Control method of autonomous mobile device, controller, device and storage medium

Abstract

The invention provides a control method of an autonomous mobile device, a controller, a device and a storage medium, and belongs to the field of robotics.The method comprises the steps that a target grid map of the environment where the autonomous mobile device is located is obtained, and the target grid map comprises a first grid located in an operation area and a second grid located outside the operation area; determining an occupied grid of the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device; determining the nearest distance between the occupied grid and the second grid, wherein the nearest distance is the distance between the occupied grid and the nearest second grid; and controlling the autonomous mobile device according to a control strategy matched with the nearest distance, so that the autonomous mobile device operates in the operation area. The autonomous mobile equipment can be accurately ensured to operate in the operation area, and the operation safety of the autonomous mobile equipment is greatly improved.

Description

Technical Field

[0001] The present application relates to the field of robotics, and in particular to a control method, controller, device and storage medium for an autonomous mobile device. Background Art

[0002] A mowing robot is an intelligent device that can perform fully automatic mowing operations in the work area. It can perform mowing tasks on behalf of users, freeing up the user's labor. Before the mowing robot performs the mowing task on the mowing area, it needs to plan the mowing path according to the boundary of the mowing area, so that the mowing robot can perform the mowing task along the mowing path.

[0003] At present, lawn mower robots use physical boundary lines to fix the working area. This physical boundary line is usually a thin line fixed on the edge of the lawn. During the operation of the lawn mower robot, the lawn mower robot is judged whether it is around the boundary by magnetic induction physical boundary lines. If it is around the boundary, it will turn around to prevent the lawn mower robot from going out of the boundary. However, the physical boundary line has the problem that it is too cumbersome to bury the magnetic induction line in advance and the magnetic induction line is damaged and difficult to maintain. Or the user needs to remotely control the lawn mower robot to walk around the lawn boundary so that the lawn mower robot can locate its position and confirm the lawn boundary, and the user needs to establish the lawn boundary based on the positioning and boundary confirmation information of the lawn mower robot. This method is labor-intensive and cumbersome, resulting in a poor user experience.

[0004] Therefore, how to accurately and conveniently control the operation of the lawn mower robot and ensure that the lawn mower robot operates within the operating area is a problem that needs to be solved urgently. Summary of the invention

[0005] The main purpose of the present application is to provide a control method, controller, device and storage medium for an autonomous mobile device, aiming to ensure that the lawn mower operates within the operating area.

[0006] In a first aspect, the present application provides a control method for an autonomous mobile device, the control method for an autonomous mobile device comprising the following steps:

[0007] Acquire a target grid map of the environment in which the autonomous mobile device is located, wherein the target grid map includes a first grid located in an operating area and a second grid located outside the operating area;

[0008] Determining the grid occupied by the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device;

[0009] Determine the shortest distance between the occupied grid and the second grid, the shortest distance being the distance between the occupied grid and the nearest second grid;

[0010] The autonomous mobile device is controlled according to a control strategy matching the shortest distance, so that the autonomous mobile device operates in the operating area.

[0011] In a second aspect, the present application also provides a controller, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the control method of the autonomous mobile device as described above are implemented.

[0012] In a third aspect, the present application also provides an autonomous mobile device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the control method of the autonomous mobile device as described above are implemented.

[0013] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the control method of the autonomous mobile device as described above are implemented.

[0014] The present application provides a control method, controller, device and storage medium for an autonomous mobile device. The present application obtains a target grid map of the environment in which the autonomous mobile device is located, the target grid map including a first grid located in an operating area and a second grid located outside the operating area; determines the occupied grid of the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device; determines the closest distance between the occupied grid and the second grid, the closest distance being the distance between the occupied grid and the nearest second grid; controls the autonomous mobile device according to a control strategy matching the closest distance, so that the autonomous mobile device operates in the operating area. In the present application, a target grid map including a first grid located in the operating area and a second grid located outside the operating area is first established, and the occupied grid of the autonomous mobile device in the target grid map is determined, and then a control strategy is matched based on the closest distance between the occupied grid and the second grid, and the autonomous mobile device is controlled according to the control strategy, which can accurately ensure that the autonomous mobile device is operating in the operating area, greatly improving the safety of the operation of the autonomous mobile device. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A schematic diagram of a flow chart of a control method for an autonomous mobile device provided in an embodiment of the present application;

[0017] Figure 2 for Figure 1 A schematic flow chart of sub-steps of a method for controlling an autonomous mobile device;

[0018] Figure 3 A schematic diagram of a scene of an initial grid map provided for implementing this embodiment;

[0019] Figure 4 A schematic diagram of a scene of a target grid map provided for implementing this embodiment;

[0020] Figure 5 A schematic diagram of a scenario for implementing the control method of the autonomous mobile device provided in this embodiment;

[0021] Figure 6 Another schematic diagram of a target grid map for implementing this embodiment;

[0022] Figure 7 A schematic diagram of another scenario for implementing the control method of the autonomous mobile device provided in this embodiment;

[0023] Figure 8 A schematic diagram of another scenario for implementing the control method of the autonomous mobile device provided in this embodiment;

[0024] Fig. 9 A schematic block diagram of the structure of a control device provided in an embodiment of the present application;

[0025] Fig.10 A schematic block diagram of the structure of an autonomous mobile device provided in an embodiment of the present application.

[0026] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0029] A mowing robot is an intelligent device that can perform fully automatic mowing operations in the work area. It can perform mowing tasks on behalf of users, freeing up the user's labor. Before the mowing robot performs the mowing task on the mowing area, it needs to plan the mowing path according to the boundary of the mowing area, so that the mowing robot can perform the mowing task along the mowing path.

[0030] At present, lawn mower robots use physical boundary lines to fix the working area. This physical boundary line is usually a thin line fixed on the edge of the lawn. During the operation of the lawn mower robot, the lawn mower robot is judged whether it is around the boundary by magnetic induction physical boundary lines. If it is around the boundary, it will turn around to prevent the lawn mower robot from going out of the boundary. However, the physical boundary line has the problem that it is too cumbersome to bury the magnetic induction line in advance and the magnetic induction line is damaged and difficult to maintain. Or the user needs to remotely control the lawn mower robot to walk around the lawn boundary so that the lawn mower robot can locate its position and confirm the lawn boundary, and the user needs to establish the lawn boundary based on the positioning and boundary confirmation information of the lawn mower robot. This method is labor-intensive and cumbersome, resulting in a poor user experience.

[0031] To solve the above problems, the embodiments of the present application provide a control method, controller, device and storage medium for an autonomous mobile device. The control method for the autonomous mobile device includes: obtaining a target grid map of the environment in which the autonomous mobile device is located, the target grid map including a first grid located in an operating area and a second grid located outside the operating area; determining the grid occupied by the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device; determining the closest distance between the occupied grid and the second grid, the closest distance being the distance between the occupied grid and the nearest second grid; and controlling the autonomous mobile device according to a control strategy matching the closest distance so that the autonomous mobile device operates in the operating area.

[0032] Among them, the control method of the autonomous mobile device can be applied to a controller, and the controller can be an electronic device such as a mobile phone, a tablet computer, a laptop personal digital assistant, and a wearable device. Of course, the control method of the autonomous mobile device can also be applied to an autonomous mobile device, and the autonomous mobile device can be a lawn mower robot and a sweeping robot, etc. For example, the autonomous mobile device is a lawn mower robot, which is an intelligent device that can perform fully automatic lawn mowing operations in the working area. In the case of random lawn mowing operations, when encountering obstacles, it rotates 45°, 90°, or 180° clockwise or counterclockwise to change the working direction to complete the lawn mowing task in the working area.

[0033] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0034] Please refer to Figure 1 , Figure 1 A flowchart of a control method for an autonomous mobile device provided in an embodiment of the present application.

[0035] like Figure 1 As shown, the control method of the autonomous mobile device includes steps S101 to S104.

[0036] Step S101: Acquire a target grid map of the environment in which the autonomous mobile device is located, wherein the target grid map includes a first grid located within an operating area and a second grid located outside the operating area.

[0037] The operation area is an area where the autonomous mobile device needs to work; the area outside the operation area is an area where the autonomous mobile device does not work, for example, a rock area outside the mowing operation area.

[0038] In some embodiments, Figure 2 As shown, step S101 includes sub-steps S1011 to S1012.

[0039] Sub-step S1011: During the movement of the autonomous mobile device, environmental information of the environment in which the autonomous mobile device is located is collected through a sensor, wherein the sensor is mounted on the autonomous mobile device.

[0040] The sensor may be a binocular camera.

[0041] In some embodiments, during the movement of the autonomous mobile device, image information of the environment in which the autonomous mobile device is located is collected by a binocular camera, and the image information includes an operating area and a non-operating area.

[0042] Sub-step S1012: constructing the target grid map according to the environmental information.

[0043] The target grid map includes a first grid located within the working area and a second grid located outside the working area.

[0044] In some embodiments, corresponding semantic information and depth information are obtained according to environmental information; the semantic information and depth information are mapped to an initial grid map to obtain a target grid map, each grid in the target grid map corresponds to a cost value, the cost value is used to represent the type of an object in the environment, and the first grid and the second grid are determined based on the cost value corresponding to each grid. By mapping the semantic information and depth information to the initial grid map, the target grid map can be accurately obtained.

[0045] In some embodiments, the depth information includes a depth map of each pixel; a preset depth estimation algorithm is obtained, and the depth of each pixel in the environmental information is identified by the preset depth estimation algorithm to obtain a depth map of each pixel. The preset depth estimation algorithm may be a camera parameter derivation method. By identifying the depth of each pixel in the environmental information by the preset depth estimation algorithm, the depth map of each pixel can be accurately obtained.

[0046] In some embodiments, the method of obtaining corresponding semantic information according to environmental information can be: obtaining a preset semantic segmentation algorithm, dividing the environmental information into an operating area and a non-operating area by the preset semantic segmentation algorithm, and obtaining semantic information, wherein the semantic information includes pixels located in the operating area and pixels located outside the operating area. The preset semantic segmentation algorithms include U-Net, DeepLab V1, DeepLab V2, DeepLabv3, UNet++, PSPNet, and the like.

[0047] In some embodiments, the second position of each pixel in the rigid coordinate system is determined based on the first position of each pixel in the semantic information in the camera coordinate system, the depth of each pixel in the depth information, the first transformation matrix from the camera coordinate system to the rigid coordinate system of the autonomous mobile device, and a preset intrinsic parameter matrix; a preset initial grid map is obtained; and each grid in the initial grid map is marked as a first grid or a second grid based on the second position of each pixel in the rigid coordinate system and the second transformation matrix from the rigid coordinate system to the grid coordinate system to obtain a target grid map.

[0048] It should be noted that the preset initial grid map is an initial grid map set in advance according to a preset scale based on the shape of the environment. The preset scale can be set according to actual conditions, and the embodiment of the present invention does not specifically limit this. For example, the shape of the environment is a square area of ​​5m*5m, and the preset scale is 100:1. The initial grid map of the shape of the environment of 5m*5m is constructed at the preset scale of 100:1, and an initial grid map of 5cm*5cm is obtained.

[0049] In some embodiments, each grid in the initial grid map is marked as the first grid or the second grid according to the second position of each pixel point in the rigid coordinate system and the second transformation matrix from the rigid coordinate system to the grid coordinate system, and the target grid map can be obtained by: multiplying the second position of each pixel point in the rigid coordinate system by the second transformation matrix to obtain the third position of each pixel point in the initial grid map; and marking each grid in the initial grid map as the first grid or the second grid according to the third position of each pixel point in the initial grid map to obtain the target grid map.

[0050] Exemplarily, the first position of the pixel point in the camera coordinate system according to the semantic information is p c =(u,v,1), where u represents the horizontal coordinate and v represents the vertical coordinate; the depth of the pixel is d; the first transformation matrix is ​​T; the preset internal parameter matrix is ​​K; for the depth d and the first position p c =(u,v,1), the first transformation matrix T and the inverse matrix of the internal parameter matrix K are multiplied to obtain the second position of the pixel point in the rigid body coordinate system, that is, to obtain the preset first calculation formula, which is p b =dTp c K -1 , where p b For the second position, p c is the first position, d is the depth, T is the first transformation matrix, K -1 is the inverse matrix of the internal parameter matrix. Get the preset second calculation formula, which is p o =T2p b , where p o is the third position, T2 is the second transformation matrix, p b is the second position, based on the preset second calculation formula p o =T2p b , for the second position p b Multiply it with the second transformation matrix T2 to get the third position p of the pixel in the initial grid map o , based on the third position p oEach grid in the initial grid map is marked as the first grid or the second grid, and a target grid map is obtained.

[0051] In some embodiments, marking each grid in the initial grid map as a first grid or a second grid, and obtaining a target grid map includes: obtaining the type of object represented by each grid in the environment, and marking each grid as a first grid and a second grid according to the type of each grid, to obtain the target grid map.

[0052] For example, Figure 3 The initial grid map is shown. According to the first position of each pixel in the camera coordinate system in the semantic information, the depth of each pixel in the depth information, the first transformation matrix from the camera coordinate system to the rigid coordinate system of the autonomous mobile device, and the preset internal parameter matrix, the second position of each pixel in the rigid coordinate system is determined; according to the second position of each pixel in the rigid coordinate system and the second transformation matrix from the rigid coordinate system to the grid coordinate system, each grid in the initial grid map is marked as the first grid or the second grid, and the following is obtained: Figure 4 The target grid map shown in FIG. Grid area 11 represents grass, grid area 12 represents puddles, and grid area 13 represents stones.

[0053] Step S102: Determine the grid occupied by the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device.

[0054] The current positioning information and the contour information of the autonomous mobile device are obtained, and the grid occupied by the autonomous mobile device in the target grid map is determined according to the current positioning information and the contour information, so as to obtain the occupied grid of the autonomous mobile device in the target grid map.

[0055] For example, Figure 5 As shown, the current positioning information of the autonomous mobile device 14 and the contour information of the autonomous mobile device are obtained, and based on the current positioning information and the contour information, the grid occupied by the autonomous mobile device 14 in the target grid map is determined, and the occupied grid 15 (the grid within the dotted area) of the autonomous mobile device in the target grid map is obtained.

[0056] Step S103: determining the shortest distance between the occupied grid and the second grid, wherein the shortest distance is the distance between the occupied grid and the nearest second grid.

[0057] In some embodiments, the target grid map is binarized so that the cost values ​​corresponding to each first grid are the same, and the cost values ​​corresponding to each second grid are the same; based on the target grid map after the binarization processing, the closest distance between each first grid and the nearest second grid is obtained; based on the closest distance between each first grid and the nearest second grid, the closest distance between the occupied grid and the second grid is determined.

[0058] In some embodiments, the target grid map is binarized so that the cost values ​​corresponding to each first grid are the same and the cost values ​​corresponding to each second grid are the same. The method can be: each first grid in the target grid map is marked with a first color, and each second grid in the target grid map is marked with a second color, so that the cost values ​​corresponding to each first grid are the same, and the cost values ​​corresponding to each second grid are the same.

[0059] For example, Figure 4 The grass in grid area 11 is marked as white, and the puddle in grid area 12 and the stone in grid area 13 are marked as black, so that Figure 6 The target grid map shown.

[0060] In some embodiments, in the target grid map after binarization, the distance between each first grid and each second grid is calculated to obtain the distance from each first grid to each second grid; the closest distance between each first grid and the nearest second grid is obtained; the target position of the occupied grid of the autonomous mobile device in the first grid is obtained, and the closest distance from the first grid to the nearest second grid of each target position is obtained to obtain at least one closest distance; the smallest closest distance is determined as the target closest distance between the occupied grid and the second grid.

[0061] It should be noted that the method for calculating the distance between each first grid and each second grid can be selected according to actual conditions, and the embodiment of the present invention does not specifically limit this. For example, the method for calculating the distance can be Euclidean distance. Exemplarily, the center point of each grid is used as a reference point for distance calculation, and the Euclidean distance between each first grid and the nearest second grid is calculated to obtain the nearest distance between each first grid and the second grid.

[0062] In some embodiments, based on the current positioning information of the autonomous mobile device and the outline information of the autonomous mobile device, the grid occupied by at least one point on the outline of the autonomous mobile device is determined as the occupied grid. By using the grid occupied by at least one point on the outline of the autonomous mobile device as the occupied grid, the occupied grid can be quickly determined.

[0063] For example, Figure 7As shown, grid area 21 is a first grid, grid area 22 is a second grid; a grid 23 (grid within a dotted frame) occupied by at least one point on the outline of the autonomous mobile device 20 is determined as an occupied grid.

[0064] In some embodiments, if there are multiple occupied grids, the smallest closest distance among the closest distances corresponding to the multiple occupied grids is determined as the target closest distance; and the autonomous mobile device is controlled according to a control strategy that matches the target closest distance. When there are multiple grids occupied by the autonomous mobile device, the smallest closest distance is determined as the target closest distance, so that the closest distance can be determined more accurately.

[0065] For example, Figure 8 As shown, grid area 21 is the first grid, grid area 22 is the second grid; the grid area 23 (in the dotted box) occupied by the outline of the autonomous mobile device 20 includes occupied grid a, occupied grid b, occupied grid c, occupied grid d, occupied grid e and occupied grid f, wherein the minimum distance between the occupied grid a and the second grid is the smallest closest distance among all the closest distances, and the minimum distance between the occupied grid a and the second grid is determined as the target closest distance.

[0066] Step S104: controlling the autonomous mobile device according to a control strategy matching the shortest distance, so that the autonomous mobile device operates within the operating area.

[0067] The control strategy includes but is not limited to a stop operation strategy, a deceleration operation strategy and a maintain current operation strategy.

[0068] In some embodiments, if the closest distance is zero, the operation control strategy of the autonomous mobile device is determined to be a stop operation strategy, and the autonomous mobile device is controlled to stop the operation; if the closest distance is less than or equal to the preset distance, the operation control strategy of the autonomous mobile device is determined to be a deceleration operation strategy, and the autonomous mobile device is controlled to decelerate the operation; if the closest distance is greater than the preset distance, the operation control strategy of the autonomous mobile device is determined to be a maintain current operation strategy, and the autonomous mobile device is controlled to maintain the current operation. Among them, the preset distance can be set according to actual conditions, and the implementation of the present invention does not make specific limitations on this. The autonomous mobile device is controlled according to the control strategy that matches the closest distance to ensure that the autonomous mobile device is operating in the operating area, which greatly improves the intelligence of the autonomous mobile device.

[0069] Exemplarily, the autonomous mobile device is a lawn mowing robot. If the closest distance between the grid occupied by the lawn mowing robot and the second grid is zero, it is determined that the lawn mowing robot is in a non-operating area. If the non-operating area is a stone area, based on the closest distance being zero, the operation control strategy of the autonomous mobile device is determined to be a stop operation strategy, and the lawn mowing robot is controlled to stop operating, which can effectively protect the blades of the lawn mowing robot.

[0070] Exemplarily, the autonomous mobile device is a lawn mowing robot. If the closest distance between the grid occupied by the lawn mowing robot and the second grid is less than or equal to a preset distance, it is determined that the lawn mowing robot may be heading towards a non-operating area. Based on the fact that the closest distance is less than or equal to the preset distance, the operating control strategy of the autonomous mobile device is determined to be a deceleration operating strategy, and the lawn mowing robot is controlled to decelerate its operation, which can effectively prevent the lawn mowing robot from rushing out of the operating area.

[0071] The control method of the autonomous mobile device provided in the above embodiment obtains a target grid map of the environment in which the autonomous mobile device is located, the target grid map includes a first grid located in the working area and a second grid located outside the working area; determines the occupied grid of the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device; determines the closest distance between the occupied grid and the second grid, the closest distance being the distance between the occupied grid and the closest second grid; controls the autonomous mobile device according to a control strategy matching the closest distance, so that the autonomous mobile device operates in the working area. In the present application, a target grid map including a first grid located in the working area and a second grid located outside the working area is first established, and the occupied grid of the autonomous mobile device in the target grid map is determined, and then a control strategy is matched based on the closest distance between the occupied grid and the second grid, and the autonomous mobile device is controlled according to the control strategy, which can accurately ensure that the autonomous mobile device is operating in the working area, greatly improving the safety of the operation of the autonomous mobile device.

[0072] See also Fig. 9 , Fig. 9 A schematic block diagram of a controller provided in an embodiment of the present application

[0073] like Fig. 9 As shown, the controller 200 includes a processor 202 and a memory 203 connected via a system bus 201, wherein the memory 203 may include a storage medium and an internal memory.

[0074] The storage medium may store a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor 202 may execute any control method of an autonomous mobile device.

[0075] The processor 202 is used to provide computing and control capabilities to support the operation of the entire controller.

[0076] The internal memory provides an environment for the operation of the computer program in the storage medium. When the computer program is executed by the processor, the processor can execute any control method of the autonomous mobile device.

[0077] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the controller to which the scheme of the present application is applied. The specific controller may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0078] It should be understood that the processor 202 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0079] In one embodiment, the processor 202 is used to run a computer program stored in the memory to implement the following steps:

[0080] Acquire a target grid map of the environment in which the autonomous mobile device is located, wherein the target grid map includes a first grid located in an operating area and a second grid located outside the operating area;

[0081] Determining the grid occupied by the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device;

[0082] Determine the shortest distance between the occupied grid and the second grid, the shortest distance being the distance between the occupied grid and the nearest second grid;

[0083] The autonomous mobile device is controlled according to a control strategy matching the shortest distance, so that the autonomous mobile device operates in the operating area.

[0084] In one embodiment, when the processor 202 implements the acquisition of the target grid map of the environment in which the autonomous mobile device is located, it is used to implement:

[0085] During the movement of the autonomous mobile device, environmental information of the environment in which the autonomous mobile device is located is collected by a sensor, wherein the sensor is mounted on the autonomous mobile device;

[0086] The target grid map is constructed according to the environmental information.

[0087] In one embodiment, when implementing the construction of the target grid map according to the environmental information, the processor 202 is used to implement:

[0088] Obtaining corresponding semantic information and depth information according to the environmental information;

[0089] The semantic information and the depth information are mapped to an initial grid map to obtain the target grid map, each grid in the target grid map corresponds to a cost value, the cost value is used to represent the type of object in the environment, and the first grid and the second grid are determined based on the cost value corresponding to each grid.

[0090] In one embodiment, the processor 202, when implementing the determining of the shortest distance between the occupied grid and the second grid, is configured to implement:

[0091] Binarization is performed on the target grid map so that the cost values ​​corresponding to the first grids are the same and the cost values ​​corresponding to the second grids are the same;

[0092] According to the target grid map after binarization, the closest distance between each first grid and the nearest second grid is obtained;

[0093] The shortest distance between the occupied grid and the second grid is determined according to the shortest distance between each of the first grids and the second grid.

[0094] In one embodiment, when the processor 202 determines the grid occupied by the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device, it is configured to implement:

[0095] According to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device, a grid occupied by at least one point on the contour of the autonomous mobile device is determined as an occupied grid.

[0096] In one embodiment, when the processor 202 implements the control of the autonomous mobile device according to the control strategy matching the closest distance, it is configured to implement:

[0097] If there are multiple occupied grids, the smallest closest distance among the closest distances corresponding to the multiple occupied grids is determined as the target closest distance;

[0098] The autonomous mobile device is controlled according to a control strategy that matches the closest distance to the target.

[0099] In one embodiment, when the processor 202 implements the control of the autonomous mobile device according to the control strategy matching the closest distance, it is configured to implement:

[0100] If the closest distance is zero, determining that the operation control strategy of the autonomous mobile device is a stop operation strategy, and controlling the autonomous mobile device to stop the operation;

[0101] If the closest distance is less than or equal to the preset distance, determining that the operation control strategy of the autonomous mobile device is a deceleration operation strategy, and controlling the autonomous mobile device to decelerate the operation;

[0102] If the shortest distance is greater than a preset distance, the operation control strategy of the autonomous mobile device is determined to be a strategy for maintaining the current operation, and the autonomous mobile device is controlled to maintain the current operation.

[0103] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the controller described above can refer to the corresponding process in the aforementioned control method embodiment of the autonomous mobile device, and will not be repeated here.

[0104] See also Fig.10 , Fig.10 A schematic block diagram of the structure of an autonomous mobile device provided in an embodiment of the present application.

[0105] like Fig.10 As shown, the autonomous mobile device 300 includes a processor 302 and a memory 303 connected via a system bus 301 , wherein the memory may include a storage medium and an internal memory.

[0106] The storage medium may store a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor 302 may execute any control method for an autonomous mobile device.

[0107] The processor 302 is used to provide computing and control capabilities to support the operation of the entire autonomous mobile device.

[0108] The internal memory provides an environment for the operation of the computer program in the storage medium. When the computer program is executed by the processor, the processor can execute any control method of the autonomous mobile device.

[0109] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the autonomous mobile device to which the scheme of the present application is applied. The specific autonomous mobile device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0110] It should be understood that the processor 302 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0111] In one embodiment, the processor 302 is used to run a computer program stored in the memory to implement the following steps:

[0112] Acquire a target grid map of the environment in which the autonomous mobile device is located, wherein the target grid map includes a first grid located in an operating area and a second grid located outside the operating area;

[0113] Determining the grid occupied by the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device;

[0114] Determine the shortest distance between the occupied grid and the second grid, the shortest distance being the distance between the occupied grid and the nearest second grid;

[0115] The autonomous mobile device is controlled according to a control strategy matching the shortest distance, so that the autonomous mobile device operates in the operating area.

[0116] In one embodiment, when the processor 302 implements the acquisition of the target grid map of the environment in which the autonomous mobile device is located, it is used to implement:

[0117] During the movement of the autonomous mobile device, environmental information of the environment in which the autonomous mobile device is located is collected by a sensor, wherein the sensor is mounted on the autonomous mobile device;

[0118] The target grid map is constructed according to the environmental information.

[0119] In one embodiment, when implementing the construction of the target grid map according to the environmental information, the processor 302 is used to implement:

[0120] Obtaining corresponding semantic information and depth information according to the environmental information;

[0121] The semantic information and the depth information are mapped to an initial grid map to obtain the target grid map, each grid in the target grid map corresponds to a cost value, the cost value is used to represent the type of object in the environment, and the first grid and the second grid are determined based on the cost value corresponding to each grid.

[0122] In one embodiment, the processor 302, when implementing the determining of the shortest distance between the occupied grid and the second grid, is configured to implement:

[0123] Binarization is performed on the target grid map so that the cost values ​​corresponding to the first grids are the same and the cost values ​​corresponding to the second grids are the same;

[0124] According to the target grid map after binarization, the closest distance between each first grid and the nearest second grid is obtained;

[0125] The shortest distance between the occupied grid and the second grid is determined according to the shortest distance between each of the first grids and the second grid.

[0126] In one embodiment, when the processor 302 determines the grid occupied by the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device, it is configured to implement:

[0127] According to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device, a grid occupied by at least one point on the contour of the autonomous mobile device is determined as an occupied grid.

[0128] In one embodiment, when the processor 302 implements the control of the autonomous mobile device according to the control strategy matching the closest distance, it is configured to implement:

[0129] If there are multiple occupied grids, the smallest closest distance among the closest distances corresponding to the multiple occupied grids is determined as the target closest distance;

[0130] The autonomous mobile device is controlled according to a control strategy that matches the closest distance to the target.

[0131] In one embodiment, when the processor 302 implements the control of the autonomous mobile device according to the control strategy matching the closest distance, it is configured to implement:

[0132] If the closest distance is zero, determining that the operation control strategy of the autonomous mobile device is a stop operation strategy, and controlling the autonomous mobile device to stop the operation;

[0133] If the closest distance is less than or equal to the preset distance, determining that the operation control strategy of the autonomous mobile device is a deceleration operation strategy, and controlling the autonomous mobile device to decelerate the operation;

[0134] If the shortest distance is greater than a preset distance, the operation control strategy of the autonomous mobile device is determined to be a strategy for maintaining the current operation, and the autonomous mobile device is controlled to maintain the current operation.

[0135] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the autonomous mobile device described above can refer to the corresponding process in the aforementioned control method embodiment of the autonomous mobile device, and will not be repeated here.

[0136] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the control method of the autonomous mobile device of the present application.

[0137] The computer-readable storage medium may be an internal storage unit of the controller and / or the autonomous mobile device described in the foregoing embodiments, such as a hard disk or memory of the controller and / or the autonomous mobile device. The computer-readable storage medium may be non-volatile or volatile. The computer-readable storage medium may also be an external storage device of the controller and / or the autonomous mobile device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the controller and / or the autonomous mobile device.

[0138] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0139] It should also be understood that the term "and / or" used in the present specification refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0140] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific implementation mode of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application.

Claims

1. A control method for an autonomous mobile device, characterized in that: include: Acquire a target grid map of the environment in which the autonomous mobile device is located, wherein the target grid map includes a first grid located in an operating area and a second grid located outside the operating area; Determining the grid occupied by the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device; Determine the shortest distance between the occupied grid and the second grid, the shortest distance being the distance between the occupied grid and the nearest second grid; The autonomous mobile device is controlled according to a control strategy matching the shortest distance, so that the autonomous mobile device operates in the operating area.

2. The control method of the autonomous mobile device according to claim 1, characterized in that: The step of obtaining a target grid map of the environment in which the autonomous mobile device is located includes: During the movement of the autonomous mobile device, environmental information of the environment in which the autonomous mobile device is located is collected by a sensor, wherein the sensor is mounted on the autonomous mobile device; The target grid map is constructed according to the environmental information.

3. The control method of the autonomous mobile device according to claim 2, characterized in that: The step of constructing the target grid map according to the environmental information includes: Obtaining corresponding semantic information and depth information according to the environmental information; The semantic information and the depth information are mapped to an initial grid map to obtain the target grid map, each grid in the target grid map corresponds to a cost value, the cost value is used to represent the type of object in the environment, and the first grid and the second grid are determined based on the cost value corresponding to each grid.

4. The control method of the autonomous mobile device according to claim 2, characterized in that: The determining the shortest distance between the occupied grid and the second grid comprises: Binarization is performed on the target grid map so that the cost values ​​corresponding to the first grids are the same and the cost values ​​corresponding to the second grids are the same; According to the target grid map after binarization, the closest distance between each first grid and the nearest second grid is obtained; The shortest distance between the occupied grid and the second grid is determined according to the shortest distance between each of the first grids and the second grid.

5. The control method of the autonomous mobile device according to claim 1, characterized in that: The step of determining the grid occupied by the autonomous mobile device in the target grid map according to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device comprises: According to the current positioning information of the autonomous mobile device and the contour information of the autonomous mobile device, a grid occupied by at least one point on the contour of the autonomous mobile device is determined as an occupied grid.

6. The control method of the autonomous mobile device according to claim 1, characterized in that: The controlling the autonomous mobile device according to the control strategy matching the closest distance comprises: If there are multiple occupied grids, the smallest closest distance among the closest distances corresponding to the multiple occupied grids is determined as the target closest distance; The autonomous mobile device is controlled according to a control strategy that matches the closest distance to the target.

7. The control method of an autonomous mobile device according to any one of claims 1 to 6, characterized in that: The controlling the autonomous mobile device according to the control strategy matching the closest distance comprises: If the shortest distance is zero, determining that the operation control strategy of the autonomous mobile device is a stop operation strategy, and controlling the autonomous mobile device to stop the operation; If the closest distance is less than or equal to the preset distance, determining that the operation control strategy of the autonomous mobile device is a deceleration operation strategy, and controlling the autonomous mobile device to decelerate the operation; If the shortest distance is greater than a preset distance, the operation control strategy of the autonomous mobile device is determined to be a strategy for maintaining the current operation, and the autonomous mobile device is controlled to maintain the current operation.

8. A controller, characterized in that: The controller comprises a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the control method of the autonomous mobile device according to any one of claims 1 to 7 are implemented.

9. An autonomous mobile device, characterized in that: The autonomous mobile device comprises a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the control method of the autonomous mobile device according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method for controlling an autonomous mobile device according to any one of claims 1 to 7 are implemented.

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