A navigation control method, device and medium for AGV cluster

Abstract

The present invention provides a navigation control method, device and medium of an AGV cluster, and relates to the technical field of AGVs. The method comprises: obtaining an environmental map where the AGV cluster is located and converting it into a grid map, adopting a leader-follower control strategy to determine a leader and a follower in the AGV cluster, adopting an A* algorithm to determine a planned path of the AGV cluster from a starting grid to a destination grid, controlling the AGV cluster to travel along the planned path according to a formation configuration, and determining an obstacle avoidance path based on position information and displacement speed of moving objects around the AGV cluster, so that the AGV cluster continues to travel along the planned path after avoiding surrounding obstacles until it reaches the destination grid. The present invention reasonably configures the AGV cluster, thereby reducing the amount of information collected and the complexity of processing, and determining the travel path of the AGV cluster with the minimum calculation cost, so as to improve the navigation efficiency of the AGV cluster under the premise of avoiding obstacles.

Description

Technical Field

[0001] The present invention relates to the field of AGV technology, but is not limited to it, and in particular to a navigation control method, device and medium for an AGV cluster. Background Art

[0002] With the development of simultaneous positioning and mapping technology, laser radar positioning, image positioning and other methods are becoming more mature in the AGV field. When dealing with complex tasks, due to the low efficiency and small processing volume of a single AGV, it cannot meet actual needs, and AGV is gradually showing a trend of clustering development.

[0003] When an AGV cluster performs tasks in a complex environment, it needs to continuously identify the environment and plan paths. In the existing technology, there is also a way to use AGV clusters to collaboratively perform tasks. However, during the obstacle avoidance and navigation process, there is still a problem of insufficient collaborative efficiency, and the overall navigation efficiency of the AGV cluster is not high.

[0004] Therefore, it is necessary to improve the collaborative efficiency of the existing AGV cluster and to improve the navigation efficiency of the AGV cluster under the premise of obstacle avoidance. Summary of the invention

[0005] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0006] The embodiments of the present invention provide a navigation control method, device and medium for an AGV cluster, which can improve the navigation efficiency of the AGV cluster under the premise of avoiding obstacles.

[0007] In a first aspect, an embodiment of the present invention provides a navigation control method for an AGV cluster, comprising:

[0008] Step S100, obtaining an environment map where an AGV cluster is located; wherein the AGV cluster includes a plurality of AGVs, and each AGV in the AGV cluster is communicatively connected with each other;

[0009] Step S200, converting the environment map into a grid map, and determining the starting grid and the destination grid of the AGV cluster; wherein the starting grid is the grid where the starting point of the AGV cluster is located, and the destination grid is the grid where the end point of the AGV cluster is located;

[0010] Step S300: using a leader-follower control strategy to determine the leader and the follower in the AGV cluster, and after the AGV cluster is configured in a diamond formation, each AGV in the AGV cluster is numbered in sequence;

[0011] Step S400, using the A* algorithm to determine the planned path of the AGV cluster from the starting grid to the destination grid, controlling the AGV cluster to travel along the planned path according to the formation configuration, and determining the corner AGVs in the AGV cluster in real time; wherein the corner AGVs are three AGVs at the corner positions in the forward direction of the AGV cluster;

[0012] Step S500, obtaining distance information and image information collected by each corner point AGV about the surrounding environment, and determining the position information and displacement speed of moving objects around the AGV cluster based on the distance information and image information;

[0013] Step S600: determining an obstacle avoidance path of the AGV cluster based on the position information and displacement speed of moving objects around the AGV cluster, so that the AGV cluster continues to travel along the planned path after avoiding the surrounding obstacles until reaching the destination grid.

[0014] The beneficial effects of the present invention include: the present invention reasonably configures the AGV cluster, obtains obstacle information around the AGV cluster through the AGV at the corner point position, thereby reducing the amount of information collected and the complexity of processing, and determines the driving path of the AGV cluster with the minimum computational cost, which can improve the navigation efficiency of the AGV cluster under the premise of obstacle avoidance.

[0015] In some embodiments, in step S500, obtaining distance information and image information collected by each corner AGV about the surrounding environment, and determining the position information and displacement speed of the moving objects around the AGV cluster based on the distance information and image information, includes:

[0016] Step S510, obtaining distance information and image information collected by the AGV at each corner point;

[0017] Step S520: reconstructing the distance information and image information collected by each corner AGV based on the SLAM algorithm to obtain a three-dimensional point cloud map of the surrounding environment of the AGV cluster;

[0018] Step S530: Match the three-dimensional point cloud image with the grid map to obtain a three-dimensional grid map;

[0019] Step S540: Determine in real time whether there is a moving object in the three-dimensional grid image; if there is a moving object in the three-dimensional grid image, obtain the position information and displacement speed of the moving object.

[0020] In some embodiments, in step S540, determining in real time whether there is a moving object in the three-dimensional grid image, and if there is a moving object in the three-dimensional grid image, obtaining the position information and displacement speed of the moving object includes:

[0021] Step S541, obtaining a three-dimensional grid image of an initial frame;

[0022] Step S542: filtering the point cloud in the three-dimensional grid image of the initial frame to filter out the point cloud located on the wall;

[0023] Step S543: performing plane detection on the remaining point clouds in the three-dimensional grid image of the initial frame by using a random consistency algorithm to remove the point clouds located on the ground, and using the remaining point clouds in the three-dimensional grid image of the initial frame as a background point cloud image;

[0024] Step S544, estimating the optical flow motion vector of the background point cloud image by the optical flow method, and determining the background point cloud image in the three-dimensional grid image of the current frame based on the optical flow motion vector of the background point cloud image;

[0025] Step S545, taking the point cloud data other than the background point cloud image in the three-dimensional grid image of the current frame as the feature point cloud;

[0026] Step S546, extracting feature points of the moving object from the feature point cloud using the Euclidean clustering point cloud segmentation algorithm;

[0027] Step S547, estimating the optical flow motion vector of the feature point by using the optical flow method, and converting the optical flow motion vector of the feature point into a theoretical displacement of the moving object in the world coordinate system;

[0028] Step S548: determine the position information of the moving object based on the three-dimensional grid image of the current frame, and determine the displacement speed of the moving object based on the theoretical displacement of the moving object in the world coordinate system.

[0029] In some embodiments, in step S600, determining the obstacle avoidance path of the AGV cluster based on the position information and displacement speed of the moving objects around the AGV cluster so that the AGV cluster continues to travel along the planned path after avoiding the surrounding obstacles until reaching the destination grid includes:

[0030] Step S610, obtaining the movement direction of each corner AGV in the AGV cluster;

[0031] Step S620, determining the angles between the moving object and each of the corner point AGVs based on the moving direction of each of the corner point AGVs and the displacement speed of the moving object, and taking the angle with the smallest angle value as the characteristic angle;

[0032] Step S630, determining the value range of the characteristic angle, if the value range of the characteristic angle is 0° to 90°, executing step S640; if the value range of the characteristic angle is 90° to 180°, executing step S680;

[0033] Step S640, determining whether the shortest distance from the AGV cluster to the moving object is greater than the safety distance, if not, executing step S650; if yes, executing step S670;

[0034] Step S650, obtaining the displacement speed of the AGV cluster, comparing the displacement speed of the AGV cluster with the displacement speed of the moving object, if the displacement speed of the AGV cluster is greater than the displacement speed of the moving object, executing step S660; otherwise, executing step S670;

[0035] Step S660: Control the displacement speed of the AGV cluster to be less than the displacement speed of the moving object, determine in real time the grid corresponding to the planned path and the grid with the farthest distance from the moving object, and use the grid as the avoidance grid of the AGV cluster;

[0036] Step S670, using a dynamic window method to determine the planned path of the AGV cluster from the current grid to the avoidance grid, controlling the AGV cluster to travel to the avoidance grid according to the planned path, and then executing step S680;

[0037] Step S680: Control the AGV cluster to continue traveling along the planned path until it reaches the destination grid.

[0038] In a second aspect, an embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the navigation control method for the AGV cluster as described in the first aspect is implemented.

[0039] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the navigation control method of the AGV cluster as described in the first aspect.

[0040] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0042] Figure 1 It is a flowchart of a navigation control method of an AGV cluster in an embodiment of the present invention;

[0043] Figure 2 It is a structural diagram of an electronic device provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] It should be noted that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than in the flowchart. The terms "first", "second", etc. in the specification, claims or the above drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0046] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0047] like Figure 1 As shown, Figure 1 The flowchart of a navigation control method of an AGV cluster provided by an embodiment of the present invention includes but is not limited to the following steps:

[0048] Step S100, obtaining an environment map where an AGV cluster is located; wherein the AGV cluster includes a plurality of AGVs, and each AGV in the AGV cluster is communicatively connected with each other;

[0049] Step S200, converting the environment map into a grid map, and determining the starting grid and the destination grid of the AGV cluster; wherein the starting grid is the grid where the starting point of the AGV cluster is located, and the destination grid is the grid where the end point of the AGV cluster is located;

[0050] Step S300: using a leader-follower control strategy to determine the leader and the follower in the AGV cluster, and after the AGV cluster is configured in a diamond formation, each AGV in the AGV cluster is numbered in sequence;

[0051] It should be noted that, in some embodiments, an AGV is selected as the navigator in the AGV cluster, and the other AGVs in the AGV cluster are followers, and the behavior of the navigator determines the behavior of the followers. The use of the leader-follower control strategy can ensure the team shape of the AGV cluster during driving, and each AGV in the AGV cluster is numbered as an individual, and the numbering order is from top to bottom and from left to right. Since the behavior of the remaining AGVs in the AGV cluster except the leading AGV depends on the AGV as the navigator, when the AGV as the navigator fails, the next AGV is selected as the navigator according to the sequence number, and the failed AGV is treated as an obstacle for obstacle avoidance.

[0052] Step S400, using the A* algorithm to determine the planned path of the AGV cluster from the starting grid to the destination grid, controlling the AGV cluster to travel along the planned path according to the formation configuration, and determining the corner AGVs in the AGV cluster in real time; wherein the corner AGVs are three AGVs at the corner positions in the forward direction of the AGV cluster;

[0053] It should be noted that after the corner AGV in the AGV cluster is determined, the distance information and image information of the surrounding environment of the corner AGV is obtained in a targeted manner. Since the formation configuration of the AGV cluster is a rhombus, during the driving process of the AGV cluster, the three corners of the rhombus serve as corner points, and the three AGVs at the corner point position are the corner AGVs.

[0054] Step S500, obtaining distance information and image information collected by each corner point AGV about the surrounding environment, and determining the position information and displacement speed of moving objects around the AGV cluster based on the distance information and image information;

[0055] In this step, the AGV collects the distance information of the surrounding environment in real time by carrying a laser radar, and collects the image information of the surrounding environment in real time by carrying a camera; obtains the posture information of the AGV through an inertial navigation device, and obtains the location information of the AGV through a GPS positioning device.

[0056] Step S600: determining an obstacle avoidance path of the AGV cluster based on the position information and displacement speed of moving objects around the AGV cluster, so that the AGV cluster continues to travel along the planned path after avoiding the surrounding obstacles until reaching the destination grid.

[0057] In this step, after the obstacle avoidance path is determined, the grid that each corner AGV takes next is obtained, and the AGV cluster performs autonomous navigation and obstacle avoidance according to the planned route.

[0058] The present invention configures the AGV cluster reasonably and obtains obstacle information around the AGV cluster through AGVs at corner positions, thereby reducing the amount of information collected and the complexity of processing, and determining the driving path of the AGV cluster with the minimum computational cost, so as to maximize the collaborative efficiency of multiple AGV path navigation and improve the navigation efficiency of the AGV cluster under the premise of obstacle avoidance.

[0059] In addition, in one embodiment, Figure 1 In step S500 of the illustrated embodiment, the step of obtaining the distance information and image information collected by each corner AGV on the surrounding environment, and determining the position information and displacement speed of the moving objects around the AGV cluster based on the distance information and image information, includes:

[0060] Step S510, obtaining distance information and image information collected by the AGV at each corner point;

[0061] Specifically, the distance information collected by the radar sensor and the image information collected by the camera are obtained, and a local map is established based on the environmental data of the distance information and the image information.

[0062] Step S520: reconstructing the distance information and image information collected by each corner AGV based on the SLAM algorithm to obtain a three-dimensional point cloud map of the surrounding environment of the AGV cluster;

[0063] In one embodiment, based on the SLAM algorithm, three-dimensional point cloud reconstruction is performed on the distance information and image information collected by each corner point AGV to obtain a three-dimensional point cloud map corresponding to each corner point AGV, and each three-dimensional point cloud map is spliced ​​to obtain a three-dimensional point cloud map of the surrounding environment of the AGV cluster.

[0064] Step S530: Match the three-dimensional point cloud image with the grid map to obtain a three-dimensional grid map;

[0065] In one embodiment, the three-dimensional point cloud image has distance information of each point cloud. The position information of the point cloud can be determined based on the distance information of the point cloud and the position information of the AGV, thereby determining the position of the point cloud in the three-dimensional grid map, thereby achieving matching between the three-dimensional point cloud image and the grid map.

[0066] Step S540: Determine in real time whether there is a moving object in the three-dimensional grid image; if there is a moving object in the three-dimensional grid image, obtain the position information and displacement speed of the moving object.

[0067] In addition, in one embodiment, in step S540, determining in real time whether there is a moving object in the three-dimensional grid image, and if there is a moving object in the three-dimensional grid image, obtaining the position information and displacement speed of the moving object includes:

[0068] Step S541, obtaining a three-dimensional grid image of an initial frame;

[0069] Step S542: filtering the point cloud in the three-dimensional grid image of the initial frame to filter out the point cloud located on the wall;

[0070] Step S543: performing plane detection on the remaining point clouds in the three-dimensional grid image of the initial frame by using a random consistency algorithm to remove the point clouds located on the ground, and using the remaining point clouds in the three-dimensional grid image of the initial frame as a background point cloud image;

[0071] Step S544, estimating the optical flow motion vector of the background point cloud image by the optical flow method, and determining the background point cloud image in the three-dimensional grid image of the current frame based on the optical flow motion vector of the background point cloud image;

[0072] Step S545, taking the point cloud data other than the background point cloud image in the three-dimensional grid image of the current frame as the feature point cloud;

[0073] Step S546, extracting feature points of the moving object from the feature point cloud using the Euclidean clustering point cloud segmentation algorithm;

[0074] Step S547, estimating the optical flow motion vector of the feature point by using the optical flow method, and converting the optical flow motion vector of the feature point into a theoretical displacement of the moving object in the world coordinate system;

[0075] Step S548: determine the position information of the moving object based on the three-dimensional grid image of the current frame, and determine the displacement speed of the moving object based on the theoretical displacement of the moving object in the world coordinate system.

[0076] In addition, in one embodiment, in step S600, determining the obstacle avoidance path of the AGV cluster based on the position information and displacement speed of the moving objects around the AGV cluster, so that the AGV cluster continues to travel along the planned path after avoiding the surrounding obstacles until reaching the destination grid, includes:

[0077] Step S610, obtaining the movement direction of each corner AGV in the AGV cluster;

[0078] Step S620, determining the angles between the moving object and each of the corner point AGVs based on the moving direction of each of the corner point AGVs and the displacement speed of the moving object, and taking the angle with the smallest angle value as the characteristic angle;

[0079] Step S630, determining the value range of the characteristic angle, if the value range of the characteristic angle is 0° to 90°, executing step S640; if the value range of the characteristic angle is 90° to 180°, executing step S680;

[0080] Step S640, determining whether the shortest distance from the AGV cluster to the moving object is greater than the safety distance, if not, executing step S650; if yes, executing step S670;

[0081] Step S650, obtaining the displacement speed of the AGV cluster, comparing the displacement speed of the AGV cluster with the displacement speed of the moving object, if the displacement speed of the AGV cluster is greater than the displacement speed of the moving object, executing step S660; otherwise, executing step S670;

[0082] Step S660: Control the displacement speed of the AGV cluster to be less than the displacement speed of the moving object, determine in real time the grid corresponding to the planned path and the grid with the farthest distance from the moving object, and use the grid as the avoidance grid of the AGV cluster;

[0083] Step S670, using a dynamic window method to determine the planned path of the AGV cluster from the current grid to the avoidance grid, controlling the AGV cluster to travel to the avoidance grid according to the planned path, and then executing step S680;

[0084] Step S680: Control the AGV cluster to continue traveling along the planned path until it reaches the destination grid.

[0085] In the embodiment provided by the present invention, by determining the characteristic angle between the moving object and the moving direction of the AGV cluster, the magnitude of the displacement speed and the shortest distance, it is ensured that the AGV cluster, when performing navigation control, stays as close to the planned path as possible while avoiding the moving object, thereby improving the navigation efficiency of the AGV cluster.

[0086] In addition, refer to Figure 2 An embodiment of the present invention further provides an electronic device 10, which includes: a memory 11, a processor 12, and a computer program stored in the memory 11 and executable on the processor 12.

[0087] The processor 12 and the memory 11 may be connected via a bus or other means.

[0088] The non-transient software program and instructions required to implement the navigation control method for the AGV cluster in the above embodiment are stored in the memory 11 , and when executed by the processor 12 , the navigation control method for the AGV cluster in the above embodiment is executed.

[0089] In addition, an embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor or controller, for example, by a processor in the above-mentioned electronic device embodiment, so that the above-mentioned processor can execute the navigation control method of the AGV cluster in the above-mentioned embodiment.

[0090] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0091] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions without violating the spirit of the present invention. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A navigation control method for an AGV cluster, characterized in that: include: Step S100, obtaining an environment map where an AGV cluster is located; wherein the AGV cluster includes a plurality of AGVs, and each AGV in the AGV cluster is communicatively connected with each other; Step S200, converting the environment map into a grid map, and determining the starting grid and the destination grid of the AGV cluster; wherein the starting grid is the grid where the starting point of the AGV cluster is located, and the destination grid is the grid where the end point of the AGV cluster is located; Step S300: using a leader-follower control strategy to determine the leader and the follower in the AGV cluster, and after the AGV cluster is configured in a diamond formation, each AGV in the AGV cluster is numbered in sequence; Step S400, using the A* algorithm to determine the planned path of the AGV cluster from the starting grid to the destination grid, controlling the AGV cluster to travel along the planned path according to the formation configuration, and determining the corner AGVs in the AGV cluster in real time; wherein the corner AGVs are three AGVs at the corner positions in the forward direction of the AGV cluster; Step S500, obtaining distance information and image information collected by each corner point AGV about the surrounding environment, and determining the position information and displacement speed of moving objects around the AGV cluster based on the distance information and image information; Step S600: determining an obstacle avoidance path of the AGV cluster based on the position information and displacement speed of moving objects around the AGV cluster, so that the AGV cluster continues to travel along the planned path after avoiding the surrounding obstacles until reaching the destination grid; In step S500, the distance information and image information collected by each corner AGV about the surrounding environment are obtained, and the position information and displacement speed of the moving objects around the AGV cluster are determined based on the distance information and image information, including: Step S510, obtaining distance information and image information collected by the AGV at each corner point; Step S520: reconstructing the three-dimensional point cloud of the distance information and image information collected by each corner AGV based on the SLAM algorithm to obtain a three-dimensional point cloud map of the surrounding environment of the AGV cluster; Step S530: Match the three-dimensional point cloud image with the grid map to obtain a three-dimensional grid map; Step S540: determining in real time whether there is a moving object in the three-dimensional grid image, and if there is a moving object in the three-dimensional grid image, obtaining position information and displacement speed of the moving object; In step S540, it is determined in real time whether there is a moving object in the three-dimensional grid image. If there is a moving object in the three-dimensional grid image, the position information and displacement speed of the moving object are obtained, including: Step S541, obtaining a three-dimensional grid image of an initial frame; Step S542: filtering the point cloud in the three-dimensional grid image of the initial frame to filter out the point cloud located on the wall; Step S543: performing plane detection on the remaining point clouds in the three-dimensional grid image of the initial frame by using a random consistency algorithm to remove the point clouds located on the ground, and using the remaining point clouds in the three-dimensional grid image of the initial frame as a background point cloud image; Step S544, estimating the optical flow motion vector of the background point cloud image by the optical flow method, and determining the background point cloud image in the three-dimensional grid image of the current frame based on the optical flow motion vector of the background point cloud image; Step S545, taking the point cloud data other than the background point cloud image in the three-dimensional grid image of the current frame as the feature point cloud; Step S546, extracting feature points of the moving object from the feature point cloud using the Euclidean clustering point cloud segmentation algorithm; Step S547, estimating the optical flow motion vector of the feature point by using the optical flow method, and converting the optical flow motion vector of the feature point into a theoretical displacement of the moving object in the world coordinate system; Step S548: determine the position information of the moving object based on the three-dimensional grid image of the current frame, and determine the displacement speed of the moving object based on the theoretical displacement of the moving object in the world coordinate system.

2. The navigation control method of the AGV cluster according to claim 1, characterized in that: In step S600, the obstacle avoidance path of the AGV cluster is determined based on the position information and displacement speed of the moving objects around the AGV cluster, so that the AGV cluster continues to travel along the planned path after avoiding the surrounding obstacles until reaching the destination grid, including: Step S610, obtaining the movement direction of each corner AGV in the AGV cluster; Step S620, determining the angles between the moving object and each of the corner point AGVs based on the moving direction of each of the corner point AGVs and the displacement speed of the moving object, and taking the angle with the smallest angle value as the characteristic angle; Step S630, determining the value range of the characteristic angle, if the value range of the characteristic angle is 0° to 90°, executing step S640; if the value range of the characteristic angle is 90° to 180°, executing step S680; Step S640, determining whether the shortest distance from the AGV cluster to the moving object is greater than the safety distance, if not, executing step S650; if yes, executing step S670; Step S650, obtaining the displacement speed of the AGV cluster, comparing the displacement speed of the AGV cluster with the displacement speed of the moving object, if the displacement speed of the AGV cluster is greater than the displacement speed of the moving object, executing step S660; otherwise, executing step S670; Step S660: Control the displacement speed of the AGV cluster to be less than the displacement speed of the moving object, determine in real time the grid corresponding to the planned path and the grid with the farthest distance from the moving object, and use the grid as the avoidance grid of the AGV cluster; Step S670, using a dynamic window method to determine the planned path of the AGV cluster from the current grid to the avoidance grid, controlling the AGV cluster to travel to the avoidance grid according to the planned path, and then executing step S680; Step S680: Control the AGV cluster to continue traveling along the planned path until it reaches the destination grid.

3. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the navigation control method for an AGV cluster as described in any one of claims 1 to 2 when executing the computer program.

4. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are used to execute the navigation control method of the AGV cluster as described in any one of claims 1 to 2.

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