data to be compared with the detection data .

  A measurement point whose distance to the object to be measured is within a predetermined value in the detection data and the reference data may be determined as a coincidence point.

  Preferably, the angle between the reference data and the detection data is shifted by a predetermined value, and the similarity between the two data is determined. As a result, it is possible to absorb variations caused by the difference in the orientation of the mobile robot when acquiring the reference data and when detecting the detection data in a round, and it is possible to make a more accurate determination.

  The abnormality determination unit determines that an intruding object abnormality has occurred when a distance to the object to be measured in the detection data is shorter than a distance to the object to be measured in the similar reference data. If the distance to the object to be measured is longer than the distance to the object to be measured in the similar reference data, it may be determined that the lost object abnormality has occurred. Thereby, the abnormality of an intruding object and a loss | disappearance object can be determined suitably.

  The mobile robot further includes a travel control unit that controls the travel unit so as to travel on the route in the environment, and the position detection unit travels on the route from a predetermined reference position as the self-position. Distance information may be detected. In this configuration, assuming that the mobile robot travels along a predetermined route, the self-position is represented by the travel distance, and the self-position can be easily detected. In addition, since the self-position is expressed by simple information, other processes using the self-position data can be easily performed.

  The position detection unit may detect information including a position in the environment and a traveling direction of the mobile robot as the self-position. This configuration is suitable for a mobile robot that typically travels while detecting a position by dead reckoning. With this configuration, even when the vehicle does not travel on a predetermined route, it is possible to determine abnormality by appropriately handling the position information.

  As described above, according to the present invention, it is possible to reduce the erroneous determination and improve the abnormality detection capability by performing the abnormality determination after selecting appropriate reference data from the reference data group obtained in the past travel. .

  Hereinafter, a mobile robot according to an embodiment of the present invention will be described with reference to the drawings. The mobile robot of the present embodiment detects abnormalities such as intrusion / disappearance of an object with a laser sensor while traveling around a predetermined route in a monitoring area, and is preferably used as a security robot. When the mobile robot detects an abnormality, the mobile robot sends an abnormality signal together with the captured image to the remote monitoring center. In the monitoring center, when an abnormal signal is received, the received captured image is displayed on the monitor of the monitoring apparatus, and the abnormality is confirmed by the monitoring staff. The monitoring device performs remote control of the mobile robot according to the operation of the monitoring staff.

  FIG. 2 shows a configuration of the mobile robot according to the present embodiment, and FIG. 3 shows a monitoring area that is an environment in which the mobile robot is used and an appearance of the mobile robot.

  As shown in FIG. 2, the mobile robot 1 includes a moving unit 3, a movement control unit 5, a guide detection unit 7, a self-position detection unit 9, a detection unit 11, an intrusion / disappearance determination unit 13, a storage unit 15, and a communication unit 17. The imaging unit 19 and a control unit 21 that controls these units, and a power source unit 23 that supplies power to the units. Each part will be described below.

  The mobile robot 1 has four wheels as shown in FIG. 3B, and two of them, the right wheel 31 and the left wheel 33, function as drive wheels. The moving means 3 includes motors 35 and 37 that independently drive the right wheel 31, the left wheel 33, and the left and right wheels, and the straight traveling speed and the turning traveling speed are controlled by the rotational speeds of the left and right wheels, and the turning direction is also controlled. Is done. The rotation speed of the left and right wheels is controlled by the movement control unit 5. Instead of controlling the rotation independently on the left and right, a method of controlling the turning speed by controlling the steering angle may be adopted. Further, instead of wheel driving, a method of independently controlling the left and right crawlers may be employed.

  The guide detection unit 7 detects guide means on the movement path. The guide means is shown in FIG. As shown in the figure, a white line tape 101 as a guide means is fixedly provided over the entire length of the path on a preset movement path. In addition to the white line tape 101, an indication marker 103 as a point indication means is fixedly provided at a predetermined point in the route. The instruction marker 103 is a white rectangular mark, and is provided at a point such as a boundary of a section set on the movement route.

  The guide detection unit 7 includes a white line detection camera 41 and a road surface information extraction unit 43. The white line detection camera 41 is installed on the bottom surface of the mobile robot 1 so that the road surface can be photographed. The road surface information extraction unit 43 is realized by a computer of the mobile robot 1 and performs image processing. The road surface information extraction unit 43 detects the white line tape 101 and the pointing marker 103 that should guide the route of the mobile robot 1 from the photographed image of the white line detection camera 41 by processing such as edge extraction and Hough conversion, and the control unit 21. Output to.

  In addition, the guide detection part 7 is not limited to said structure. The guide detection unit 7 may be configured by a magnetic sensor, an electromagnetic induction sensor, or the like. In this case, the guide detection unit 7 may be configured to detect a magnetic guide or an electromagnetic induction guide as guide means installed in the movement path. It is preferable that the guide means and the guide detection unit can be selected depending on the installation environment. Further, each point on the route may be recognized by the mobile robot 1 based on position information calculated by dead reckoning or GPS without providing guide means or point instruction means on the route.

  The movement control unit 5 is a means for controlling the drive motors 35 and 37. The movement control unit 5 controls the drive motors 35 and 37 to move along the white line tape 101 by, for example, well-known PID control according to the detection output of the white line tape 101 by the guide detection unit 7. Moreover, the movement control part 5 controls a moving speed based on the preset route information according to the detection of the travel section. The moving speed is preset for each section and stored as a part of the route information. In addition, the movement control unit 5 temporarily stops traveling according to the route information in response to detection of the specific point.

  The self-position detection unit 9 includes resolvers 51 and 53 and a position calculation unit 55 and functions as a position detection unit. The resolvers 51 and 53 are installed in the motors 35 and 37, respectively, and detect the absolute position of the motor rotation shaft of the motors 35 and 37. The resolvers 51 and 53 are one form of the rotation amount detection unit. The position calculation unit 55 is realized by a computer of the mobile robot 1, calculates the rotation amounts of the left and right wheels 31 and 33 from the rotation amount of the motor rotation shaft obtained from the resolver output, and calculates the rotation amount and wheel radius of each of the left and right wheels. The travel distance of the mobile robot 1 is calculated by calculating the average of the calculated travel distances of the left and right wheels. In the present embodiment, since the travel route serving as the cyclic route is fixed, the travel distance can be set as the self-position. The travel distance may be recorded as the total distance from the tour start point or the distance from the position of each instruction marker 103.

  In addition, the position calculation unit 55 counts the number of detections of the indication marker 103 according to the detection output of the indication marker 103 by the guide detection unit 7, detects the current travel section based on the route information, and corrects the self position. And so on.

  The detection unit 11 is a unit that operates when the mobile robot 1 starts patrol and detects objects around the mobile robot 1. The detection unit 11 includes a laser sensor 61 installed toward the front of the mobile robot 1 and receives reflected light when the laser light emitted from the laser oscillator is reflected by an object on the optical path. The detection unit 11 controls the irradiation direction of the laser light emitted from the laser oscillator by the scanning mirror and the means for rotationally driving the scanning mirror, so that a predetermined range including the front of the mobile robot 1 is set to a period of, for example, 30 msec. Spatial scanning with.

  The detection unit 11 then calculates the distance between the detection unit 11 calculated from the time from laser light irradiation to reflected light detection and the object (measurement point) reflecting the laser light, and the angle of the scanning mirror that is rotationally driven. Thus, the relative position of the object reflecting the laser beam, that is, the measurement point reflecting the laser beam is calculated. The relative position is the position of the measurement point with respect to the mobile robot 1. Further, when the reflected wave does not return within a predetermined time, the detection unit 11 determines that there is no object within the distance that can be irradiated with the laser light, and records predetermined pseudo data as a relative position. The pseudo data may be a predetermined value or an appropriate value larger than the effective measurement distance of the laser sensor 61.

  In the present embodiment, measurement data obtained by the detection unit 11 is referred to as distance measurement data. Ranging data is a form of detection data of the present invention.

  The storage unit 15 stores information used for various processes in the mobile robot 1. The information stored in the storage unit 15 includes route information 71 indicating information on a moving route, a reference distance measurement data group 73 that is a set of measurement results of the detection unit 11, and a mobile robot calculated by the position calculation unit 55. 1 current position information (self-position) 75 is included.

  As the route information 71, in correspondence with the number of each section (section from one pointing marker to the next pointing marker) in the moving path, the section distance measured in advance and the difference in azimuth between the start point and end point of the section (angle) Difference), the traveling speed of each section, the operation of the robot on each indication marker, and the like are stored. In the route information, the section number is associated with the number of markers (number of detections) to be detected by the corresponding section. In the traveling control, the section number is specified from the number of marker detections, and the traveling speed and the operation of the robot on the marker are specified by referring to the route information from the section number.

  The reference distance measurement data group 73 is a set of reference distance measurement data measured by the laser sensor 61 by traveling on a route in the monitoring area. Each distance measurement data obtained for each predetermined distance on the route during past driving is the reference distance measurement data, and a set of reference distance measurement data obtained over the entire route is a reference distance measurement data group. is there. Each reference distance measurement data is stored in association with position information of the mobile robot 1 when the data is obtained. At this time, in addition to the position information when the data is obtained, it may be associated with the marker section, the course number of the route to be visited, and the like. The reference distance measurement data is used as comparison reference data in an abnormality determination process, which will be described later, and is compared with distance measurement data of the laser sensor 61 obtained in the current travel. The distance measurement data is a form of the detection data of the present invention as described above, whereas the reference distance measurement data is a form of the reference data of the present invention.

  As described above, the reference distance measurement data is distance measurement data in the past travel. In this regard, the reference distance measurement data is obtained when the mobile robot 1 travels for learning purposes before traveling monitoring. May be generated. Further, the reference distance measurement data may be measurement data in the previous patrol monitoring. In either case, the reference distance measurement data can be said to be data obtained in a past learning run.

  Further, the distance measurement data and the reference distance measurement data are specifically the result of measuring a predetermined range in front at predetermined angular intervals by one scan of the laser sensor 61. For example, when distance measurement data is acquired in a range of 180 degrees at intervals of 0.5 degrees, 361 distance values are obtained. A set of these 361 distances becomes one distance measurement data. The distance measurement data may be a table of angles and distances. Then, the distance measurement data is stored as learning data and handled as reference distance measurement data.

  The position information 75 is self-position information of the mobile robot 1 calculated by the position calculation unit 55 of the self-position detection unit 9, and specifically, the travel distance is position information as described above.

  The intrusion / disappearance determination unit 13 detects abnormalities based on the distance measurement data obtained by the detection unit 11, the self-position information calculated by the position calculation unit 9, and the reference distance measurement data group 73 stored in the storage unit 15. Determine. The intrusion / disappearance determination unit 13 includes a reference data selection unit 81 and an abnormality determination unit 83, and the abnormality determination unit 83 includes an evaluation value calculation unit 85. The reference data selection unit 81 selects, from the reference distance measurement data group, the reference distance measurement data corresponding to the current self-position and most similar to the current distance measurement data as the similar reference distance measurement data. Here, the reference distance measurement data corresponding to the current self-position is reference distance measurement data acquired at a position around the self-position including the self-position. In addition, it is good also as the reference | standard ranging data memorize | stored corresponding to the reference | standard ranging data acquired in the marker area in which a self position is included, or the course number of the path | route which self is traveling. The abnormality determination unit 83 compares the data selected by the reference data selection unit 81 with the current distance measurement data to determine whether there is an intrusion or disappearance abnormality. In the abnormality determination unit 83, the evaluation value calculation unit 85 compares the data selected by the reference data selection unit 81 with the current distance measurement data, and determines an evaluation value for determining the presence or absence of an object based on the comparison result. When the evaluation value exceeds the predetermined value, the abnormality determination unit 83 determines that an intruder exists and an abnormality has occurred. In addition, the abnormality determination unit 83 similarly determines a lost object. The processing of the intrusion / disappearance determination unit 13 will be described in detail later. When an abnormality is determined, an abnormality signal is output from the communication unit 17, and the movement control unit 5 is controlled by the control unit 21 to determine movement stoppage or deceleration, avoidance or tracking of an intruding object or obstacle, and the like. Process.

  The imaging unit 19 is a means that is mounted on the mobile robot 1 and images the surroundings. For example, as shown in FIG. 3B, the imaging unit 19 includes six cameras housed in a housing above the robot. These cameras are pointed in different directions, ensuring a 360 degree field of view in the horizontal direction. The image of the imaging unit 19 is buffered in the storage unit 15 and sent from the communication unit 17 to the monitoring center.

  The communication unit 17 is a wireless communication unit that transmits and receives signals to and from a remote monitoring center. When the intrusion / disappearance determination unit 13 detects an intrusion or disappearance abnormality, the communication unit 17 outputs an abnormality signal to a remote monitoring center by wireless or the like. The communication unit 17 transmits the image captured by the imaging unit 19 and the self-position of the mobile robot 1 detected by the self-position detection unit 9 to the remote monitoring center, and receives the control command received from the monitoring center to the control unit 21. input.

  The control unit 21 is a means for controlling the configuration of each part of the mobile robot 1 described above, and is configured by a computer including a CPU and the like. In addition, what can be computer-processed by each part structure mentioned above may be implement | achieved by the same computer. For example, the road surface information extraction unit 43, the position calculation unit 55, the intrusion / disappearance determination unit 13, the movement control unit 5, and the like may be realized by the same computer. The storage unit 15 may be realized by a memory of the computer, an external storage device, or the like.

  Next, the operation of the mobile robot 1 according to the present embodiment will be described. Here, operations relating to abnormality determination will be mainly described. As an outline, the mobile robot 1 stores the distance measurement data obtained in the past learning run as a reference distance measurement data group, and the distance measurement data obtained at the time of patrol and the stored reference distance measurement data group. An abnormality is determined by comparison. The past learning run for acquiring the reference distance measurement data group may be a run for the purpose of learning alone or a patrol monitoring run performed in the past. In the following, the operation of the mobile robot 1 will be described by dividing it into an operation for obtaining the reference distance measurement data and an operation at the time of patrol using it.

  FIG. 4 shows a flowchart of the operation when acquiring the reference distance measurement data group. The process of FIG. 4 is performed by each component configuration of the mobile robot 1 including the control unit 21 under the control of the control unit 21. As shown in the figure, the mobile robot 1 initializes the robot travel distance (S1), starts the travel of the mobile robot 1 according to the patrol route (S2), and records the current travel distance as P1 (S3). Hereinafter, the travel distance is treated as the self position of the mobile robot 1.

  Next, the mobile robot 1 determines whether or not the scanning result of the laser sensor 61 is input to the detection unit 11 (S4). If the distance measurement data is not input (S4, No), the mobile robot 1 proceeds to step S9.

  The distance measurement data is obtained by scanning the laser sensor 61. The distance measurement data is obtained as a result of the laser sensor 61 scanning the front predetermined range W ° at a predetermined angular interval S °, and D (n) (n = 0, 1, 2, 3... N). expressed. D (n) is a distance to the object to be measured, and n is a number sequentially given in the irradiation direction every S °, and N = W / S. If ranging data is input (S4, Yes), the mobile robot 1 acquires the current travel distance from the self-position detection unit 9 and records it as P2 (S5). Further, the mobile robot 1 compares the travel distance P1 acquired in the previous measurement cycle of the laser sensor 61 with the current travel distance P2, and determines whether the mobile robot 1 has traveled a predetermined distance or more (S6). If the distance has not been advanced (S6, No), the process returns to step S4.

  When the mobile robot 1 has traveled a predetermined distance or more from the travel distance P1 to P2 (S6, Yes), the value of the travel distance P1 is replaced with the current travel distance P2 (S7), along with the travel distance, section number, etc. The distance measurement data (D (n) (n = 1, 2, 3... N) of the laser sensor 61 is stored (S8) The distance measurement data is further stored together with other information such as a traveling course number. It's okay.

  Next, the mobile robot 1 determines whether or not the patrol of all routes has been completed (S9). If the patrol route has not ended, the process returns to step S4. If the patrol route has ended, the mobile robot 1 ends the process. The determination in step S9 is made based on whether or not the route end point indicating marker 103 is detected.

  In the above, the suitable example of the process which acquires reference | standard ranging data was demonstrated. In the above example, the self-position of the mobile robot 1 is specified by the travel distance using the fact that the movement route is determined in advance. However, the present invention is not limited to this. The self position may be data of the position and the traveling direction of the mobile robot 1 acquired by dead reckoning or the like (the position may be coordinates on the map). As described above, the difference from the case where the travel distance is used as the self-position is only to replace “travel distance” with “position and traveling direction” in the above flow. Is similarly expressed in position and direction.

  In the case of the above modification, more specifically, the current position (X, Y) and posture (robot direction θ) of the robot may be calculated as a method of detecting the self position. In this embodiment, since the movement is controlled by the rotational speeds of both wheels, the position detection process obtains the travel distance and the angle change from the wheel rotation amount, and captures the position and posture at each time point from these information. Can do. At this time, the angle change is calculated from the difference between the rotation amounts of both wheels and the wheel interval. This is a technique generally known as dead reckoning.

  Next, the operation of the mobile robot 1 during a tour will be described. FIG. 5 is a flowchart showing the operation of the mobile robot 1 during patrol. The processing in FIG. 5 is performed by each component configuration of the mobile robot 1 including the control unit 21 under the control of the control unit 21.

  In FIG. 5, when starting the tour, the mobile robot 1 initializes the robot travel distance (S20) and starts traveling according to the tour route (S21). The mobile robot 1 determines whether or not the distance measurement data D (n) of the laser sensor 61 is input to the detection unit 11 (S22). If no distance measurement data is input (S22, No), the process proceeds to step S31.

  If distance measurement data has been input (S22, Yes), the mobile robot 1 acquires the travel distance P of the mobile robot 1 from the self-position detector 9 (S23). As described above, in the present embodiment, the travel distance P is processed as the self-position. Then, the reference data selection unit 81 of the mobile robot 1 reads out a predetermined number of reference distance measurement data obtained in a predetermined range around the current travel distance P from the reference distance measurement data group 73 of the storage unit 15 to the buffer. (S24).

  Here, the reference distance measurement data is Di (n). “I” is a number assigned in the order of obtaining the reference distance measurement data. The reference distance measurement data acquired in the past with the current travel distance P is defined as Dp (n). The reference data selection unit 81 is a distance measurement data in the vicinity of the reference distance measurement data Dp (n), specifically, k reference distance data Di (n) (i = pk,. ..., P-1, p, p + 1,..., P + k) are read into the buffer.

  Next, the reference data selection unit 81 selects the reference distance measurement data most similar to the current distance measurement data as the similar reference distance measurement data from among the plurality of data read out to the buffer (S25). The similar reference distance measurement data corresponds to the similar reference distance data of the present invention. The selection process of the similar reference distance measurement data will be described later.

  Next, the abnormality determination unit 83 of the mobile robot 1 compares the similar reference distance measurement data selected from the reference distance measurement data group 73 in step S25 with the current distance measurement data and performs abnormality determination (S26 to S26). S30). Here, first, the mobile robot 1 calculates the difference between the similar reference distance measurement data selected from the reference distance measurement data group and the current distance measurement data (S26), and determines the intruding object using the difference data. Processing is performed (S27). In the intruding object determination process, an evaluation value related to the intruding object is calculated. Then, the abnormality determination unit 83 determines the presence or absence of an intruding object from the evaluation value obtained as a result of performing the intruding object determination process in step S27 (S28). If the evaluation value is greater than or equal to a predetermined determination reference value for intrusion determination, it is determined that there is an intruding object, and if the evaluation value is less than the determination reference value, it is determined that there is no intruding object. If there is an intruding object (S28, Yes), the movement of the mobile robot 1 is stopped (S32), and an abnormality alarm is output (S33). Details of the intruding object determination will be described later.

  If there is no intruding object (S28, No), the abnormality determination unit 83 performs a lost object determination process using the difference data calculated in step S26 (S29). Here, an evaluation value related to the lost object is calculated. The abnormality determination unit 83 determines the presence or absence of a lost object from the evaluation value obtained as a result of performing the lost object determination process in step S29 (S30). If the evaluation value is equal to or greater than the predetermined determination reference value for disappearance determination, it is determined that the object disappearance has occurred. If the evaluation value is less than the determination reference value, it is determined that no object disappearance has occurred. If there is a lost object (S30, Yes), the movement of the mobile robot 1 is stopped (S32), and an abnormality alarm is output (S33). Details of the lost object determination will be described later.

  If there is no disappearing object (S30, No), the mobile robot 1 determines whether or not the patrol of all routes has been completed (S31). If the patrol route is not completed, the process returns to step S22. If the patrol route is completed, the mobile robot 1 terminates the process. The determination in step S31 is made based on whether or not the route end point indicating marker 103 is detected.

  FIG. 6 shows a conceptual diagram of the operation during the above-mentioned patrol. As already described with reference to FIG. 5, the self-position of the mobile robot 1 is represented by the travel distance P. Assume that distance measurement data D (n) is obtained at the travel distance P that is the current position. The reference data selection unit 81 takes out the reference distance measurement data obtained before and after the travel distance P from the reference distance measurement data group 73 of the storage unit 15. As shown in the drawing, 2k + 1 pieces of reference distance measurement data are extracted with reference to the distance measurement data Dp (n) obtained at the travel distance P. Then, the reference data selection unit 81 compares the extracted 2k + 1 reference distance measurement data with the current distance measurement data D (n), and extracts the most similar reference distance measurement data as similar reference distance measurement data. Then, the abnormality determination unit 83 compares the extracted similar reference distance measurement data with the current distance measurement data, and determines intrusion and disappearance.

  Next, a process in which the reference data selection unit 81 selects similar reference distance measurement data will be described. Here, from the 2k + 1 reference distance measurement data Di (n) (i = pk,..., P-1, p, p + 1,..., P + k), the current distance measurement data D (n The most similar data is selected. The reference data selection unit 81 compares each distance measurement data Di (n) with the distance measurement data D (n) and selects the reference distance measurement data Di (n) having the highest similarity.

  In the present embodiment, the number of matching points is used as the similarity. The coincidence point is a measurement point where the distance to the object to be measured coincides with the reference distance measurement data Di (n) and the distance measurement data D (n).

  More specifically, in the example of the present embodiment, the distance measurement data D (n) (n = 0 to N) is a set of distances measured at N angles at intervals of 0.5 degrees. The distance at each angle is called an “angle component”. A measurement point where the difference between the angle components of the distance measurement data and the reference distance measurement data is equal to or smaller than a predetermined threshold is called a coincidence point, and a measurement point where the difference between the angle components exceeds the threshold is called a disagreement point. The similarity is represented by the number of matching points.

  FIG. 7 shows the matching points and the mismatching points. In FIG. 7, the horizontal axis is an angle, and the distance difference on the vertical axis is a difference in angle components (difference between distance measurement data and reference distance measurement data at each angle). If the distance difference is within the threshold, the corresponding measurement point is a coincidence point (black mark). If the distance difference exceeds the threshold value, the corresponding measurement point is a mismatch point (white mark).

  The similarity of this embodiment is advantageous in the following points. In FIG. 7, the mismatch point is highly likely to be a point where an intruding object or a disappearing object is measured. If the similarity including such abnormal object data is calculated, the similarity may not be properly evaluated. On the other hand, in the present embodiment, since the measurement point of the abnormal object is excluded, a suitable similarity evaluation can be performed.

  Further, the scanning range of the laser sensor 61 is wide, and is 180 ° in the example of the present embodiment. A change in distance due to the presence of an intruding object or the like appears only in a part of the scanning range. Therefore, the similarity can be appropriately evaluated even when the distance measurement data other than the portion (angle component) where the change is caused by the intruding object or the like as described above is compared.

  The reference data selection unit 81 of the present embodiment further absorbs the angle error of the distance measurement data by the following processing. As shown in FIG. 8, the reference data selection unit 81 shifts each reference distance measurement data not only between the measurement points of the same angle, but also slightly compares each distance measurement data with each reference distance measurement data. As the similarity, the highest similarity (number of coincidence points) is adopted, thereby absorbing the angular deviation caused by the rotation direction of the mobile robot 1 or the like. In the example of FIG. 8, the point of the reference distance measurement data corresponding to the point A of the current distance measurement data is Q3. However, the reference distance measurement data is shifted in the angular direction, and the point A is compared with the points Q2 and Q1. Thus, the angular deviation is absorbed.

  However, when considering the deviation in the angular direction as described above, it is necessary to reduce the number of data, and a range obtained by removing both end portions from the distance measurement data is used. In the example of the present embodiment, the scanning range is 180 degrees. If the reference distance measurement data and the distance measurement data are shifted within a range of ± 15 degrees, the data for 150 degrees are compared. From the distance measurement data, the data for 15 degrees on the left and right are removed, and the data for the center 150 degrees are left. Data for 150 degrees is also used from the reference distance measurement data. Then, the reference distance measurement data is shifted a little by 15 degrees on both sides little by little. When measurement is performed at intervals of 0.5 degrees as in the example of the present embodiment, data is shifted little by 30 on both sides, and the matching points are counted at each shift position to obtain the similarity. Therefore, the maximum similarity is adopted as the similarity of each reference distance measurement data. Then, one reference ranging data having the highest similarity among the plurality of reference ranging data is extracted as similar reference ranging data.

  In addition, when the above processing is performed, there may be a plurality of reference distance measurement data in which the number of matching points is maximized. In this case, any one reference distance measurement data may be selected. More preferably, the most similar reference distance measurement data is selected from the plurality of reference distance measurement data. For example, the reference data selection unit 81 obtains the sum of absolute values of the distance difference at each matching point in each reference distance measurement data, and selects the reference distance measurement data that minimizes the sum. By such a process, when a plurality of candidates are selected by the determination using the number of matching points, it is possible to select one more appropriate candidate.

  The selection process of the similar reference distance measurement data is summarized as follows.

1): Ranging data D (n) at the travel distance P is obtained.

2): 2k + 1 reference distance data Di (n) before and after the reference distance data Dp (n) at the travel distance P (i = pk,... P-1, p, p + 1,. , P + k) is read from the storage unit 15.

3): The number of coincidence points is calculated as the similarity between the distance measurement data D (n) and each reference distance measurement data Di (n), and the reference distance measurement data having the maximum similarity is selected as the similarity reference distance measurement data. To do. At this time, each reference distance measurement data Di (n) is slightly shifted in the angular direction within a predetermined range, and the maximum number of matching points is adopted as the similarity. The coincidence point is a measurement point that satisfies the following expression.
−Th ≦ (Di (n−j) −D (n)) ≦ Th
Here, Th is a threshold value. Further, j indicates the shift amount in the angular direction.

4): When there is a plurality of reference distance measurement data with the maximum similarity, the sum of absolute differences (Σ | Di (n−j) −D (n) |) at the coincidence points is compared, and this sum The reference distance measurement data that minimizes is finally selected as similar reference distance measurement data.

5): The selected similar reference distance measurement data is compared with the distance measurement data D (n), and abnormality determination is performed. At this time, the selected similar reference distance measurement data is held in a state shifted in the angular direction so that the number of matching points is maximized in the process of 3), and the abnormality determination process is performed in the state of being shifted. .

  The selection process of the similar reference distance measurement data has been described above. Next, the intruding object determination process in step S27 and the lost object determination process in step S29 will be described with reference to FIGS. In these processes, the evaluation values for the intrusion object determination and the disappearance object determination are calculated by the abnormality determination unit 83 of the intrusion / disappearance determination unit 13. FIG. 9 is a flowchart of the intruding object determination process, FIG. 10 is a flowchart of the lost object determination process, and FIG. 11 is a conceptual diagram of both determination processes.

  In FIG. 9, the abnormality determination unit 83 calculates a change point where the current distance measurement data is closer than the selected distance measurement data by a predetermined distance or more based on the calculation result of the difference between the current distance measurement data and the similar reference distance measurement data. Existence is checked (S200). In FIG. 11, the change point is a point where the distance difference is a negative value and is −Δ or less (a point where the distance difference is − or more on the − side).

  If there is a change point, the abnormality determining unit 83 examines the continuous section and labels the continuous area Ga (i) (S202). Here, since the angular interval at the time of scanning with the laser sensor 61 is sufficiently dense as compared with the detection target object (for example, a person), discontinuous change points (isolated points) may be removed as noise.

  Next, the evaluation value calculation unit 85 of the abnormality determination unit 83 calculates an evaluation value (S204). Here, the number of measurement points included in the label is the evaluation value Ea (i) of the region Ga (i). In the example of FIG. 11, since Ga (i) includes three measurement points, the evaluation value Ea (i) = 3.

  In the evaluation value calculation, not only the number of measurement points is simply used as the evaluation value, but the greater the difference between the distance measurement data and the similar reference distance measurement data, the greater the probability of abnormality, so the difference (absolute value) The cumulative value may be used as the evaluation value Ea (i) after a weight proportional to is applied.

  When the evaluation value is calculated, the abnormality determination unit 83 stores the evaluation value of the label in the storage unit 15 together with the angle and distance difference of the measurement points included in the label (S206). Then, the abnormality determination unit 83 compares the processing results of the previous period and the current period, and determines whether or not there is a tracking target (S208). In tracking, the movement distance of the robot is taken into consideration, and whether or not there is a tracking target is determined by whether or not there are objects of substantially the same size within a predetermined angle and distance range between the previous cycle and the current cycle. If there is a corresponding object, that object becomes the tracking target. If there is no tracking target (S208, No), the intruding object determination process ends.

  When the tracking target exists (S208, Yes), the abnormality determination unit 81 cumulatively adds the evaluation value detected in the current cycle to the evaluation value of the area detected in the previous cycle, and obtains a new evaluation value Ea (i). (S210). As a result, the evaluation value is updated, and the intruding object determination process ends. Then, in step S28 of FIG. 5, the presence or absence of an intruding object is determined depending on whether or not the evaluation value is equal to or greater than a predetermined value.

  In the present embodiment, since the travel distance is acquired as the self-position of the mobile robot 1, if the patrol route is not a straight line, an accurate position cannot be acquired, and thus accurate tracking cannot be performed strictly. However, in tracking, it is sufficient if the same object can be correctly determined. From this viewpoint, the traveling course can be approximated to a straight line by shortening the scanning interval (cycle), and tracking can be performed with sufficient accuracy.

  Further, as already described, in the present embodiment, the current position coordinates on the two-dimensional map may be obtained by dead reckoning or the like. In this case, by projecting the distance measurement data on the two-dimensional map, an object detected at a nearby position on the two-dimensional map in the previous cycle and the current cycle may be set as the tracking target.

  Next, lost object determination will be described. In FIG. 10, the abnormal object determination unit 83 determines that the distance measurement data is farther than the similar reference distance measurement data by a predetermined distance or more based on the difference between the distance measurement data and the similar reference distance measurement data. Whether or not there is is determined (S300). In the example of FIG. 11, the change point is a point where the distance difference is + Δ or more (a point where the distance difference is Δ or more on the + side).

  If there is a change point, the abnormality determination unit 83 examines the continuous section, and labels the continuous area as Gd (j) (S302). Similar to the intruding object determination process, the angular intervals when scanning with the laser sensor 61 are sufficiently dense compared to the detection target object, so that the discontinuous change points (isolated points) may be removed as noise.

  Next, the evaluation value calculation unit 85 of the abnormality determination unit 83 calculates an evaluation value (S304). Here, the number of measurement points constituting the extraction region Gd (j) is defined as the evaluation value Ed (j) of the region Gd (j). In the example of FIG. 11, since Gd (j) includes three measurement points, Ed (j) = 3. Similar to the intruding object determination processing, in the evaluation value calculation, not only the number of measurement points is simply set as the evaluation value, but the greater the difference between the distance measurement data and the similar reference distance measurement data, the more likely the abnormality is. Therefore, the accumulated value may be used as the evaluation value Ed (j) after a weight proportional to the difference (absolute value) is applied.

  When the evaluation value is calculated, the abnormality determination unit 83 records the label evaluation value in the storage unit 15 together with the angle and distance difference of the measurement points included in the detected label (S306). Then, the abnormality determination unit 83 compares the processing results of the previous period and the current period, and determines whether there is a tracking target (S308). The determination criteria are the same as intruding object determination. If there is no tracking target (S308, No), the lost object determination process ends.

  When the tracking target exists (S308, Yes), the abnormality determination unit 83 further determines whether the tracking target (extraction region Gd (j)) has moved from the previous cycle (S310). This process is for determining whether the tracking target is a lost object or if there is a moving object in the monitoring area when the reference distance measurement data group is acquired.

  When the tracking target is not moving (S310, No), the abnormality determination unit 83 cumulatively adds the evaluation value detected in the current cycle to the evaluation value of the region detected in the previous cycle, and obtains a new evaluation value Ed (j ) (S212). As a result, the evaluation value is updated, and the lost object determination process ends. Then, in step S30 of FIG. 5, the presence or absence of a lost object is determined depending on whether or not the evaluation value is equal to or greater than a predetermined value.

  If it is determined that the tracking target is moving (S310, Yes), the abnormality determining unit 83 initializes the evaluation value (S314). This is due to the following reason. Originally, if the tracking target is a lost object, the tracking target should not move. The movement of the tracking target can occur when there is a moving object in the monitoring area when the reference distance measurement data group is acquired. That is, when there is a moving object when acquiring the reference distance measurement data group, if the object does not exist at the time of patrol, an area Gd (j) that is farther than the reference distance measurement data is observed as the movement area. Will be. On the other hand, if the object has disappeared, the position of the tracked object should not change. Therefore, when it is determined that the tracking target region Gd (j) is moving, the abnormality determination unit 83 initializes the evaluation value of the region Gd (j) as a non-disappearing object, thereby further accurately. Judgment becomes possible.

  The operation of the mobile robot 1 has been described above. Next, a specific example in which the mobile robot 1 can avoid erroneous determination will be described. Here, the example of the slope of FIG. 1 is used. Considering before and after the hill in FIG. 1, the hill is measured as an object on the forward path, but the hill is not measured on the return path. If an abnormality determination is to be performed simply using a map of an existing object, the existing object is not detected on the return path, and as a result, an erroneous determination is made that the object has disappeared.

  On the other hand, in the present embodiment, in the place as shown in FIG. 1, different reference distance measurement data is acquired for both the outward path and the return path, and is stored in association with the travel distance information at the time of acquisition. . At this time, the reference distance measurement data for the forward path includes a slope as an object, but the reference distance measurement data for the return path does not include a slope. At the time of patrol, the distance measurement data on the return path is compared with the reference distance measurement data on the return path that does not include the hill because the reference distance measurement data is stored in association with the travel distance information, and abnormality determination is performed. Is called. As a result, it is possible to avoid a situation in which a slope is erroneously determined as a lost object.

  The preferred embodiments of the present invention have been described above. As described above, according to the present invention, the reference data corresponding to the current self-position and similar to the current detection data is selected from the reference data group obtained during past travel, and the selected reference is selected. Anomaly judgment is performed using data. Thus, even in the same place such as a hill or the like, even in a situation where the detection result may differ depending on the condition, the past reference data under the same condition is selected and used for abnormality determination. Therefore, erroneous determination can be reduced and abnormality detection accuracy can be improved.

  This point will be further described. As described in the above example, in an environment with a height difference such as a slope or an environment with an object such as a wire mesh, the measured values of the detection unit such as a laser sensor can be obtained differently even in the same place. Sometimes. If the abnormality determination is simply performed using the map of the existing object as in the prior art, the existing object is not detected, and as a result, there is a possibility that an erroneous determination that the object has disappeared is made. On the other hand, according to the present invention, determination can be performed using past appropriate reference data, and erroneous determination of a lost object can be avoided. In this way, the present invention can detect an intruding object into the monitoring area with high accuracy, and can detect a lost object with high accuracy even in an environment with a height difference such as a slope.

  Further, according to the present invention, when selecting similar reference data corresponding to a self-position, a plurality of reference data corresponding to peripheral positions including the self-position are extracted from the reference data group, and the plurality of reference data Among them, the one most similar to the detection data acquired by the detection unit is selected. Thereby, the reference data corresponding to the current self-position and similar to the detection data is preferably selected. By such processing, even if self-position detection varies, reference data created at the same position as the self-position is appropriately selected and used for abnormality determination by determination processing based on similarity. Therefore, erroneous determination can be reduced and abnormality detection accuracy can be improved.

  Further, according to the present invention, distance data to the object to be measured is generated by scanning the environment as detection data. Then, similar reference data is selected from the reference data group based on the number of points where the distance to the object to be measured matches between the reference data and the detection data. By the process based on the coincidence point, appropriate reference data can be selected as described above. Preferably, the reference data that maximizes the number of matching points is selected as the similar reference data. By comparing the number of coincidence points, it is possible to determine the level of similarity of a region excluding an abnormal part such as an intruding object, and to select appropriate reference data to be compared with detection data. Note that, as described above, a measurement point whose distance to the object to be measured is within a predetermined value between the detection data and the reference data may be determined as a coincidence point.

  In the above-described embodiment, the angle between the reference data and the detection data is shifted by a predetermined value, and the similarity between the two data is determined. Thus, when the reference data is acquired and when the detection data is acquired in a round Variations in the direction of the mobile robot can be absorbed, and more accurate determination can be made.

  Further, the present invention determines that an intruding object abnormality has occurred when the distance to the measurement object in the detection data is shorter than the similar reference data, and the distance to the measurement object in the detection data is compared with the similar reference data. If it is far away, it is determined that the disappeared object abnormality has occurred, so that the abnormality of the intruding object and the disappearing object can be suitably determined.

  In the present invention, the traveling means is controlled to travel on a route in the environment. Then, travel distance information on a route from a predetermined reference position is detected as the mobile robot's own position. In the above example, the reference position is a route start point or an instruction marker. As described above, on the assumption that the mobile robot travels along a predetermined route, the self-position is represented by the travel distance, and thus the self-position can be easily detected. In addition, since the self-position is expressed by simple information, other processes using the self-position data can be easily performed.

  In another example, the present invention may detect information including the position in the environment and the traveling direction of the robot as the self-position of the mobile robot. This self-position is typically obtained by dead reckoning running. By using such a self-position, it is possible to determine abnormality by appropriately handling position information even when the vehicle does not travel on a predetermined route.

  The preferred embodiments of the present invention have been described above. However, the present invention is not limited to the above-described embodiments, and it goes without saying that those skilled in the art can modify the above-described embodiments within the scope of the present invention.

  As described above, the mobile robot according to the present invention can detect abnormalities in the monitoring area and is useful as a security robot or the like.

It is a figure which shows the abnormality determination process of a mobile robot when there exists a slope on a path | route in a prior art. It is a block diagram which shows the structure of the mobile robot in embodiment of this invention. It is a figure which shows the environment where a mobile robot is used, and the external appearance of a mobile robot. It is a flowchart which shows operation | movement of the mobile robot at the time of reference | standard ranging data group acquisition. It is a flowchart which shows operation | movement of the mobile robot at the time of patrol. It is a figure which shows notionally the operation | movement of the mobile robot at the time of patrol. It is a figure which shows the process which calculates | requires the similarity degree of ranging data and reference | standard ranging data. It is a figure which shows the process which calculates | requires the similarity degree of ranging data and reference | standard ranging data. It is a flowchart of an intruding object determination process. It is a flowchart of a lost object determination process. It is a figure which shows an intruding object determination process and a loss | disappearance object determination process notionally.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Mobile robot 3 Moving means 5 Movement control part 7 Guide detection part 9 Self-position detection part 11 Detection part 13 Intrusion / disappearance determination part 15 Storage part 17 Communication part 19 Imaging unit 21 Control part 31 Right wheel 33 Left wheel 35, 37 Motor 41 White line detection camera 43 Road surface information extraction unit 51, 53 Resolver 55 Position calculation unit 61 Laser sensor 71 Path information 73 Reference distance measurement data group 75 Position information 81 Reference data selection unit 83 Abnormality determination unit 85 Evaluation value calculation unit 101 White line tape 103 Instruction Marker

Claims (6)

A mobile robot that travels within a predetermined environment by traveling means,
A position detection unit for detecting a self-position of the mobile robot in the environment;
A detection unit that detects a measurement object in the environment and generates detection data;
A storage unit that stores the detection data obtained when traveling in the environment in the past as a reference data group for abnormality determination in association with the self-position when each detection data is obtained,
Reference data similar to the detection data generated by the detection unit corresponding to the self position detected by the position detection unit in the current travel is used as similar reference data from the reference data group of the storage unit. A reference data selection section to select;
An abnormality determination unit for determining an abnormality in the environment by comparing the detection data detected in the current travel with the similar reference data;
A mobile robot characterized by comprising:   The reference data selection unit extracts a plurality of reference data acquired at peripheral positions including the self position from the reference data group, and the detection data acquired by the detection unit from the plurality of reference data The mobile robot according to claim 1, wherein the most similar data is selected as similarity reference data. The detection unit, as the detection data, scans the environment and measures distances at a plurality of measurement points to the object to be measured.
The reference data selection unit selects the similar reference data from the reference data group based on the number of measurement points having the same distance to the object to be measured in the reference data and the detection data. The mobile robot according to claim 1 or 2.   The abnormality determination unit determines that an intruding object abnormality has occurred when a distance to the object to be measured in the detection data is shorter than a distance to the object to be measured in the similar reference data, and the object to be measured in the detection data The mobile robot according to claim 3, wherein it is determined that a lost object abnormality has occurred when a distance to an object is longer than a distance to an object to be measured in the similar reference data. Furthermore, a travel control unit that controls the travel means to travel on the route in the environment,
The mobile robot according to claim 1, wherein the position detection unit detects travel distance information on the route from a predetermined reference position as the self position.   The mobile robot according to claim 1, wherein the position detection unit detects information including a position in the environment and a traveling direction of the mobile robot as the self-position.

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