JP5247494B2 - Autonomous mobile device - Google Patents

Description

  The present invention relates to an autonomous mobile device that autonomously moves while detecting an obstacle and avoiding a collision with the obstacle, and relates to an autonomous mobile device that performs self-diagnosis related to an obstacle detection function.

  Traditionally, in environments where there are moving objects that can be obstacles such as people and trolleys, such as factories, hospitals, and general buildings, they move autonomously while avoiding obstacles, and are transported, delivered, guarded, cleaned, etc. An autonomous mobile device that performs the above-described work is known. Such an autonomous mobile device usually obtains position information of an obstacle using a plurality of distance sensors, and outputs information of an encoder attached to a driving wheel, information on a moving distance from a gyro sensor, information on a turning amount, and the like. Thus, a device has been devised that moves efficiently and safely while avoiding obstacles and recognizing the self-position. The operation of the autonomous mobile device is based on the reliability of the sensor itself or its output value. Therefore, there are various methods for ensuring the operation by using the failure determination function provided in the sensor itself, duplicating the sensors and comparing the information, and comparing the information from different sensors. It has been devised.

  As a method for comparing information from the above-mentioned different types of sensors, for example, in a cleaning robot that recognizes its own position by obtaining a moving distance based on the rotation amount of a driving wheel, an azimuth detection sensor that detects its own azimuth, A detection circuit that detects the amount of rotation of at least one of the pair of drive wheels, and a determination circuit that determines an abnormality in the running state of the cleaning robot based on the detection results of the azimuth detection sensor and the detection circuit. It is known that an abnormality in a running state due to a slip between a wheel and a wheel is grasped, and a warning sound is uttered and paused when the abnormality occurs (see, for example, Patent Document 1).

Japanese Patent Laid-Open No. 10-055215

  However, the above-described method and the abnormality determination method shown in Patent Document 1 have the following problems when applied to a distance sensor for detecting an obstacle. That is, the method using the failure determination function provided in the sensor itself cannot normally grasp the absolute value abnormality even if the relative abnormality between the distance measurement values can be grasped. For example, even if the actual distance to the obstacle is 1 m and the measured value is 2 m, the abnormality cannot be grasped. Further, the method of duplicating the sensors has a problem in cost because it is necessary to prepare a plurality of equivalent sensors. Further, the technique disclosed in Patent Document 1 as a technique for comparing information from different types of sensors is not applicable to abnormality detection of a sensor that measures the distance to an obstacle.

  The present invention solves the above-mentioned problems, and with a simple configuration, an abnormal state of an autonomous mobile device, in particular, an abnormal state related to a sensor for acquiring surrounding obstacle position information can be easily and at low cost. It aims at providing the autonomous mobile device which can be detected.

  In order to achieve the above object, the invention of claim 1 is directed to a plurality of environmental information acquisition means for acquiring obstacle position information by detecting obstacles around itself, and self-position acquisition for acquiring own position information. Means for storing obstacle position information acquired by the environment information acquisition means, self-position information acquired by the self-position acquisition means, map information, and control parameters, and stored in the storage means Autonomous comprising route generation means for generating a travel route based on information and control parameters, and movement control means for controlling the movement means along the travel route generated by the route generation means to move its own position In the mobile device, the map information includes obstacle position information detected by the environment information acquisition means, which is an obstacle fixed to the environment. Obstacles detected by one of the other environmental information acquisition means even though the position of the obstacle detected by any one of the environmental information acquisition means is located at the position of the obstacle included in the map information Is provided with an abnormality determining means for determining that the autonomous mobile device is in an abnormal state when the position is on the near side or the far side of the position of the obstacle in the map information.

  According to a second aspect of the present invention, in the autonomous mobile device according to the first aspect, the abnormality determination unit is configured such that the position of the obstacle detected by the other one environment information acquisition unit is the position of the obstacle in the map information. It is determined that there is an abnormal condition only when it is on the far side.

  According to a third aspect of the present invention, in the autonomous mobile device according to the first or second aspect, the abnormality determination means is abnormal only when the state of the detection by the two environmental information acquisition means continues for a predetermined time or more. It is judged that it is in a state.

  According to a fourth aspect of the present invention, in the autonomous mobile device according to the first or second aspect, the abnormality determining means is still generated even if the state of detection by the two environmental information acquiring means has moved a predetermined distance. It is determined that there is an abnormal condition only in the case.

  According to a fifth aspect of the present invention, in the autonomous mobile device according to any one of the first to fourth aspects, when the movement control means determines that the abnormal state is detected by the abnormality determination means, Information indicating that the operation is stopped and in an abnormal state is output to the outside.

  According to a sixth aspect of the present invention, in the autonomous mobile device according to any one of the first to fifth aspects, the abnormality determining means is configured such that the distance from the self position to the position of the obstacle is greater than a predetermined distance. Only when it is near, it is judged whether or not it is in an abnormal state.

  A seventh aspect of the present invention is the autonomous mobile device according to any one of the first to sixth aspects, wherein the map information used for determining the abnormal state is an obstacle on a traveling surface on which the autonomous mobile device travels. Is two-dimensional information indicating the position of.

  According to an eighth aspect of the present invention, in the autonomous mobile device according to any one of the first to seventh aspects, at least one of the environmental information acquisition means is a laser radar, and the map information includes the laser radar. The information on the wall that is an obstacle detected by is included.

  The invention according to claim 9 is the autonomous mobile device according to any one of claims 1 to 8, wherein one of the environment information acquisition means compares the map information with the self-location of the autonomous mobile device. And the abnormality determination means is configured to detect an obstacle position by the other environmental information acquisition means in a state where the self-position is corrected by the comparison information. Based on the information, it is determined whether or not there is an abnormal state.

  According to the first aspect of the present invention, the abnormality detection means for comparing the obstacle detection result by the at least two environment information acquisition means provided for acquiring the obstacle position information with the position of the obstacle in the map information is provided. Therefore, an abnormal state of the autonomous mobile device, particularly an abnormal state related to a sensor for acquiring surrounding obstacle position information can be detected easily and at low cost, that is, self-diagnosis can be performed. That is, by providing the abnormality determination means, the position of the obstacle acquired by the other environmental information acquisition means even though the position of the obstacle acquired by any of the environmental information acquisition means matches the map information. Since it is determined that there is some abnormality when does not match the map information, safety can be added to the autonomous mobile device. Further, the environment information acquisition means and the map information are usually provided in the autonomous mobile device, and the environment information acquisition means is usually provided with a plurality, so that an abnormality determination means is additionally provided. It is possible to detect an abnormal state of the autonomous mobile device only with this. Furthermore, since the abnormality determination means can be configured in software, the abnormality determination means can be easily and inexpensively equipped on an existing autonomous mobile device, and the safety of the autonomous mobile device can be improved. it can.

  According to the invention of claim 2, since the accuracy of abnormal state detection can be improved, it is possible to suppress erroneous determination that a state that is not an original abnormality is an abnormal state, and to perform a stop operation based on the erroneous determination. In addition, efficient movement of the autonomous mobile device can be realized. In other words, the obstacle on the back side of the obstacle in the map information is usually hidden behind the obstacle in front and should not be detected, and there is a high possibility that it is an abnormal state related to the sensor. It is possible to improve the accuracy of abnormal state detection by making a determination by paying attention to the above. In addition, the obstacle detected in front of the obstacle in the map information may be an actual moving obstacle, and its state should avoid collision with the obstacle, and the distance It is not an abnormal condition related to position measurement by the sensor.

  According to the invention of claim 3, since the determination based on the phenomenon of only one time is not performed, it is stable without being affected by the abnormal state temporarily generated, for example, the abnormal state generated by the influence of noise or the like. Efficient autonomous movement and operation can be realized. Further, since the determination is made in consideration of the passage of time, this determination method can be applied even when the autonomous mobile device is stopped.

  According to the invention of claim 4, since the determination is not based on a single phenomenon, it is stable without being affected by an abnormal state that occurs temporarily, for example, an abnormal state that occurs due to the influence of noise, Efficient autonomous movement and operation can be realized. In addition, since the moving distance is taken into consideration, the determination can be made with higher accuracy under the change of the relative position of the autonomous mobile device with respect to the environmental object and the obstacle.

  According to the invention of claim 5, since the autonomous mobile device is stopped and the abnormal state is made known, it is possible to avoid collisions with other objects due to movement in the abnormal state, and the autonomous mobile device itself and surrounding people And the safety of environmental items. In addition, the inspection and repair of the autonomous mobile device and the work return can be performed without delay.

  According to the invention of claim 6, since the accuracy of the abnormal state detection can be increased, it is possible to suppress erroneous determination that a state that is not an original abnormality is an abnormal state, and to perform a stop operation based on the erroneous determination. In addition, efficient movement of the autonomous mobile device can be realized. That is, when the distance between the obstacle in the map information and the autonomous mobile device (self-position) is so close that there is no possibility of an obstacle in between, it is not an influence of the moving obstacle, but an abnormal state related to the sensor. There is a high possibility that there is a high possibility, and it is possible to improve the accuracy of the abnormal state detection by making a determination while paying attention to such a state.

  According to the invention of claim 7, since the map information is not as complicated as a three-dimensional map, the position of the obstacle in the map information and the position of the obstacle acquired by the environment information acquisition means can be easily compared, An abnormal condition related to the sensor can be easily determined.

  According to the eighth aspect of the present invention, the information on the continuous surface position of the wall can be easily obtained by a normal laser radar, and the obstacle position can be expressed by a simple line segment. Abnormal conditions can be easily determined.

  According to the ninth aspect of the present invention, when the position of the obstacle acquired by any of the environmental information acquisition means does not match the position of the obstacle in the map information, the obstacle position information acquired by the specific environmental information acquisition means Based on the map, the position on the map recognized by the autonomous mobile device is corrected so that the autonomous mobile device can detect the position of the obstacle detected by the specific environment information acquisition means and the other environment information acquisition means. It can be determined whether or not there is an abnormal state.

The block block diagram of the autonomous mobile apparatus which concerns on the 1st Embodiment of this invention. The perspective view of an apparatus same as the above. The side view which shows a mode that the apparatus same as the above is detecting the obstruction. The flowchart of the process which judges whether an apparatus same as the above is in an abnormal state. The flowchart of the process which judges whether the autonomous mobile apparatus which concerns on 2nd Embodiment is in an abnormal state. The flowchart of the process which judges whether the autonomous mobile device which concerns on 3rd Embodiment is in an abnormal state. The flowchart of the process which judges whether the autonomous mobile device which concerns on 4th Embodiment is in an abnormal state. The perspective view of the autonomous mobile device which concerns on 5th Embodiment. The flowchart of the process which judges whether the autonomous mobile apparatus which concerns on 6th Embodiment is in an abnormal state. (A) (b) (c) is a top view explaining the example of the map information which the autonomous mobile device which concerns on 7th Embodiment memorize | stores. The top view of the autonomous mobile device which concerns on 8th Embodiment. The side view of an apparatus same as the above. The top view which shows a mode that the coincidence with the position of the obstruction which the apparatus same as the above detected and the position of the obstruction in map information is determined. The top view which shows the other example of a mode that determines the coincidence with the position of the obstruction which the apparatus same as the above and the position of the obstruction in map information. The top view explaining the method of the abnormality determination performed about the position of the obstruction detected by the apparatus same as the above. The top view explaining the modification of the method of the abnormality determination performed about the position of the obstruction detected by the apparatus same as the above. The top view which shows an example when an apparatus same as the above is in an abnormal state. The top view which shows the other example when an apparatus same as the above is in an abnormal state. The flowchart of the process which judges whether an apparatus same as the above is in an abnormal state. The flowchart of the process which judges whether the autonomous mobile device which concerns on 9th Embodiment is in an abnormal state. (A) is a top view which shows a mode that the apparatus same as the above has detected the position of an obstruction, (b) is a top view which shows a mode that it is judged how an apparatus is in an abnormal state after correcting its own position. The side view which shows a mode that the autonomous mobile apparatus which concerns on 10th Embodiment is detecting the obstruction. The side view which shows the example of the detection point on the obstruction which an apparatus same as the above detects. The perspective view of the autonomous mobile device which concerns on 11th Embodiment. The perspective view which shows the other example of an apparatus same as the above.

Hereinafter, an autonomous mobile device according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is common to each embodiment and is referred to as appropriate.
(First embodiment)
1 to 4 show the first embodiment. As shown in FIGS. 1, 2, and 3, the autonomous mobile device 1 detects two obstacles around itself and acquires position information of the obstacles (first environment information acquisition). Means 21 and second environment information acquisition means 22), self-position acquisition means 3 for acquiring own position information, obstacle position information 11 acquired by the environment information acquisition means 2, and self-position acquisition means 3 Storage means 4 for storing self-location information 12, map information 13 and control parameter 14, route generation means 5 for generating a travel route based on the information stored in storage means 4 and control parameter 14, and route generation A movement control means 6 for controlling the movement means 15 along the travel route generated by the means 5 to move its own position, and an abnormality determination means for determining whether or not the apparatus is in an abnormal state. And a 7. The map information 13 includes obstacle position information detected by the environment information acquisition means 2 that is an obstacle fixed to the environment.

  The first environmental information acquisition means 21 is provided with a laser radar that is provided at the center of the lower front portion of the vehicle body and scans the front horizontal plane, more generally the plane parallel to the traveling plane R (scan plane A). Yes. The second environment information acquisition means 22 is configured by a laser radar that is provided at the center of the front upper part of the vehicle body and scans downward from above. The surface (scanning surface B) from which the second environment information acquisition unit 22 acquires obstacle information is a surface inclined downward. These two laser radars oscillate a laser beam at a predetermined constant angle in each of the scan planes A and B, and acquire a distance to an object or an obstacle in a semicircular obstacle detection area having a predetermined radius. . Here, the coordinate system xyz that moves together with the autonomous mobile device 1 is an orthogonal coordinate system of the traveling direction x and the height direction z. When these are operating normally, the first environment information acquisition unit 21 and the second environment information acquisition unit 22 are obstructed with respect to an obstacle M such as a vertical wall in front as shown in FIGS. As the object position information, the same x coordinate value is output under the same y coordinate value.

  The self-position acquisition means 3 detects the environmental component information such as characteristic wall information included in the map information 13 of the travel area, the landmarks provided exclusively in the actual travel area, and the map information And the self-location of the autonomous mobile device 1 is acquired. Information acquired by the self-position acquisition means 3 is stored in the storage means 4 as self-position information 12 and is referred to by the route generation means 5 and the movement control means 6. Also, information from the moving means 15 is usually used for acquiring the self position. The moving means 15 includes a motor driven by the battery BT and driving wheels 61 (FIG. 2). The motor is provided with an encoder for measuring the rotation speed and rotation speed. The movement control means 6 can know the movement distance and the movement direction from the output of this encoder. The autonomous mobile device 1 performs so-called dead reckoning (estimated navigation) based on the self-position information obtained thereby, and moves autonomously. In order to correct the self-position based on the information from the moving means 15, self-position information based on a comparison between a detection result such as a landmark and map information is used.

  The self-position acquisition unit 3, the storage unit 4, the route generation unit 5, the movement control unit 6, and the like constitute a controller 10 as a whole. In order to configure the control unit 10, an electronic computer having a general configuration including a CPU, a memory, an external storage device, a display device, an input device, and the like, and a set of processes or functions thereon can be used. .

  Next, the abnormality determination means 7 will be described. The abnormality determination means 7 is autonomous based on the map information 13 and the obstacle position information acquired by each of the first environment information acquisition means 21 and the second environment information acquisition means 22, that is, three pieces of information having different information sources. It is determined whether or not the mobile device 1 is in an abnormal state. For example, as shown in FIG. 3, the first environment information acquisition unit 21 detects the obstacle M at the point a, and the position of the detected obstacle M is at the position of the obstacle M included in the map information 13. . However, the second environment information acquisition unit 22 detects an obstacle M (shown by a broken line) at a point b ahead of the point a. In such a case, the abnormality determination unit 7 determines that the state of the autonomous mobile device 1 is in an abnormal state. In other words, the abnormality determination unit 7 determines whether the position of the obstacle M detected by any one of the environment information acquisition units 2 is the position of the obstacle M included in the map information 13. When the position of the obstacle M detected by the environment information acquisition unit 2 is on the near side or the rear side of the position of the obstacle M in the map information 13, it is determined that the autonomous mobile device 1 is in an abnormal state.

  The process performed by the abnormality determination means 7 will be described with reference to the flowchart of FIG. Here, the first environment information acquisition unit 21 is abbreviated as one sensor 21, and the second environment information acquisition unit 22 is abbreviated as the other sensor 22. When the autonomous mobile device 1 starts to operate, the sensors 21 and 22 perform an obstacle detection operation to acquire information on the obstacle position on each of the scan planes A and B (S1). Next, the abnormality determination means 7 checks whether or not the obstacle position by one sensor 21 matches the obstacle position in the map information 13 (S2). If the positions of the obstacles in both pieces of information match (Yes in S2), it is checked whether the obstacle position by the other sensor 22 matches the position of the obstacle in the map information 13 (S3). ) If they match (Yes in S3), the control returns to the first step (S1) and the above process is repeated.

  If it is determined in step (S3) that they do not match (No in S3), the abnormality determination means 7 determines that the state of the autonomous mobile device 1 is in an abnormal state (S5), and the control unit 10 or movement The control means 6 performs an abnormality treatment (S6). The abnormal treatment is, for example, a treatment such as slowing the moving speed of the autonomous mobile device 1, stopping the movement, or switching to a spare sensor provided separately.

  In the above-described step (S2), when it is determined that the position of the obstacle by one sensor 21 and the position of the obstacle in the map information 13 do not match (No in S2), the obstacle by the other sensor 22 It is checked whether or not the position and the position of the obstacle in the map information 13 match (S4). If they do not match (No in S4), the control returns to the first step (S1) and the above processing is performed. Is repeated. If they match (Yes in S4), the abnormality determination unit 7 determines that the state of the autonomous mobile device 1 is in an abnormal state (S5), and the control unit 10 or the movement control unit 6 performs an abnormality treatment. (S6).

  In other words, the processing by the abnormality determining means 7 is an abnormal state when only one of the two pieces of information using the sensors 21 and 22 is different from the map information, and the sensors 21 and 22 are used as information sources. When both of the two pieces of information are different from the map information or coincide with the map information, this is a process that does not result in an abnormal state. If it supplementarily demonstrates here, the case where both the two information which uses the sensors 21 and 22 as information sources differ from map information will not make it an abnormal state. This is to prevent a situation in which a moving obstacle is interrupted between 1 and 1 from being included in an abnormal state. That is, since such an interrupt state is not an abnormal state, the efficient movement of the autonomous mobile device 1 is not impaired.

  Further, as a cause of the occurrence of the “abnormal state” as described above, for example, there is an abnormality in the self-position recognition by the self-position acquisition unit 3. In this case, the position of the autonomous mobile device 1 in the map information 13 is shifted. In addition, when one or both of the sensors 21 and 22 break down and the measured values are shifted or reproducibility is lost, an abnormality occurs in the mounting position or mounting angle of the sensors 21 and 22, and autonomous A case where an error occurs in the calculation of the position of the obstacle viewed from the mobile device 1 can be considered. Even if the above-described determination of the abnormal state by the abnormality determination means 7 cannot determine which of these abnormalities, it is possible to detect that some abnormality has occurred.

  According to the invention of the present embodiment, the abnormality determination means 7 for comparing the obstacle detection result by the two environmental information acquisition means 2 provided for acquiring the obstacle position information 11 with the position of the obstacle in the map information 13. Therefore, the abnormal state of the autonomous mobile device 1, in particular, the abnormal state related to the sensor for obtaining the surrounding obstacle position information 11 can be detected easily and at low cost, that is, self-diagnosis can be performed. That is, by providing the abnormality determination means 7, since some abnormality can be found, the safety of the autonomous mobile device 1 can be improved. Moreover, since the abnormality determination means 7 can be configured by software, it can be easily added to the existing autonomous mobile device 1 provided with a plurality of environment information acquisition means 2 and map information 13, An autonomous mobile device that can detect an abnormal state easily and at low cost can be provided.

(Second Embodiment)
The autonomous mobile device 1 of the present embodiment is different from the first embodiment in the abnormality determination process by the abnormality determination means 7, and as shown in the flowchart of FIG. 5, the abnormality determination in FIG. 4 of the first embodiment. A determination step (# 1) is added immediately before step (S5). In this determination step (# 1), of the obstacle positions detected by the environmental information acquisition means 2 of either the sensor 21 or 22, the obstacles that do not match the obstacle position in the map information 13 are displayed. Only when the position is behind the obstacle in the map information 13 is determined to be in an abnormal state (Yes in # 1). If the position of the obstacle that has become inconsistent is on the near side rather than the position of the obstacle in the map information 13 (No in # 1), it is not determined as an abnormal state, and the process is the first It returns to step (S1) and subsequent operation | movement is continued. That is, the state that is determined to be abnormal in the present embodiment is a more limited state than in the case of the first embodiment.

  According to the invention of this embodiment, since the accuracy of abnormal state detection can be improved, erroneous determination that a state that is not an original abnormality is an abnormal state can be suppressed, and a stop operation based on the erroneous determination is performed. In addition, efficient movement of the autonomous mobile device 1 can be realized. That is, the obstacle on the back side of the obstacle in the map information 13 is normally hidden behind the obstacle in front and should not be detected, and is likely to be an abnormal state related to the sensor. By making a determination by paying attention to the state, it is possible to improve the accuracy of the abnormal state detection. Further, the obstacle detected in front of the obstacle in the map information 13 may be an actual obstacle, that is, a moving obstacle that has interrupted in front of the environment information acquisition unit 2. Such a state is a state in which a collision with an obstacle should be avoided, and it can be said that the state is not an abnormal state related to position measurement by a distance sensor.

(Third embodiment)
The autonomous mobile device 1 of the present embodiment is different from the second embodiment in the process of abnormality determination by the abnormality determination means 7, and as shown in the flowchart of FIG. 6, the position of the second embodiment in FIG. This determination step (# 1) is changed to a temporal determination step (# 2). In this determination step (# 2), a state in which the position of the obstacle in the map information 13 does not match among the positions of the obstacle detected by the environmental information acquisition means 2 of either the sensor 21 or 22 is predetermined. Only when it continues for more than a certain time, it is determined that the state is abnormal (Yes in # 2). If the mismatched state does not continue for a predetermined time or longer (No in # 2), the abnormal state is not determined and the process returns to the first step (S1), and the subsequent operation continues. Is done. That is, the state that is determined to be abnormal in the present embodiment is a more limited state than in the case of the first embodiment.

  According to the invention of the present embodiment, since determination is not made based on a single phenomenon, it is stable without being affected by an abnormal state that occurs temporarily, for example, an abnormal state that occurs due to the influence of noise or the like. Efficient autonomous movement and operation can be realized. Further, since the determination is made in consideration of the passage of time, this determination method can be applied even when the autonomous mobile device 1 is stopped.

(Fourth embodiment)
The autonomous mobile device 1 of the present embodiment is different from the second embodiment in the process of abnormality determination by the abnormality determination means 7, and as shown in the flowchart of FIG. 7, the position of the second embodiment in FIG. This determination step (# 1) is changed to a distance determination step (# 3). In this determination step (# 3), the state in which the position of the obstacle in the map information 13 does not match among the positions of the obstacle detected by the environmental information acquisition means 2 of either the sensor 21 or 22 is as follows. It is determined that there is an abnormal condition only when it continues to occur even during the movement of the autonomous mobile device 1 beyond a predetermined distance (Yes in # 3). Further, when the mismatched state is resolved before the movement of the predetermined distance by the autonomous mobile device 1 (No in # 3), it is not determined as an abnormal state, and the process is performed in the first step (S1). ) And the subsequent operation is continued. That is, the state that is determined to be abnormal in the present embodiment is a more limited state than in the case of the first embodiment.

  According to the invention of the present embodiment, since determination is not made based on a single phenomenon, it is stable without being affected by an abnormal state that occurs temporarily, for example, an abnormal state that occurs due to the influence of noise or the like. Efficient autonomous movement and operation can be realized. In addition, since the moving distance is taken into consideration, the determination can be made with higher accuracy under the change of the relative position of the autonomous mobile device with respect to the environmental object and the obstacle.

(Fifth embodiment)
The autonomous mobile device 1 according to the present embodiment is the same as the first to fourth embodiments described above, in which the movement control unit 6 stops the autonomous mobile device 1 when the abnormality determination unit 7 determines that the abnormal state has occurred, and the abnormality is detected. Information indicating the state is output to the outside. As shown in FIG. 8, the autonomous mobile device 1 uses a display board 1a such as a liquid crystal display, a speaker 1b for voice notification, and transmission / reception for wirelessly transmitting information to an external device as means for outputting information to the outside. Any or all of the means such as the device 1d, the light emitting display 1c such as red, yellow, green, and the like can be provided.

  According to the invention of the present embodiment, since the autonomous mobile device 1 is stopped and the abnormal state is made known, it is possible to avoid an accident such as moving in the abnormal state and colliding with another object, The safety of the autonomous mobile device 1 itself, the surrounding people and environmental objects can be secured. Moreover, the inspection and repair of the autonomous mobile device 1 can be performed at an early stage, and the autonomous mobile device 1 can be returned to operation without delay.

(Sixth embodiment)
The autonomous mobile device 1 of the present embodiment is different from the second embodiment in the process of abnormality determination by the abnormality determination means 7, and as shown in the flowchart of FIG. 9, the position of the second embodiment in FIG. This determination step (# 1) is changed to a distance determination step (# 4) regarding the position of the obstacle. In this determination step (# 4), the position of the obstacle detected by the environmental information acquisition means 2 of either one of the sensors 21 and 22 does not match the position of the obstacle in the map information 13. Thus, it is determined that there is an abnormal condition only when the distance from the autonomous mobile device 1 (self position) to the position of the obstacle is shorter than a predetermined distance (Yes in # 4). If the distance is not shorter than the predetermined distance (No in # 4), the abnormal state is not determined and the process returns to the first step (S1), and the subsequent operation is continued. That is, the state that is determined to be abnormal in the present embodiment is a more limited state than in the case of the first embodiment. The predetermined distance serving as a reference for determination is a distance that is so close that there is no possibility of an obstacle entering between the obstacle and the autonomous mobile device 1.

  According to the invention of this embodiment, since the accuracy of abnormal state detection can be improved, erroneous determination that a state that is not an original abnormality is an abnormal state can be suppressed, and a stop operation based on the erroneous determination is performed. In addition, efficient movement of the autonomous mobile device 1 can be realized. That is, when there is no possibility of an obstacle entering between the obstacle and the autonomous mobile device 1 in the map information, there is a high possibility that it is not an influence of the moving obstacle or the like but an abnormal state related to the sensor. By making a determination by paying attention to such a state, it is possible to improve the accuracy of the abnormal state detection.

(Seventh embodiment)
The present embodiment relates to the configuration of the map information 13. As shown in FIG. 10, the map information 13 used for determining the abnormal state is preferably two-dimensional information indicating the position of an obstacle on the traveling surface R (see FIGS. 2 and 3) on which the autonomous mobile device 1 travels. Used. 10A shows a fixed obstacle W such as a wall surface of an indoor corridor or an outdoor fence by a line segment 11a, and the storage means 4 has coordinates of both end points 11b of the line segment 4a. Depending on the set of values, the inclination of the line segment 11a with respect to the coordinate system is also stored. 10 (b) represents a fixed obstacle W such as a wall surface or a saddle by a set of component points 11c arranged along the wall surface. It is stored by the coordinate value of the point 11c. Also, what is shown in FIG. 10 (c) is map information 13 formed by dividing the working environment plane of the autonomous mobile device 1 in a grid pattern and assigning the existence probability 11e of the fixed obstacle W to each partition 11d. The map information 13 may be other than these configurations, and various types of information in which the position of an obstacle is recorded can be used.

  According to the present embodiment, the map information 13 is two-dimensional information and is not as complex as a three-dimensional map. Therefore, the obstacle position in the map information 13 and the obstacle acquired by the environment information acquisition means 2 The position of the object can be easily compared with less processing, and an abnormal state related to the sensor can be easily determined.

(Eighth embodiment)
11 to 19 show an eighth embodiment. As shown in FIGS. 11 and 12, the autonomous mobile device 1 of this embodiment includes an environment information acquisition unit 21 (sensor 21) including a laser radar for detecting an obstacle on a horizontal plane in the front x direction, and autonomous movement. Environmental information acquisition means 23 (sensor 23) comprising a plurality of ultrasonic sensors for detecting obstacles in the three-dimensional space near the front surface and near the left and right side surfaces of the apparatus 1 is provided. The map information 13 includes information on walls that are obstacles detected by the laser radar (sensor 21). Although the obstacle detection points by the sensors 21 and 23 described here are described as two-dimensional data projected on the traveling surface R, they can be extended to three-dimensional data. Also in the map information 13, as in the seventh embodiment described above, two-dimensional data is stored, or three-dimensional data is projected onto two dimensions and used. By configuring the environment information acquisition unit 2 with such a single laser radar and a plurality of ultrasonic sensors, it is possible to detect an obstacle with an inexpensive ultrasonic sensor without using a plurality of expensive laser radars. . Moreover, according to a normal laser radar, the information on the continuous surface position of the wall can be easily obtained, and the obstacle position can be expressed by a simple line segment, so that an abnormal state related to the sensor can be easily determined.

  Here, a method for determining whether the obstacle position information on the wall obtained by the sensor 21 matches the map information will be described. As shown in FIG. 13, measurement points “a” in which distances d1, d2, d3,... Between each measurement point “a” acquired by the sensor 21 and a line segment M representing an obstacle in the map information are within a certain distance are continuous. In this case, it is determined that the data of these continuous measurement points a is consistent with the map information. Further, as shown in FIG. 14, it is assumed that the segment La can be extracted by linear approximation based on a predetermined determination condition based on the continuation of the measurement points a acquired by the sensor 21, for example. In this case, when the line segment M described in the map information 13 and the line segment acquired by the sensor 21, that is, the distance between the segment La and the angle θ are within a certain range, each measurement point a acquired by the sensor 21 is It is determined that the map information 13 matches.

  Next, determination of coincidence / non-coincidence with the obstacle detection point c by the sensor 23 in the front in the state where the segment La as the measurement result by the sensor 21 and the line segment M in the map information 13 coincide will be described. When it is determined that there is a mismatch, the state of the autonomous mobile device 1 is set to an abnormal state. As shown in FIG. 15, when an obstacle detection point c is detected at a distance D from the line segment M (or segment La), if the distance D is equal to or greater than a predetermined distance, these positions are It is determined not to match, and if it is close, it is determined to match. The predetermined constant distance includes the measurement error range of each sensor 21, 23, the error range when setting the line segment M, the error range when determining the segment La, and the error included in the self-position information 12 In consideration of the above range, the distance is more than these errors and may be a significant distance. The significant distance is, for example, a distance necessary for ensuring safety and is a distance within the braking distance of the autonomous mobile device 1.

  As shown in FIG. 16, when the sensor 23 obtains a detection result including the segment Lb, in addition to the center position and the distance D of the line segment M and the segment Lb, the inclination of the segment Lb with respect to the line segment M Considering α, the coincidence / non-coincidence of both is determined. When the distance D is equal to or greater than a certain distance or the slope α is equal to or greater than a certain angle, it is determined that the line segment M and the segment Lb do not match. The fixed angle used as the determination criterion may be determined in consideration of each error in the same manner as the determination criterion for the distance D.

  Next, a description will be given of limiting the range of measurement points in order to determine the abnormality by comparing the obstacle position information 11 obtained by the sensors 21 and 23 with each other. The reason why the comparison processing is established for the obstacle position information 11 and the map information 13 is based on the premise that each piece of information is information related to the same obstacle. The determination of the presence or absence of an abnormal state based on the comparison is useful for autonomous driving, for example, a sensor that detects obstacles ahead, which is important for autonomous driving, or obstacle position information in front. Therefore, by considering these points and limiting the range of measurement points, the abnormality determination means 7 can perform faster and more useful determination processing.

  FIG. 17 shows a point sequence of measurement points a by the sensor 21 and measurement points c, c, and c0 by the sensor 23 for the obstacle represented by the line segment M in the map information in front of the autonomous mobile device 1. Has been. It is assumed that the measurement point a or the like by the sensor 21 is determined to coincide with the line segment M. The measurement limit A0 preset for the sensor 21 and the measurement limit C0 preset for the sensor 23 are shown in semicircular shapes, respectively. Among these measurement points, the comparison target is the measurement point in the range H included in the measurement limit C0. In the range H, the positions of the measurement points c and c by the sensor 23 coincide with the line segment M, and the measurement point c0 does not coincide. Therefore, the abnormality determination means 7 makes a determination in the range H, and determines that the autonomous mobile device 1 is in some abnormal state. Here, for example, when the abnormality is a sensor abnormality, it is considered that the sensor 23 is abnormal. Further, if the three measurement points c, c, c0 are all due to the sensor 23 composed of different ultrasonic sensors, it is assumed that the ultrasonic sensor that outputs the measurement point c0 is abnormal.

  FIG. 18 shows that the measurement points c, c, c of the sensor 23 all coincide with the position of the line segment M under the same situation as in FIG. In particular, a situation in which the position of the line segment M is not coincident is shown. In this case, the abnormality determination means 7 makes a determination within the range H and determines that the autonomous mobile device 1 is in some abnormal state. The setting method of the range in which the abnormality determination unit 7 determines the abnormality needs to be set in consideration of differences in the sensor type, the mounting method, the measurement range, and the like. As a simple method, a preset range can be used as a range for abnormality determination. In addition, as shown in FIGS. 17 and 18 described above, it is expected that an obstacle is detected by a sensor that makes an abnormality determination such as a range H in consideration of the measurement range (measurement limit) of the sensor. Only the range can be set as the range of abnormality determination (abnormality determination range or simply determination range). Further, when there are a plurality of sensors, the abnormality determination range is different for each sensor. Furthermore, as the autonomous mobile device 1 moves, the positional relationship with the obstacle changes, so the determination range also changes.

  Next, the process in the case where the determination process is performed by incorporating the abnormality determination range will be described with reference to the flowchart of FIG. In the following description, it is assumed that one of the two sensors is the sensor 21 in FIG. 12 and the other sensor is the sensor 23 that is an ultrasonic sensor, but the type and arrangement of each sensor are limited to these. is not. When the autonomous mobile device 1 starts operation, the sensors 21 and 23 perform obstacle detection operation to acquire information on the obstacle position in each detection area or detection space (S1). Next, the abnormality determination means 7 sets an abnormality determination range, that is, an abnormality determination range for the measurement result by one sensor 21 (S12), and the obstacle position by one sensor 21 within the abnormality determination range is determined. It is checked whether or not the position of the obstacle in the map information 13 matches (S13). If the information of the sensor 21 and the position of the obstacle in the map information 13 are coincident (Yes in S13), out of the obstacle position as a result of measurement by the other sensor 23, the range in which abnormality is determined, that is, abnormal A determination range is set (S14). Next, in the abnormality determination range, it is checked whether or not it matches the position of the obstacle in the map information 13 (S15), and if it matches (Yes in S15), at least the abnormality determination range matches the three pieces of information. Therefore, the control returns to the first step (S1) and the above process is repeated. If they do not match (No in S15), the abnormality determination unit 7 determines that the state of the autonomous mobile device 1 is in an abnormal state (S5), and the control unit 10 or the movement control unit 6 performs an abnormality treatment. (S6).

  In the third step (S13), when the information by one sensor 21 and the position of the obstacle in the map information 13 do not match (No in S13), the obstacle as a measurement result by the other sensor 23 Of the positions, an abnormality determination range, that is, an abnormality determination range is set (S16), and it is checked whether or not the measurement result of the other sensor 23 matches the map information 13 within the abnormality determination range (S17). If they do not match (No in S17), the control returns to the first step (S1) and the above process is repeated. If they match (Yes in S17), an abnormality determination range is set for the measurement result of one sensor 21 (S18). Next, in the abnormality determination range of one of the sensors 21, it is checked whether or not the measurement result of the sensor 21 matches the position of the obstacle in the map information 13 (S19), and if they match (Yes in S19). Thus, at least within the abnormality determination range in which the determination range is limited, the three pieces of information are consistent, and control returns to the first step (S1) and the above processing is repeated. If they do not match (No in S19), the abnormality determination means 7 determines that the state of the autonomous mobile device 1 is in an abnormal state (S5).

(Ninth embodiment)
20 and 21 show the ninth embodiment. The autonomous mobile device 1 of the present embodiment performs self-position correction before the abnormality determination by the abnormality determination means 7. That is, one of the environment information acquisition means 2 of the autonomous mobile device 1 detects comparison information for correcting the self-position of the autonomous mobile device 1 by comparing with the map information 13, and an abnormality determination means. 7 determines whether or not there is an abnormal state based on the obstacle position information 11 by the other environment information acquisition means 2 in a state where the self-position is corrected by the information for comparison. In the following description, it is assumed that the sensor 21 that is the laser sensor in FIG. 12 is configured as one sensor and the sensor 23 that is an ultrasonic sensor is configured as the other sensor, but the types and arrangement of the sensors are limited to these. Is not to be done.

  As shown in the flowchart of FIG. 20, when the autonomous mobile device 1 starts operating, the sensors 21 and 23 perform an obstacle detection operation to acquire information on the obstacle position in each detection area or detection space (S1). . Next, the position of the autonomous mobile device 1 is corrected based on the obstacle position information by one sensor 21 and the map information (S22). By this self-position correction, the coordinate data included in the obstacle position information, which is a measurement result by the sensors 21 and 23, is subjected to parallel movement correction and rotational movement correction, and becomes obstacle position information including new coordinate data. After this self-position correction, it is considered that the obstacle position by one sensor 21 and the position of the obstacle in the map information 13 match. Next, it is checked whether or not the obstacle position measured by the other sensor 23 matches the obstacle position in the map information 13 (S23). If they match (Yes in S23), the control is performed. Returns to the first step (S1) and the above process is repeated. If they do not match (No in S23), the abnormality determination unit 7 determines that the state of the autonomous mobile device 1 is in an abnormal state (S5), and the control unit 10 or the movement control unit 6 performs an abnormality treatment. (S6).

  FIGS. 21A and 21B show specific examples of the above processing. The state of FIG. 21A shows a state before self-position correction. The arrangement of the measurement points a on the sensor 21 has an inclination of an angle φ with respect to a straight line indicating the obstacle M included in the map information 13. Further, the measurement point c by the sensor 23 is obtained ahead of the arrangement of the measurement points a. If the self position is corrected by the self position correcting step (S22) with respect to this state, the state shown in FIG. An autonomous mobile device 91, measurement points 9a and 9c, and an obstacle 9M indicated by broken lines indicate positions before correction. In this position correction, the autonomous mobile device 1 performs rotational movement and parallel movement of the center of gravity on the map information. In addition, as correction amount data necessary for this position correction (for example, data necessary for parallel movement), data of measurement points other than the measurement point a shown in the drawing is also used.

  According to the invention of this embodiment, when the position of the obstacle acquired by any of the environmental information acquisition means 2 does not coincide with the position of the obstacle in the map information 13, the obstacle acquired by the specific environmental information acquisition means 2 Since the position on the map recognized by the autonomous mobile device 1 is corrected based on the object position information 11, the obstacle detected by the specific environment information acquisition unit 2 and the other environment information acquisition unit 2 is corrected. As for the position, it can be determined with higher accuracy whether the autonomous mobile device 1 is in an abnormal state.

(Tenth embodiment)
22 and 23 show the tenth embodiment. The autonomous mobile device 1 of this embodiment has three-dimensional obstacle position information (three-dimensional map) as map information. The three-dimensional map is stored in the storage unit 4 by representing obstacle information by a set of three-dimensional coordinates, for example. Further, the obstacle can be expressed by a surface constituting the surface and stored as surface data. Such a three-dimensional map is not a mere set of three-dimensional coordinates or a set of surface data, but at least a specific obstacle is grouped so as to be identifiable for each obstacle. Therefore, for such an obstacle, it is possible to determine whether the two points on the obstacle acquired by the environment information acquisition unit 2 are points on the same obstacle or different obstacles. The determination possibility can be determined according to the perspective of the two points and the shape of the obstacle.

  The obstacle M shown in FIGS. 22 and 23 is an example of a specific obstacle included in the above-described three-dimensional map. In addition, the sensors 21 and 22 of the autonomous mobile device 1 have the same configuration as that in the first embodiment. The sensor 21 detects a point “a” below the obstacle M, and the sensor 22 detects a point “b” at the upper overhanging portion of the obstacle M. The measurement result by the sensor 22 matches the position of the obstacle M in the three-dimensional map information 13, but the measurement result by the sensor 21 does not match. Therefore, the abnormality determination unit 7 determines that the state of the autonomous mobile device 1 is in an abnormal state, and the control unit 10 or the movement control unit 6 performs an abnormality treatment. When such a three-dimensional map is used, the autonomous mobile device 1 has various shapes of obstacles in the operating environment, such as obstacles with overhangs and protrusions, obstacles that can pass through like the desk, It is possible to detect an obstacle having a different shape from the lower part, avoid a collision with these, determine an abnormal state based on these, and efficiently move autonomously.

(Eleventh embodiment)
An example of the autonomous mobile device 1 that uses various distance measurement sensors as the environment information acquisition means 2 will be described. As shown in FIG. 24, the autonomous mobile device 1 is equipped with laser radars 21 and 22, an ultrasonic sensor 23, a stereo camera 24, a distance image camera 25, and the like and can be used as the environment information acquisition unit 2. These are devices that can measure at least the distance to an object, and various means can be used as the environment information acquisition means 2.

  FIG. 25 shows an example in which three laser radars (sensors 21, 22, and 22) are used three-dimensionally. The sensor 21 is installed at the lower center of the front surface of the autonomous mobile device 1 and has a scan plane A on the front horizontal plane, and the sensor 22 is installed above the front left and right and has a scan plane B on the oblique side. The scan planes A and B have an overlapping portion in a plan view in front of the front surface of the autonomous mobile device 1, that is, on the plane obtained by projecting the scan planes A and B on the traveling plane R (angle β in the scan plane B). Range). Therefore, the obstacle position information obtained by the sensors 21 and 22 for the obstacle M detected in the overlapping portion is an abnormality in the autonomous mobile device 1 having the two-dimensional map information 13 (see the seventh embodiment). This is information to be determined by the determination means 7. In addition, for the autonomous mobile device 1 having the three-dimensional map information 13 (see the tenth embodiment), the abnormality determination means 7 can also determine obstacle position information other than these overlapping portions. . As described above, the range in which the abnormality determination unit 7 determines the abnormality can be set based on the difference in the sensor type, the attachment method, the measurement range, the map information 13 of the autonomous mobile device 1, and the like. The present invention is not limited to the configuration in each of the above embodiments, and various modifications can be made. For example, the configurations of the above-described embodiments can be combined with each other.

DESCRIPTION OF SYMBOLS 1 Autonomous mobile device 2 Environment information acquisition means 3 Self-position acquisition means 4 Memory | storage means 5 Path | route generation means 6 Movement control means 7 Abnormality determination means 11 Obstacle position information 12 Self-position information 13 Map information 14 Control parameter 15 Movement means 21-25 Environmental information acquisition means (sensor)
M Obstacle R Running surface

Claims (9)

A plurality of environmental information acquisition means for detecting obstacles around the self and acquiring obstacle position information;
Self-position acquisition means for acquiring self-position information;
Storage means for storing obstacle position information acquired by the environment information acquisition means, self-position information acquired by the self-position acquisition means, map information, and control parameters;
Route generating means for generating a travel route based on the information and control parameters stored in the storage means;
In an autonomous mobile device comprising: a movement control means for controlling the movement means along the travel route generated by the route generation means to move its own position;
The map information includes obstacle position information that is an obstacle fixed to the environment and detected by the environment information acquisition means,
Obstacles detected by one of the other environmental information acquisition means even though the position of the obstacle detected by any one of the environmental information acquisition means is located at the position of the obstacle included in the map information An autonomous mobile device comprising: an abnormality determining unit that determines that the autonomous mobile device is in an abnormal state when the position of the autonomous mobile device is on the near side or the deeper side than the position of the obstacle in the map information.   The abnormality determination unit determines that the obstacle is detected in an abnormal state only when the position of the obstacle detected by the other environment information acquisition unit is located behind the position of the obstacle in the map information. The autonomous mobile device according to claim 1, wherein   3. The autonomous system according to claim 1, wherein the abnormality determination unit determines that the abnormality is detected only when the detection state by the two environmental information acquisition units continues for a predetermined time or more. Mobile equipment.   3. The abnormality determination unit determines that the abnormality state is in an abnormal state only when the state of the detection by the two environmental information acquisition units still occurs even after a predetermined distance of movement. The autonomous mobile device described in 1.   The said movement control means outputs the information which shows that it is in an abnormal condition outside as well as stopping an autonomous mobile apparatus, when it is judged as an abnormal condition by the said abnormality determination means. The autonomous mobile device according to any one of 4.   6. The abnormality determination unit according to claim 1, wherein the abnormality determination unit determines whether or not the abnormality is in an abnormal state only when a distance from the self position to the position of the obstacle is shorter than a predetermined distance. The autonomous mobile device according to claim 1.   The map information used for the determination of the abnormal state is two-dimensional information indicating a position of an obstacle on a traveling surface on which the autonomous mobile device travels. The autonomous mobile device described.   The at least one of the environmental information acquisition means is a laser radar, and the map information includes information on a wall that is an obstacle detected by the laser radar. The autonomous mobile device according to claim 7. One of the environment information acquisition means is for detecting information for comparison for correcting the self-position of the autonomous mobile device by comparing with the map information,
The abnormality determination means determines whether or not an abnormality is present based on the obstacle position information obtained by the other environmental information acquisition means in a state where the self-position is corrected by the comparison information. The autonomous mobile device according to any one of claims 1 to 8, wherein:

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