Information processing device, mobile object, information processing method, and computer program

Abstract

To provide an information processing device capable of implementing efficient detection of an obstacle based on an attribute of the obstacle in the surroundings.SOLUTION: In an information processing device, an attribute such as a position or a speed of an obstacle being present around a mobile such as an autonomous mobile robot is acquired from a sensor, the obstacle or an external device. Obstacle detection parameters relating to detection of the obstacle are adjusted based on the attribute of the obstacle and the obstacle is detected on the basis of the adjusted parameters, thereby implementing the efficient detection of the obstacle.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to an information processing device, a mobile object, an information processing method, and a computer program for detecting an obstacle. [Background technology]

[0002] Autonomous mobile robots and other mobile objects use sensors such as cameras and laser range scanners (laser range finders, Laser Imaging Detection and Ranging (LiDAR)) to detect obstacles while moving. If an obstacle is detected, the robot will take measures to avoid collisions, such as stopping or slowing down.

[0003] However, the wider the detection range, the faster the vehicle can detect obstacles around it, but the vehicle will have to perform collision avoidance processing each time it detects an obstacle, which may delay its arrival at its destination.On the other hand, narrowing the detection range reduces the frequency of collision avoidance processing, but may delay the detection of obstacles and result in a collision with the obstacle.

[0004] Patent Document 1 discloses a technology that expands only the portion of the detection range set around the moving object that matches the moving direction. This method reduces the possibility of colliding with an obstacle and reduces the frequency of collision avoidance processing compared to when a detection range is set uniformly around the moving object. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2019-109768 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the configuration of Patent Document 1 does not refer to attributes of obstacles present in the vicinity, such as their speed or value, and therefore may not be able to set an appropriate detection range.

[0007] The present invention has been made in view of the above-mentioned problems, and has an object to provide an information processing device that can realize efficient obstacle detection based on the attributes of surrounding obstacles. [Means for solving the problem]

[0008] As one means for achieving the above object, the information processing device of the present invention comprises: Existing around a moving object Increase or decrease in the relative distance between the object and the moving body Get Rutori With a means, before Recording The said acquired by the means of obtaining Increase or decrease in relative distance Based on the above Objects as obstacles detection do Obstacle detection parameters In a situation where the relative distance increases, the obstacle detection area between the moving body and the object is adjusted to be smaller than in a situation where the relative distance decreases, and in a situation where the relative distance decreases, the obstacle detection area between the moving body and the object is adjusted to be larger than in a situation where the relative distance increases. an obstacle detection parameter adjusting means for adjusting the obstacle detection parameters; an obstacle detection means for detecting the obstacle based on the parameters adjusted by the obstacle detection parameter adjustment means; The present invention is characterized by having the following. [Effects of the Invention]

[0009] According to the present invention having the above configuration, it is possible to realize an information processing device that can efficiently detect obstacles based on the attributes of the surrounding obstacles. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram illustrating an example of a hardware configuration of an information processing device according to a first embodiment. [Figure 2] 1 is a functional block diagram showing the functional configuration of an information processing device 100 according to a first embodiment. [Figure 3] 3 is a conceptual diagram of a screen displayed on a display unit 105 according to the first embodiment. FIG. [Figure 4]FIG. 4 is a diagram showing an example of an obstacle attribute table 400 used to manage the attributes of obstacles. [Figure 5] 10 is a flowchart showing an example of a process for adjusting obstacle detection parameters based on obstacle attributes in the first embodiment. [Figure 6] 10(A) to 10(C) are explanatory diagrams relating to generation of the initial shape of an obstacle detection map. [Figure 7] 10A and 10B are explanatory diagrams relating to calculation of an adjustment amount. [Figure 8] 4 is a flowchart showing a process for determining the details of driving control according to the first embodiment. [Figure 9] FIG. 10 is a functional block diagram showing the functional configuration of an information processing device 100 according to a second embodiment. [Figure 10] FIG. 10 is a diagram showing an example of an obstacle attribute table 1000 used by the central management device 901 to manage the attributes of obstacles. [Figure 11] FIG. 11 is a diagram showing an example of a mobile body information table 1100 used to manage information related to mobile bodies. [Figure 12] 10 is a flowchart showing a process of adjusting obstacle detection parameters based on obstacle attributes in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, the present invention is not limited to the following embodiments. In each drawing, the same members or elements are designated by the same reference numerals, and duplicate descriptions will be omitted or simplified.

[0012] <Embodiment 1> In the information processing device according to the first embodiment, attributes of obstacles present around a moving object are acquired, and obstacle detection parameters are adjusted based on the acquired attributes of the obstacles.

[0013] 1 is a diagram showing an example of the hardware configuration of an information processing device according to embodiment 1. The information processing device 100 includes a CPU 101 as a computer, a ROM 102, a RAM 103, a storage 104, a display unit 105, a communication unit 106, and a sensor 107. Furthermore, the components are connected via a system bus 108.

[0014] The CPU 101 is a central processing unit that performs calculations and logical decisions for various processes and controls each component connected to a system bus 108. The ROM (Read-Only Memory) 102 is a program memory that stores computer programs for control by the CPU 101, including various processing procedures executed in the information processing device 100.

[0015] The RAM (Random Access Memory) 103 is used as a temporary storage area such as a main memory or work area for the CPU 101. Note that a program may be loaded into the RAM 103 from an external storage device connected to the information processing device 100.

[0016] The storage 104 is an auxiliary storage device for storing electronic data and programs according to this embodiment, and may be a hard disk drive or a solid state drive.

[0017] An external storage device may also be used to fulfill a similar role. Here, the external storage device is composed of, for example, a recording medium and an external storage drive that accesses the recording medium to write and read data.

[0018] Such recording media include, for example, CD-ROM, DVD, BD, USB memory, MO, flash memory, etc. The external storage device may also be a server device connected via a network.

[0019] The display unit 105 is, for example, a CRT display, a liquid crystal display, or the like, and is a device that outputs information to a display screen. The display unit 105 may be an external device connected to the information processing device 100 by wire or wirelessly. The communication unit 106 performs two-way communication by wire or wirelessly with other information processing devices, communication devices, external storage devices, etc.

[0020] The sensor 107 is a device that acquires attributes of obstacles present around the moving object 109 and detects the obstacles. The sensor 107 may be configured from multiple types of devices. The sensor 107 may also be, for example, a LiDAR or a Doppler radar.

[0021] The LiDAR acquires the relative position of the obstacle with respect to the moving body 109, and the Doppler radar acquires the relative speed of the obstacle with respect to the moving body 109. Furthermore, a passive stereo camera, for example, may be used as a device for detecting an obstacle. The passive stereo camera detects the presence or absence of an obstacle from the distance values ​​of feature points in stereo images. In this way, in this embodiment, the obstacle attribute acquisition unit 202 acquires the attributes of the obstacle using a sensor provided on the moving body 109.

[0022] In this embodiment, the mobile object 109 is an autonomous mobile robot (AGV) or the like on which the information processing device 100 is mounted, but the mobile object 109 is not limited to an AGV or the like. In addition, a part of the information processing device 100 may be disposed outside the mobile object 109.

[0023] Fig. 2 is a functional block diagram showing the functional configuration of the information processing device 100 according to embodiment 1. Note that some of the functional blocks shown in Fig. 2 are realized by causing a CPU 101, which serves as a computer included in the information processing device 100, to execute a computer program stored in a memory, which serves as a storage medium.

[0024] However, some or all of these functions may be implemented by hardware, such as a dedicated circuit (ASIC) or a processor (reconfigurable processor, DSP).

[0025] 2 may not be contained in the same housing, but may be configured as separate devices connected to each other via signal paths. The above explanation regarding FIG. 2 also applies to FIG. 9.

[0026] The self-information acquisition unit 201 acquires information about the moving body 109, such as the position, speed, shape, and braking performance of the moving body 109. Here, the self-information acquisition unit 201 functions as a self-information acquisition means for acquiring information about the moving body 109. The position of the moving body 109 can be acquired by using Simultaneous Localization and Mapping (SLAM) on data obtained from the sensor 107.

[0027] If LiDAR is used as the sensor 107, LiDAR SLAM may be used. If a passive stereo camera is used, Visual SLAM may be used. The speed can be calculated based on the displacement between frames of the position obtained by SLAM. Static information about the moving object 109, such as its shape and braking performance, is acquired by reading information previously stored in the storage 104.

[0028] The obstacle attribute acquisition unit 202 acquires attributes of obstacles present around the mobile object 109 using the sensor 107, and stores the acquired attributes in an obstacle attribute table 400, which will be described later. In this embodiment, the attributes of the obstacles will be described using, as examples, the relative position of the obstacle with respect to the mobile object and the relative speed of the obstacle with respect to the mobile object. Here, the obstacle attribute acquisition unit 202 functions as obstacle attribute acquisition means that acquires the attributes of obstacles present around the mobile object.

[0029] The obstacle detection parameter adjustment unit 203 adjusts parameters related to obstacle detection by the obstacle detection unit 204, which will be described later, based on the attributes of the obstacle acquired by the obstacle attribute acquisition unit 202. The obstacle detection parameter adjustment unit 203 functions as obstacle detection parameter adjustment means.

[0030] The obstacle detection unit 204 performs obstacle detection using the sensor 107. In this embodiment, an obstacle detection method using an obstacle detection map that indicates an area in which obstacles are detected will be described as an example. In obstacle detection using the obstacle detection map, an example of an obstacle detection parameter is the shape of the obstacle detection map.

[0031] At this time, the obstacle detection unit 204 refers to the obstacle detection parameters adjusted by the obstacle detection parameter adjustment unit 203. Methods of detecting an obstacle include a method in which an obstacle is detected when it enters the obstacle detection map, or a method in which an obstacle is detected when the area occupied by the obstacle in the obstacle detection map is equal to or greater than a threshold. Here, the obstacle detection unit 204 functions as an obstacle detection means that detects an obstacle based on the parameters adjusted by the obstacle detection parameter adjustment unit 203.

[0032] When the obstacle detection unit 204 detects an obstacle, the driving control content determination unit 205 determines the driving control content of the moving object 109 based on the attributes (position, speed, etc.) of the obstacle. Examples of the driving control content include one or more of stopping the moving object 109, slowing down the moving object 109, making the moving object 109 avoid the obstacle, etc. Here, the driving control content determination unit 205 determines the control content of the moving object 109 based on the attributes (position, speed, etc.) of the obstacle, and functions as driving control content determination means (movement control means) that performs movement control.

[0033] When an obstacle is detected, a predetermined driving control may simply be adopted, or the driving control may be determined based on the attributes of the detected obstacle. For example, it may be determined whether to stop or slow down the moving body 109 (and the amount of deceleration) based on the relative distance between the moving body 109 and the obstacle.

[0034] In addition, the avoidance direction of the moving body 109 may be calculated from the velocity (vector) of the moving body 109 acquired by the self-information acquisition unit 201 and the relative position of the moving body 109 and the obstacle, and the driving control content may be determined to avoid the obstacle.

[0035] The driving control content determination unit 205 displays, on the display unit 105, information about the moving object 109 acquired by the self information acquisition unit 201 and the attributes of the obstacle acquired by the obstacle attribute acquisition unit 202. The display unit 105 may also display the obstacle detection parameters adjusted by the obstacle detection parameter adjustment unit 203 and the driving control content determined by the driving control content determination unit 205. Furthermore, statistical information such as the frequency and processing time of processing in each functional unit such as the obstacle detection parameter adjustment unit 203 may also be displayed.

[0036] 3 is an image diagram of a screen displayed on the display unit 105 according to the first embodiment. 301 is an icon representing the position and shape of the moving object 109. 302 is an arrow representing the traveling direction and speed of the moving object 109. The direction of the arrow represents the traveling direction, and the length represents the speed. 303 is an icon representing the position of an obstacle.

[0037] An arrow 304 indicates the relative direction of travel and relative speed of an obstacle relative to the moving object 109. The direction of the arrow indicates the relative direction of travel, and the length of the arrow indicates the relative speed. An obstacle detection map 305 is provided. Statistical information 306 is provided regarding the processing of each functional unit.

[0038] 3, the frequency and time required for the obstacle attribute acquisition process and the obstacle detection parameter adjustment process are displayed. The frequency and time required for the obstacle detection process are also displayed.

[0039] 4 is a diagram showing an example of an obstacle attribute table 400 used to manage the attributes of obstacles. Each row of the obstacle attribute table 400 represents the attributes of one obstacle. An obstacle attribute ID 401 describes a unique ID for each obstacle to uniquely identify the attributes of the obstacle stored in the obstacle attribute table 400.

[0040] The relative position 402 field describes the relative position of the obstacle with respect to the moving object 109. The relative speed 403 field describes the relative speed of the obstacle with respect to the moving object 109. In the first embodiment, it is assumed that three obstacles are detected. Therefore, as shown in Fig. 4, a table based on the attributes of three obstacles is formed in the obstacle attribute table, but as the number of obstacles increases, the number of rows in the obstacle attribute table shown in Fig. 4 increases.

[0041] Fig. 5 is a flowchart showing an example of a process for adjusting obstacle detection parameters based on the attributes of an obstacle in embodiment 1. Note that the CPU 101, which serves as a computer in the information processing device 100, executes a computer program stored in memory to perform the operation of each step in the flowchart in Fig. 5.

[0042] 5, attributes of obstacles present around the moving object 109 are acquired, and obstacle detection parameters are adjusted based on the acquired attributes. The processing of the flow in FIG. 5 is also repeatedly executed at regular intervals.

[0043] In step S501, the self-information acquisition unit 201 acquires information about the moving object 109. In step S502 (obstacle attribute acquisition step), the obstacle attribute acquisition unit 202 first resets the contents of the obstacle attribute table 400. Next, the attributes of obstacles present around the moving object 109 are acquired and stored in the obstacle attribute table 400.

[0044] In step S503, the obstacle detection parameter adjustment unit 203 generates an initial shape of the obstacle detection map, and then in the following steps S504 to S506, adjusts the shape of the obstacle detection map.

[0045] 6(A) to 6(C) are explanatory diagrams relating to the generation of the initial shape of the obstacle detection map, and Fig. 6(A) shows a shape component 601 of the initial shape of the obstacle detection map that is generated based on the self-information acquired in step S501. For example, a shape is generated around the moving object 109 taking into account the shape of the moving object 109. In other words, the obstacle detection parameter adjustment unit 203 adjusts the shape of the obstacle detection area based on information about the moving object.

[0046] 6(B) shows a shape component 603 generated based on the relative position of an obstacle 602 with respect to the moving body 109, acquired in step S502, from among the initial shapes of the obstacle detection map. For example, a shape extending from the moving body 109 toward each obstacle is generated on the line connecting the moving body 109 and each obstacle.

[0047] That is, the obstacle detection parameter adjustment unit 203 adjusts the shape of the obstacle detection area based on the attribute of the obstacle. Here, the length of the obstacle detection map on the line connecting the moving object 109 and the obstacle is set to be constant for any obstacle.

[0048] 6C shows the initial shape 604 of the obstacle detection map. Here, the initial shape 604 generated in step S503 is generated as the union of the shape component 601 and the shape component 603.

[0049] Next, in step S505, the obstacle detection parameter adjustment unit 203 determines the amount of adjustment for the obstacle detection map based on the attributes of each obstacle stored in the obstacle attribute table 400, and adjusts the obstacle detection map in step S506. Then, the processes of steps S505 to S507 are repeated until it is determined in step S507 that adjustment has been completed for all obstacles. The processes of steps S505 to S507 will be described in detail below.

[0050] First, in step S505, the obstacle detection parameter adjustment unit 203 calculates an adjustment amount for adjusting the shape of the obstacle detection map based on the attributes of each obstacle in the above-mentioned repetition. That is, the attributes (relative position 402, relative velocity 403) of each obstacle in the above-mentioned repetition are obtained from the obstacle attribute table 400 in Fig. 4. Next, a component vector on the line connecting the moving object 109 and the obstacle is calculated for the relative velocity 403.

[0051] 7(A) and (B) are explanatory diagrams relating to the calculation of the adjustment amount, and explain the processing contents in steps S505 and S506. Fig. 7(A) illustrates how the component vector on the line connecting the moving body 109 and the obstacle 602 is calculated for the relative velocity 403 in step S505.

[0052] Reference numeral 701 denotes a component vector of the relative velocity 403 on the line connecting the moving object 109 and the obstacle 602. To find the component vector 701, first, a perpendicular line is drawn from the relative velocity 403 to the line connecting the moving object 109 and the obstacle 602, and the intersection point with the line is found. Then, the vector directed from the obstacle 602 to the intersection point is calculated as the component vector 701.

[0053] In step S505, the adjustment amount is calculated using the following equation 1. Here, the "direction" in equation 1 is set to 1 if the component vector 701 is in the opposite direction to the vector from the moving body 109 to the obstacle 602, and is set to -1 if the component vector 701 is in the same direction as the vector. In addition, the "speed" in equation 1 corresponds to the magnitude of the component vector 701. Adjustment amount = direction * speed (Equation 1)

[0054] In step S506, the obstacle detection parameter adjustment unit 203 adjusts the shape of the obstacle detection map in the direction in which the obstacle exists, based on the adjustment amount calculated in step S505. If the adjustment amount in Equation 1 is a positive value, the shape of the obstacle detection map in the direction in which the obstacle exists is enlarged so as to be elongated in proportion to the absolute value of the adjustment amount. If the adjustment amount is a negative value, the shape of the obstacle detection map in the direction in which the obstacle exists is reduced in proportion to the absolute value of the adjustment amount.

[0055] 7(B) shows how the shape of the obstacle detection map is adjusted based on the adjustment amount calculated in step S505. The diagram shows how the shape of the obstacle in the direction of the obstacle is enlarged as the obstacle approaches the moving body. Conversely, the diagram shows how the shape of the obstacle in the direction of the obstacle is reduced as the obstacle moves away from the moving body.

[0056] 7(A) and 7(B), the shape of the obstacle detection map is enlarged in the direction of the obstacle as the speed at which the obstacle approaches relatively increases, and conversely, the shape of the obstacle detection map is reduced in the direction of the obstacle as the speed at which the obstacle moves relatively away increases.

[0057] Furthermore, the processing of steps S505 to S507 is repeated until it is determined in step S507 that adjustment has been completed for all obstacles. If it is determined in step S507 that adjustment has been completed for all obstacles, the flow of Fig. 5 ends. Here, steps S505 to S507 function as an obstacle detection parameter adjustment step that adjusts obstacle detection parameters related to obstacle detection based on the obstacle attributes acquired in step S502.

[0058] Fig. 8 is a flowchart showing a process for determining the driving control content according to the first embodiment, and shows the process for determining the driving control content of the moving body 109 when an obstacle is detected. Note that the CPU 101 as a computer in the information processing device 100 executes a computer program stored in memory to perform the operation of each step of the flowchart in Fig. 8. Note that the process of the flow in Fig. 8 is repeatedly executed at regular intervals.

[0059] In step S801, the obstacle detection unit 204 determines whether an obstacle has been detected in the obstacle detection map adjusted in step S506 of Fig. 5. Here, step S801 functions as an obstacle detection step that detects an obstacle based on the parameters adjusted in the obstacle detection parameter adjustment step. If an obstacle is detected, the process proceeds to step S802. If an obstacle is not detected in step S801, the flow in Fig. 8 ends.

[0060] In step S802, the driving control content determination unit 205 determines the driving control content of the moving body 109 based on the attributes (position, speed, etc.) of the detected obstacle. That is, the driving control (movement control) for the detected obstacle includes one or more of stopping the moving body 109, slowing down the moving body 109, making the moving body 109 avoid the obstacle, etc.

[0061] As described above, in this embodiment, by adjusting the obstacle detection parameters based on the acquired attributes of obstacles, it is possible to generate an optimal obstacle detection map based on the attributes of surrounding obstacles, thereby improving the efficiency and safety of mobile object operations.

[0062] In this embodiment, the attributes of an obstacle that can be acquired using the sensor 107 are described as the relative position and relative speed of the obstacle with respect to the moving body 109, but the attributes of the obstacle that can be acquired may also be, for example, the type of obstacle, height, type of cargo, etc.

[0063] Alternatively, the obstacle attribute acquisition unit 202 may acquire the attributes of an obstacle by directly communicating with the obstacle. In this way, it becomes possible to acquire the attributes of an obstacle that are difficult to acquire with the sensor 107, and generate an optimal obstacle detection map.

[0064] Note that attributes of obstacles that are difficult to obtain with the sensor 107 include attributes related to the impact of colliding with an obstacle, such as the value of the obstacle, how easily it breaks, how easily it gets dirty, etc. Also, there are attributes related to the perception characteristics and detection characteristics of the obstacle itself, such as the age of the obstacle, field of view, the detection range of the obstacle itself, and the detection frequency of the obstacle itself.

[0065] Other obstacle characteristics include attributes related to the movement performance of the obstacle, such as the weight of the obstacle, the weight of the load, the braking performance of the obstacle, how easily it falls over, etc. Also, there are attributes related to the task (job) of the obstacle, such as the content and type of task the obstacle is performing, the type of load, etc.

[0066] Although the present embodiment has been described using spatial parameters such as the shape of the obstacle detection map as examples of obstacle detection parameters, any parameter related to obstacle detection may be used. For example, a spatial parameter similar to the shape of the obstacle detection map is the spatial resolution of obstacle detection.

[0067] Furthermore, the time-related parameters include time-related parameters such as the frequency of obstacle detection processing. That is, for an obstacle approaching at a relatively fast speed based on the attributes of the obstacle, the spatial resolution for detecting the obstacle may be relatively increased, or the frequency of detecting the obstacle may be relatively increased. In this way, the obstacle detection parameters in this embodiment include parameters related to space or time.

[0068] When acquiring the attributes of an obstacle, the reliability of the attributes of the obstacle may also be acquired. In this case, the obstacle detection parameters may be adjusted based on the reliability. Here, the reliability of the attributes of the obstacle represents the accuracy of the means for acquiring the attributes. For example, if the attribute of the obstacle, i.e., the relative position of the obstacle with respect to the moving object, can be acquired with an error of N meters and if it can be acquired with an error of M meters (M is a positive real number greater than N), the reliability of the attribute of the obstacle is considered to be higher in the former case.

[0069] If the reliability of the obstacle attribute is low, the obstacle detection parameters are adjusted so that the obstacle becomes easier to detect, whereas if the reliability of the obstacle attribute is high, the obstacle detection parameters are adjusted so that the obstacle becomes harder to detect.

[0070] As a specific example, a case will be described in which the attribute of an obstacle is, for example, the relative position of the obstacle with respect to the moving body, and the obstacle detection parameter is the shape of the obstacle detection map. Under such conditions, if the reliability of the obstacle attribute is low, for example, the detection area of ​​the obstacle detection map in the direction of the obstacle may be expanded in a direction perpendicular to the line connecting the moving body and the obstacle.

[0071] The obstacle detection parameters may be adjusted based on the frequency of acquiring the obstacle attributes. Specifically, the frequency of acquiring the obstacle attributes refers to, for example, the frequency of execution of the process of acquiring the obstacle attributes (step S502). That is, if the frequency of acquiring the obstacle attributes is low, the obstacle detection parameters are adjusted so that the obstacle becomes easier to detect. If the frequency of acquiring the obstacle attributes is high, the obstacle detection parameters are adjusted so that the obstacle becomes harder to detect.

[0072] Specifically, when the obstacle detection parameter is the shape of the obstacle detection map, if the frequency of acquiring obstacle attributes is low, the obstacle detection map may be widened in all directions, whereas if the frequency of acquiring obstacle attributes is high, the obstacle detection map may be narrowed in the direction of the obstacle.

[0073] In this embodiment, the method of detecting an obstacle is described as using an obstacle detection map, but the obstacle detection map may be a weighted map in which each coordinate has a weight value.

[0074] That is, for example, when generating an initial detection map, the obstacle detection parameter adjustment unit 203 sets a weight value for each coordinate of the obstacle detection area based on the attributes of the obstacle. At that time, the weight value may be set based on, for example, the distance between the moving body and the obstacle. At this time, the weight value is set to be larger as the distance between the moving body and the obstacle becomes shorter.

[0075] The obstacle detection unit 204 may refer to the weight values ​​of each coordinate in the obstacle detection map when detecting an obstacle. For example, it may determine that an obstacle has been detected when the obstacle has entered an area of ​​the obstacle detection map with a weight value equal to or greater than a certain threshold. Alternatively, it may determine that an obstacle has been detected when the sum of the weight values ​​of the areas in the obstacle detection map where the obstacle exists is equal to or greater than a certain threshold.

[0076] The driving control content determination unit 205 may determine the control content based on the weight value of each coordinate in the obstacle detection map. For example, the driving control content determination unit 205 may calculate the average weight value of the area where an obstacle exists in the obstacle detection map, and determine the deceleration amount to be proportional to the calculated average value.

[0077] <Embodiment 2> The information processing device of embodiment 2 acquires attributes of obstacles present around a moving object from a central management device that centrally manages the attributes of obstacles, and adjusts obstacle detection parameters based on the acquired attributes of the obstacles.

[0078] 9 is a functional block diagram showing the functional configuration of an information processing device 100 according to embodiment 2. Reference numeral 100 denotes an information processing device, 109 denotes a moving body, and 201 to 205 are the same as those in embodiment 1, and therefore descriptions thereof will be omitted. In this embodiment, examples of the attributes of an obstacle will be described, including the relative position of the obstacle with respect to the moving body and the relative speed of the obstacle with respect to the moving body.

[0079] Reference numeral 901 denotes a central management device that functions as a management server. The central management device 901 also includes a CPU as a computer and a memory that stores computer programs. Reference numeral 902 denotes an obstacle attribute receiving unit, and 906 denotes an obstacle attribute transmitting unit for transmitting obstacle attributes acquired by the mobile object 109 to the outside. The obstacle attribute receiving unit 902 receives the obstacle attributes transmitted by the obstacle attribute transmitting unit 906 of the mobile object 109 and stores them in an obstacle attribute table 1000 of the central management device 901, which will be described later.

[0080] An obstacle attribute transmission unit 903 is provided in the central management device 901. It receives an acquisition request for obstacle attributes from the mobile object 109 and transmits the attributes of obstacles around the mobile object 109 based on the acquisition request. When the mobile object 109 transmits the acquisition request, the request may include information about the current location of the mobile object 109.

[0081] If the acquisition request includes information about the current location, the attributes of obstacles present within a predetermined range centered on the current location of the mobile body 109 are searched for in the obstacle attribute table 1000 of the central management device 901 (described later) and transmitted to the mobile body 109.

[0082] A mobile object information registration unit 904 receives input of information about multiple mobile objects from a user and registers the input information about multiple mobile objects by storing it in a mobile object information table 1100, which will be described later. Here, the information about the mobile object includes static information about the shape and braking performance of the mobile object.

[0083] A mobile object information transmitting unit 905 receives an acquisition request for information about the mobile object stored in a mobile object information table 1100 (described later) from the self information acquiring unit 201 of the mobile object 109. Based on the acquisition request, the mobile object information transmitting unit 905 transmits information about the mobile object stored in the mobile object information table 1100. The acquisition request may include an ID for uniquely identifying the mobile object 109.

[0084] Based on the ID of the moving body 109 included in the acquisition request, the information on the moving body identified by the ID is searched for in a moving body information table 1100 (described later) and transmitted to the moving body 109. As described above, 906 is an obstacle attribute transmission unit, which transmits the obstacle attributes acquired by the obstacle attribute acquisition unit 202 to the central management device 901.

[0085] 10 is a diagram showing an example of an obstacle attribute table 1000 used by the central management device 901 to manage the attributes of obstacles. The obstacle attribute table 1000 has a configuration similar to that of the obstacle attribute table 400 in the first embodiment. That is, each row of the obstacle attribute table 1000 represents the attributes of one obstacle. An ID for uniquely identifying the attributes of an obstacle stored in the obstacle attribute table 1000 is written in the obstacle attribute ID 1001 field.

[0086] The relative position 1002 field describes the relative position of the obstacle with respect to the moving body 109. The relative speed 1003 field describes the relative speed of the obstacle with respect to the moving body 109. Note that the obstacle attribute table 1000 stores attributes related to obstacles acquired from multiple moving bodies 109. Furthermore, it may store attributes related to obstacles acquired from cameras placed at multiple positions or attributes related to obstacles input in advance by the user.

[0087] 11 is a diagram showing an example of a mobile object information table 1100 used to manage information about mobile objects. Each row of the mobile object information table 1100 represents information about one mobile object. An ID for uniquely identifying the information about the mobile object stored in the mobile object information table 1100 is written in the mobile object information ID 1101 field.

[0088] The shape 1102 column describes information representing the shape of the moving object 109. Information representing the shape includes a two-dimensional point cloud representing the shape when viewed from above, a three-dimensional point cloud representing a three-dimensional shape, etc. In this embodiment, an example of a shape is a two-dimensional point cloud representing the shape when viewed from above.

[0089] The braking performance 1103 column describes an index that indicates how well the brakes of the moving body 109 work (braking performance). In this embodiment, braking distance (unit: meters) is shown as an example of braking performance.

[0090] Fig. 12 is a flowchart showing a process of adjusting obstacle detection parameters based on the attributes of an obstacle in embodiment 2. Note that the CPU 101, which serves as a computer in the information processing device 100, executes a computer program stored in memory to perform the operation of each step in the flowchart in Fig. 12.

[0091] In Fig. 12, attributes of obstacles present around the moving object 109 are acquired from the central management device 901, and obstacle detection parameters are adjusted based on the acquired attributes. The flow in Fig. 12 is repeatedly executed at regular intervals. Note that steps with the same numbers as in Fig. 5 are the same processes, so their explanation will be omitted.

[0092] In step S1201, the obstacle attribute acquisition unit 202 first resets the contents of the obstacle attribute table 400. Next, the obstacle attribute acquisition unit 202 transmits a request to the central management unit 901 to acquire the attributes of obstacles existing around the moving object 109.

[0093] The obstacle attribute transmission unit 903 of the central management unit 901 receives a request transmitted from the mobile unit 109 to acquire attributes of obstacles existing around the mobile unit 109. Then, based on the acquisition request, the central management unit 901 acquires attributes of obstacles existing around the mobile unit 109 from the obstacle attribute table 1000.

[0094] The acquired obstacle attributes are then transmitted to the moving object 109. The obstacle attribute acquisition unit 202 receives the obstacle attributes transmitted from the obstacle attribute transmission unit 903 of the central management device 901, and stores them in the obstacle attribute table 400 of the moving object 109. The subsequent processing of steps S503 to S507 is the same as the processing of FIG.

[0095] As described above, in the second embodiment, the attributes of obstacles present around the moving object are acquired from the central management device 901, which serves as a management server that centrally manages the attributes of obstacles, and obstacle detection parameters are adjusted based on the acquired attributes of the obstacles.

[0096] This enables the mobile body 109 to use obstacle detection parameters based on the attributes of obstacles that exist in the blind spots of the mobile body 109 and that cannot be directly acquired by the sensor 107, thereby improving the efficiency and safety of the mobile body's operations. At this time, the mobile body 109 may also use the attributes of surrounding obstacles that can be acquired by the sensor 107 at the same time.

[0097] In step S1201 of FIG. 12, the attributes of the obstacles may be obtained from the central management device 901, and the attributes of the obstacles present around the mobile object 109 may be obtained using the sensor 107 of the mobile object 109 and transmitted to the central management device 901.

[0098] That is, the obstacle attribute transmitting unit 906 of the moving object 109 transmits the obstacle attributes acquired using the sensor 107 to the central management device 901. The obstacle attribute receiving unit 902 accepts the obstacle attributes transmitted from the moving object 109 and stores them in the obstacle attribute table 1000.

[0099] In this case, the attributes of the common obstacle (e.g., position and speed) may be corrected by statistically processing them based on the attributes of the obstacles received from multiple mobile units, thereby improving the accuracy of the attributes of the common obstacle.

[0100] In the above embodiments, examples of autonomous mobile robots such as AGVs have been described as mobile bodies, but the mobile body may also be, for example, a car or drone that performs automatic driving or driving assistance, or any mobile body that is capable of moving.

[0101] Although the present invention has been described in detail above based on the preferred embodiments, the present invention is not limited to the above embodiments, and various modifications are possible based on the spirit of the present invention, and these modifications are not excluded from the scope of the present invention. The above embodiments include the following combinations.

[0102] (Configuration 1) An information processing device comprising: obstacle attribute acquisition means for acquiring attributes of obstacles present around a moving body; obstacle detection parameter adjustment means for adjusting obstacle detection parameters related to the detection of the obstacles based on the attributes of the obstacles acquired by the obstacle attribute acquisition means; and obstacle detection means for detecting the obstacles based on the parameters adjusted by the obstacle detection parameter adjustment means.

[0103] (Configuration 2) The information processing device according to configuration 1, further comprising a driving control content determination means for determining a control content for the moving body when the obstacle detection means detects an obstacle.

[0104] (Configuration 3) The information processing device according to configuration 1 or 2, wherein the obstacle detection parameters include parameters relating to space or time.

[0105] (Configuration 4) The information processing device according to any one of configurations 1 to 3, wherein the obstacle attribute acquisition means acquires the attributes of the obstacle using a sensor provided on the moving body.

[0106] (Configuration 5) The information processing device according to any one of configurations 1 to 4, wherein the obstacle attribute acquisition means acquires the attributes of the obstacle by communicating with the obstacle.

[0107] (Configuration 6) The information processing device according to any one of configurations 1 to 5, wherein the obstacle attribute acquisition means acquires the attributes of the obstacles from a central management device that manages the attributes of the obstacles.

[0108] (Configuration 7) An information processing device described in any one of configurations 1 to 6, further comprising a self-information acquisition means for acquiring information about the moving body, and the obstacle detection parameter adjustment means adjusts the obstacle detection parameters based on the information about the moving body.

[0109] (Configuration 8) The information processing device according to any one of configurations 1 to 7, wherein the obstacle detection parameter adjustment means adjusts the shape of the obstacle detection area based on the attribute of the obstacle.

[0110] (Configuration 9) The information processing device according to any one of configurations 1 to 8, wherein the obstacle detection parameter adjustment means sets a weight value for each coordinate of the obstacle detection area based on the attribute of the obstacle.

[0111] (Configuration 10) The information processing device according to any one of configurations 1 to 9, wherein the obstacle detection parameter adjustment means adjusts the frequency of detecting an obstacle based on an attribute of the obstacle.

[0112] (Configuration 11) A mobile body comprising: an information processing device according to any one of configurations 1 to 10; and a movement control means for controlling the movement of the mobile body based on an obstacle detected by the obstacle detection means.

[0113] (Method) An information processing method comprising: an obstacle attribute acquisition step for acquiring attributes of obstacles present around a moving body; an obstacle detection parameter adjustment step for adjusting obstacle detection parameters related to the detection of the obstacle based on the attributes of the obstacle acquired in the obstacle attribute acquisition step; and an obstacle detection step for detecting the obstacle based on the parameters adjusted in the obstacle detection parameter adjustment step.

[0114] (Program) A computer program for controlling the information processing device according to any one of configurations 1 to 10 or the means of the moving body according to configuration 11 by a computer.

[0115] In order to realize part or all of the control in the above-described embodiments, a computer program that realizes the functions of the above-described embodiments may be supplied to an information processing device or the like via a network or various storage media. Then, a computer (or a CPU, MPU, or the like) in the information processing device or the like may read and execute the program. In this case, the program and the storage medium storing the program constitute the present invention. [Explanation of symbols]

[0116] 100: Information processing device 109: Mobile 201: Self-information acquisition section 202: Obstacle attribute acquisition unit 203: Obstacle detection parameter adjustment unit 204: Obstacle detection unit 205: Driving control content determination unit

Claims

1. an acquisition means for acquiring an increase or decrease in the relative distance between the moving body and an object present around the moving body; an obstacle detection parameter adjusting means for adjusting an obstacle detection parameter for detecting the object as an obstacle based on an increase or decrease in the relative distance acquired by the acquiring means, so that when the relative distance increases, the obstacle detection area between the moving body and the object is smaller than when the relative distance decreases, and when the relative distance decreases, the obstacle detection area between the moving body and the object is larger than when the relative distance increases; an obstacle detection means for detecting the obstacle based on the parameters adjusted by the obstacle detection parameter adjustment means; An information processing device comprising:

2. 2. The information processing apparatus according to claim 1, further comprising: a driving control content determining means for determining a control content of said mobile object when said obstacle detecting means detects an obstacle.

3. The acquisition means acquires a reliability of an increase or decrease in the relative distance, 2. The information processing apparatus according to claim 1, wherein the obstacle detection parameter adjusting means further adjusts the obstacle detection parameters so that the obstacle detection area is enlarged when the reliability is low compared to when the reliability is high.

4. 2. The information processing apparatus according to claim 1, wherein the acquisition means acquires the attributes of the object using a sensor provided on the mobile body.

5. 2. The information processing apparatus according to claim 1, wherein the acquisition means acquires the attributes of the object by communicating with the object.

6. 2. The information processing apparatus according to claim 1, wherein said acquiring means acquires the attributes of the object from a central management device that manages the attributes of the object.

7. The mobile device further includes a self-information acquisition means for acquiring information about the mobile device, 2. The information processing apparatus according to claim 1, wherein the obstacle detection parameter adjusting means adjusts the obstacle detection parameters based on information about the moving object.

8. When there are a plurality of the objects, the acquisition means acquires an increase or decrease in the relative distance between the object and the moving body for each object, 2. The information processing apparatus according to claim 1, wherein the obstacle detection parameter adjusting means adjusts the obstacle detection parameters relating to the obstacle detection area for each object.

9. The information processing device according to claim 1 ; a movement control means for controlling the movement of the mobile body based on the obstacle detected by the obstacle detection means.

10. an acquisition step of acquiring an increase or decrease in a relative distance between the moving body and an object present around the moving body; an obstacle detection step of adjusting obstacle detection parameters for detecting the object as an obstacle based on the increase or decrease in the relative distance acquired in the acquisition step so that, when the relative distance increases, the obstacle detection area between the moving body and the object is smaller than when the relative distance decreases, and so that, when the relative distance decreases, the obstacle detection area between the moving body and the object is larger than when the relative distance increases; An information processing method comprising:

11. A computer program for causing a computer to function as an information processing device according to any one of claims 1 to 8 or as each of the means of the mobile body according to claim 9.

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