Method and device for improving artificial potential field of cluster based on regional threat degree

Abstract

The embodiment of the invention discloses a cluster artificial potential field improvement method and device based on a regional threat degree. The method comprises the following steps: acquiring the position of a static obstacle; establishing a multi-agent cluster structure, and forming following by adopting a second-order consistency algorithm; when the moving queue is an expected queue, obstacle avoidance control is carried out through an artificial potential field method, and an overall following path of the cluster is obtained; according to the obstacle avoidance path, performing path following by adopting a second-order consistency algorithm so as to control the cluster to avoid the obstacle and arrive at the target point; second obstacle position information and threat weight information are introduced, and the size and direction of repulsive force borne by the cluster are adjusted; by introducing position information and threat weight information of obstacles in a farther range, the size and the direction of repulsive force borne by the cluster are adjusted, so that the movement direction of the cluster is changed, and falling into a local minimum trap area is avoided.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of obstacle avoidance path, and in particular, to an improved method and device for swarm artificial potential field based on regional threat degree. Background Art

[0002] When the unmanned system simulates combat or conducts actual combat, it is necessary to first analyze and calculate the known environmental threat situation and make targeted predictive avoidance, so as to avoid being attacked in high-threat areas and falling into local minima and the problem of target unreachability. Summary of the Invention

[0003] The embodiments of the present application provide an improved method and device for swarm artificial potential field based on regional threat degree, so as to achieve obstacle avoidance path planning and solve the problem of local optimality by pre-sensing and predicting the avoidance of obstacles.

[0004] In a first aspect, the embodiments of the present application provide an improved method for swarm artificial potential field based on regional threat degree, and the method includes the following steps:

[0005] Obtain the positions of static obstacles;

[0006] Establish a multi-agent swarm structure and form a following using the second-order consensus algorithm;

[0007] When the moving queue is the desired queue, perform obstacle avoidance control by the artificial potential field method to obtain the overall following path of the swarm;

[0008] According to the obstacle avoidance path, perform path following using the second-order consensus algorithm to control the swarm to avoid obstacles and reach the target point;

[0009] By introducing the second obstacle position information and threat weight information, adjust the magnitude and direction of the repulsive force received by the swarm.

[0010] Further, the performing obstacle avoidance control by the artificial potential field method when the moving queue is the desired queue includes:

[0011] Define the first target as q0 and the second target as q i , i = 0, 1…, n;

[0012] Define the distance between the first agent and the second target as:

[0013]

[0014] Perform the first target following and formation maintenance according to the consensus protocol to obtain the input:

[0015]

[0016] In the formula, k x and k h are weight coefficients, a ij is an adjacency matrix element, and Δ ij is the expected distance between the first agent and the second agent;

[0017] The artificial potential field method is used for the path of the first target in the cluster;

[0018] In the artificial potential field method, the repulsive force field is:

[0019]

[0020] In the formula, d is the distance between two points, q is the position of the first target, and d Qmax is the repulsive force coefficient, and λ r is the repulsive force gain coefficient.

[0021] Furthermore, by introducing the second obstacle position information and threat weight information to adjust the magnitude and direction of the repulsive force received by the cluster, it includes:

[0022] By introducing the second obstacle position information and threat weight information;

[0023] Introduce a threat degree weight matrix to determine the size and threat degree of different obstacles, and calculate the magnitude of the repulsive force of different obstacles on the agent cluster;

[0024] When it is detected that the number of obstacles within the detection range exceeds three, a polygon repulsion strategy is established based on the three obstacles with the largest threat degree weights within the range; among them, the quadrilateral is composed of the first target of the cluster and the three obstacles;

[0025] Among them, the additional repulsive force potential field imposed by the quadrilateral strategy is:

[0026]

[0027] Among them, Q w is the weighted center position of the three obstacles with the largest weights: obstacle A, obstacle B, and obstacle C, and d cmax is the repulsive force coefficient; D1, D2, and D inter are the set identification areas respectively.

[0028] Furthermore, it also includes:

[0029] Based on the design of the quadrilateral repulsive force function in the high-threat distribution area, a double circular shift axis strategy is introduced;

[0030] The shift axis displacement distance d s is:

[0031]

[0032] Wherein, is the offset caused by the i-th obstacle in the double-shifted axis detection range, and σ circle , ε1, ε2, d circle , D M and χ are weighting coefficients.

[0033] Furthermore, it further includes:

[0034] All the first agents involved are modeled as first-order integrator models:

[0035]

[0036] Wherein, q i =[x i y i T is the position of the first agent in the navigation coordinate system, and u i =[u x u y T is the control input received by the first agent, and is also equal to the resultant force F sum , and the definition of F sum is:

[0037]

[0038] Wherein, U sum is the sum of the potential fields received by the first agent, U att is the gravitational potential field brought by the target point, is the sum of the repulsive force fields received, and F sum is the negative gradient of U sum .

[0039] Furthermore, the obtaining of the static obstacle position includes:

[0040] Establish a plane map according to the actual operating environment and mark the positions of static obstacles.

[0041] In a second aspect, an improved device for a swarm artificial potential field based on regional threat degree provided by an embodiment of the present application includes:

[0042] A position acquisition module, configured to acquire the positions of static obstacles;

[0043] A swarm establishment module, configured to establish a multi-agent swarm structure and form following by using a second-order consensus algorithm;

[0044] A path acquisition module, configured to perform obstacle avoidance control by using the artificial potential field method to obtain the overall following path of the swarm when the moving queue is an expected queue; ​​

[0045] An obstacle avoidance control module, configured to perform path following using a second-order consensus algorithm according to an obstacle avoidance path, so as to control the cluster to avoid obstacles and reach a target point;

[0046] A cluster adjustment module, configured to adjust the magnitude and direction of the repulsive force received by the cluster by introducing second obstacle position information and threat weight information.

[0047] In a third aspect, an embodiment of the present application further provides a computer device, including: a memory and one or more processors;

[0048] The memory is configured to store one or more programs;

[0049] When the one or more programs are executed by the one or more processors, the one or more processors implement an improved method for artificial potential field of a cluster based on regional threat degree as described above.

[0050] In a fourth aspect, an embodiment of the present application further provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute an improved method for artificial potential field of a cluster based on regional threat degree as described above when executed by a computer processor.

[0051] In a fifth aspect, an embodiment of the present application provides a computer program product, the computer program product includes instructions, and when the instructions are executed by a computer, the computer implements the method as described above.

[0052] The embodiment of the present application obtains the position of static obstacles; establishes a multi-agent cluster structure and forms a following using a second-order consensus algorithm; when the moving queue is an expected queue, performs obstacle avoidance control by the artificial potential field method to obtain the overall following path of the cluster; according to the obstacle avoidance path, performs path following using a second-order consensus algorithm to control the cluster to avoid obstacles and reach a target point; adjusts the magnitude and direction of the repulsive force received by the cluster by introducing second obstacle position information and threat weight information; adjusts the magnitude and direction of the repulsive force received by the cluster by introducing the position information and threat weight information of obstacles in a farther range, so as to change its moving direction and avoid falling into a local minimum trap area. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flowchart of an improved method for artificial potential field of a cluster based on regional threat degree provided by an embodiment of the present application;

[0054] Figure 2 is a schematic structural diagram of an improved device for artificial potential field of a cluster based on regional threat degree provided by an embodiment of the present application;

[0055] Figure 3It is a schematic structural diagram of a computer device provided by an embodiment of the present application;

[0056] Figure 4 It is a schematic diagram of the attractive force and repulsive force of the artificial potential field method provided by an embodiment of the present application;

[0057] Figure 5 It is a schematic diagram of the problem that the artificial potential field method provided by an embodiment of the present application falls into a local minimum trap;

[0058] Figure 6 It is a schematic diagram of a quadrilateral repulsive force strategy based on an improved artificial potential field method provided by an embodiment of the present application;

[0059] Figure 7 It is a diagram of the specific force direction and action range in the quadrilateral strategy provided by an embodiment of the present application;

[0060] Figure 8 It is a schematic diagram of a double circular shift strategy provided by an embodiment of the present application;

[0061] Figure 9 It is a comparison diagram before and after the shift of the heavy circular shift strategy provided by an embodiment of the present application. Specific embodiments

[0062] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further describes the specific embodiments of the present application in detail with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limiting the present application. Additionally, it should be noted that for the convenience of description, only parts related to the present application are shown in the drawings, rather than all the content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. When the operations are completed, the process can be terminated, but there may also be additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0063] The embodiment of the present application establishes a set of improved methods for cluster artificial potential fields based on regional threat levels to achieve obstacle avoidance path planning and solve the problem of local optimality by pre-sensing and predicting obstacles to avoid them.

[0064] The improved method of swarm artificial potential field based on regional threat degree provided in the embodiments can be executed by an improved device of swarm artificial potential field based on regional threat degree. The improved device of swarm artificial potential field based on regional threat degree can be implemented in a software and / or hardware manner and integrated in an improved device of swarm artificial potential field based on regional threat degree. Among them, the improved device of swarm artificial potential field based on regional threat degree can be a device such as a computer.

[0065] Figure 1 The flowchart of an improved method of swarm artificial potential field based on regional threat degree provided in the embodiments of the present application is shown in Figure 1 , and the method includes the following steps:

[0066] 100. Obtain the positions of static obstacles.

[0067] Optionally, establish a plane map according to the actual operating environment and mark the positions of static obstacles.

[0068] 200. Establish a multi-agent swarm structure and form a following using the second-order consensus algorithm.

[0069] 300. When the moving queue is the desired queue, perform obstacle avoidance control through the artificial potential field method to obtain the overall following path of the swarm.

[0070] 400. According to the obstacle avoidance path, use the second-order consensus algorithm for path following to control the swarm to avoid obstacles and reach the target point.

[0071] 500. By introducing the second obstacle position information and threat weight information, adjust the magnitude and direction of the repulsive force received by the swarm.

[0072] Exemplarily, the first target is the leader, and the second target is the follower. Please refer to Figure 4 , introduce a multi-agent formation, select the leader and the follower, and use the consensus protocol for the follower to track the leader and formation control. When the multi-agent moves in the desired queue, use the improved artificial potential field method to constrain the movement of the leader to obtain the swarm movement path.

[0073] In the improved artificial potential field method, by introducing the threat degree weight matrix of the obstacle, the three obstacles with the highest threat degree are judged to form a quadrilateral, and the quadrilateral repulsive force strategy is added as Figure 6 shown, so that the leader selects a relatively safe route when choosing a road; according to the magnitude and direction of the additional repulsive force calculated by the quadrilateral repulsive force strategy, as Figure 7 shown, obtain the total resultant force received by the swarm during movement, obtain the speed at the next moment, and thus make the swarm move.

[0074] In some embodiments, please refer toFigure 4 , when the moving queue is the desired queue, obstacle avoidance control is performed by the artificial potential field method, including:

[0075] Define the first target as q0 and the second target as q i , i = 0, 1…, n;

[0076] Define the distance between the first agent and the second target as:

[0077]

[0078] Perform first target following and formation maintenance according to the consensus protocol to obtain the input:

[0079]

[0080] In the formula, k x and k h are weight coefficients, a ij is an element of the adjacency matrix, Δ ij is the expected distance between the first agent and the second agent;

[0081] Use the artificial potential field method for the path of the cluster first target;

[0082] The repulsive force field in the artificial potential field method is:

[0083]

[0084] In the formula, d is the distance between two points, q is the position of the first target, d Qmax is the repulsive force coefficient, λ r is the repulsive force gain coefficient.

[0085] In some embodiments, by introducing the second obstacle position information and threat weight information, adjusting the magnitude and direction of the repulsive force received by the cluster, including:

[0086] By introducing the second obstacle position information and threat weight information;

[0087] Introduce the threat degree weight matrix, determine the size and threat degree of different obstacles, and calculate the magnitude of the repulsive force of different obstacles on the agent cluster;

[0088] When it is detected that the number of obstacles within the detection range exceeds three, establish a polygon repulsion strategy according to the three obstacles with the largest threat degree weights within the range; among them, the quadrilateral is composed of the first target of the cluster and the three obstacles;

[0089] Among them, the additional repulsive force potential field applied by the quadrilateral strategy is:

[0090]

[0091] Among them, Q w is the weighted center position of the three obstacles with the largest weights: obstacle A, obstacle B, and obstacle C, and d cmax is the repulsive force coefficient; D1, D2, and D inter are respectively Figure 7 the identification areas of

[0092] In some embodiments, please refer to Figure 8 and Figure 9 , design a quadrilateral repulsive force function based on the high-threat distribution area, and introduce a double circular shift strategy, so as to increase the probability of selecting a trajectory with fewer obstacles during the obstacle avoidance process of the robot, and finally optimize the global path planning.

[0093] The shift displacement distance d s is:

[0094]

[0095] In the formula, is the offset caused by the i-th obstacle in the double shift detection range, and σ circle , ε1, ε2, d circle , D M and χ are weight coefficients.

[0096] In some embodiments, Figure 5 the situation in

[0097] is the local minimum problem that will occur when the traditional artificial potential field method faces a specific environment. The repulsive force and the target attraction force make the intelligent agent stay in place or within a very small range. Using the improved method can solve this problem.

[0098]

[0099] In the formula, q i = [x i y i T is the position of the first intelligent agent in the navigation coordinate system, and u i = [u x u y T is the control input received by the first intelligent agent, and is also equal to the resultant force F sum , and the definition of F sum is:

[0100]

[0101] In the formula, U sum ​​is the sum of the potential fields experienced by the first agent, U att is the gravitational potential field brought by the target point is the sum of the repulsive force fields experienced, F sum is U sum is the negative gradient of

[0102] As described above, the embodiments of the present application obtain the positions of static obstacles; establish a multi-agent cluster structure and use the second-order consensus algorithm to form a following; when the moving queue is the desired queue, perform obstacle avoidance control through the artificial potential field method to obtain the overall following path of the cluster; according to the obstacle avoidance path, use the second-order consensus algorithm for path following to control the cluster to avoid obstacles and reach the target point; by introducing the second obstacle position information and threat weight information, adjust the magnitude and direction of the repulsive force received by the cluster; by introducing the position information and threat weight information of obstacles within a farther range, adjust the magnitude and direction of the repulsive force received by the cluster, thereby changing its moving direction and avoiding falling into the local minimum trap area; through the pre-perception and prediction of obstacles for avoidance, achieve the path planning of obstacle avoidance and solve the problem of local optimality at the same time.

[0103] Based on the above embodiments, please refer to Figure 2 A cluster artificial potential field improvement device based on regional threat degree provided by the embodiments of the present application specifically includes: a position acquisition module 201, a cluster establishment module 202, a path acquisition module 203, an obstacle avoidance control module 204, and a cluster adjustment module 205.

[0104] Among them, the position acquisition module 201 is used to acquire the positions of static obstacles; the cluster establishment module 202 is used to establish a multi-agent cluster structure and use the second-order consensus algorithm to form a following; the path acquisition module 203 is used to perform obstacle avoidance control through the artificial potential field method when the moving queue is the desired queue to obtain the overall following path of the cluster; the obstacle avoidance control module 204 is used to perform path following using the second-order consensus algorithm according to the obstacle avoidance path to control the cluster to avoid obstacles and reach the target point; the cluster adjustment module 205 is used to adjust the magnitude and direction of the repulsive force received by the cluster by introducing the second obstacle position information and threat weight information.

[0105] As described above, the embodiments of the present application obtain the positions of static obstacles; establish a multi-agent cluster structure and use the second-order consensus algorithm to form following; when the moving queue is the desired queue, perform obstacle avoidance control through the artificial potential field method to obtain the overall following path of the cluster; according to the obstacle avoidance path, use the second-order consensus algorithm to perform path following to control the cluster to avoid obstacles and reach the target point; by introducing the second obstacle position information and threat weight information, adjust the magnitude and direction of the repulsive force received by the cluster; by introducing the position information and threat weight information of obstacles in a farther range, adjust the magnitude and direction of the repulsive force received by the cluster, thereby changing its moving direction and avoiding falling into the local minimum trap area.

[0106] The improved device of the cluster artificial potential field based on regional threat degree provided by the embodiments of the present application can be used to execute the improved method of the cluster artificial potential field based on regional threat degree provided by the above embodiments, and has the corresponding functions and beneficial effects.

[0107] The embodiments of the present application also provide a computer device, which can integrate the improved device of the cluster artificial potential field based on regional threat degree provided by the embodiments of the present application. Figure 3 It is a schematic structural diagram of a computer device provided by the embodiments of the present application. Refer to Figure 3 , this computer device includes: an input device 33, an output device 34, a memory 32, and one or more processors 31; the memory 32 is used to store one or more programs; when the one or more programs are executed by the one or more processors 31, the one or more processors 31 implement the improved method of the cluster artificial potential field based on regional threat degree provided by the above embodiments. Among them, the input device 33, the output device 34, the memory 32, and the processor 31 can be connected through a bus or other means, Figure 3 taking the connection through the bus as an example.

[0108] The processor 31 executes various functional applications and data processing of the device by running software programs, instructions, and modules stored in the memory 32, that is, implements the above-mentioned improved method of the cluster artificial potential field based on regional threat degree.

[0109] The above-provided computer device can be used to execute the improved method of the cluster artificial potential field based on regional threat degree provided by the above embodiments, and has the corresponding functions and beneficial effects.

[0110] The embodiment of the present application further provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute an improved method of clustered artificial potential field based on regional threat degree when executed by a computer processor. The improved method of clustered artificial potential field based on regional threat degree includes: adding scheduling information through the background of the scheduling system, where the scheduling information includes point location, path, and action information; automatically generating an operation path based on the starting point and the target point of the robot and distributing it to the robot; and adjusting the operation path in real time according to the real-time task status and real-time working status of the elevator, combining with the real-time action information of the robot, and controlling the robot to operate.

[0111] Storage medium - any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media such as CD-ROMs, floppy disks, or magnetic tape devices; computer device memories or random access memories such as DRAM, DDRRAM, SRAM, EDORAM, Rambus RAM, etc.; non-volatile memories such as flash memory, magnetic media (such as hard disks or optical storage); register or other similar types of memory elements, etc. The storage medium may also include other types of memories or combinations thereof. Additionally, the storage medium may be located in the first computer device in which the program is executed, or may be located in a different second computer device, and the second computer device is connected to the first computer device through a network (such as the Internet). The second computer device may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (such as in different computer devices connected through a network). The storage medium may store program instructions executable by one or more processors (such as specifically implemented as a computer program).

[0112] Of course, for a storage medium containing computer-executable instructions provided by the embodiment of the present application, the computer-executable instructions are not limited to the improved method of clustered artificial potential field based on regional threat degree as described above, and may also execute related operations in the improved method of clustered artificial potential field based on regional threat degree provided by any embodiment of the present application.

[0113] The embodiments of the present application also provide a computer program product. The methods described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in the form of a computer program product in whole or in part. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, a core network device, an OAM (Open Application Model), or other programmable devices.

[0114] The improved device for swarm artificial potential field based on regional threat degree, storage medium, and computer device provided in the above embodiments can execute the method for improving swarm artificial potential field based on regional threat degree provided in any embodiment of the present application. For technical details not described in detail in the above embodiments, reference can be made to the method for improving swarm artificial potential field based on regional threat degree provided in any embodiment of the present application.

[0115] The above is only the preferred embodiment of the present application and the technical principles applied. The present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions that can be made by those skilled in the art will not depart from the protection scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, it can also include more other equivalent embodiments, and the scope of the present application is determined by the scope of the claims.

Claims

1. A method for improving cluster artificial potential field based on regional threat degree, characterized in that: The method comprises the following steps: Get the static obstacle position; Establish a multi-agent cluster structure and use the second-order consensus algorithm to form a follow-up; When the moving queue is the expected queue, obstacle avoidance control is performed through the artificial potential field method to obtain the overall following path of the cluster; According to the obstacle avoidance path, a second-order consensus algorithm is used to follow the path to control the cluster to avoid obstacles and reach the target point; By introducing the second obstacle position information and threat weight information, the size and direction of the repulsive force on the cluster are adjusted.

2. The method for improving the cluster artificial potential field based on regional threat degree according to claim 1 is characterized in that: When the moving queue is the expected queue, obstacle avoidance control is performed by an artificial potential field method, including: Define the first target as q0 and the second target as q i ,i=0,1…,n; The distance between the first agent and the second target is defined as: According to the consistency protocol, the first target is followed and the formation is maintained, and the input is obtained: In the formula, k x and k h is the weight coefficient, a ij is the adjacency matrix element, Δ ij is the expected distance between the first agent and the second agent; For the path of the first target of the cluster, the artificial potential field method is used; The repulsive force field in the artificial potential field method is: In the formula, d is the distance between two points, q is the position of the first target, and d Qmax is the repulsion coefficient, λ r is the repulsion gain coefficient.

3. The method for improving cluster artificial potential field based on regional threat degree according to claim 1 is characterized in that: The adjusting the magnitude and direction of the repulsive force on the cluster by introducing the second obstacle position information and the threat weight information includes: The method comprises introducing second obstacle position information and threat weight information; The threat weight matrix is ​​introduced to determine the size and threat level of different obstacles to calculate the repulsive force of different obstacles on the intelligent cluster; When more than three obstacles are detected within the detection range, a polygonal exclusion strategy is established based on the three obstacles with the largest threat weights within the range; wherein the quadrilateral is composed of the first target of the cluster and the three obstacles; Among them, the additional repulsive potential field imposed by the quadrilateral strategy is: Among them, Q w is the weighted center position of the three obstacles with the largest weights: obstacle A, obstacle B, and obstacle C, d cmax is the repulsion coefficient; D1, D2 and D inter They are respectively set identification areas.

4. The method for improving the cluster artificial potential field based on regional threat degree according to claim 3 is characterized in that: Also includes: Based on the quadrilateral repulsion function design of high-threat distribution areas, a double circular axis shift strategy is introduced; Shift distance d s for: In the formula, is the offset caused by the ith obstacle in the double-shift detection range, σ circ l e , ε1, ε2, d circle , D M and χ are weight coefficients.

5. The method for improving cluster artificial potential field based on regional threat degree according to claim 1, characterized in that: Also includes: Model the first agents involved as first-order integrator models: i=0,1…,n; In the formula, q i =[x i y i ] T is the position of the first agent in the navigation coordinate system, u i =[u x u y ] T is the control input of the first agent, and is also equal to the resultant force F sum , F sum is defined as: F sum =-▽U sum ; Where U sum is the sum of the potential fields of the first agent, U att The gravitational potential field brought to the target point, is the sum of the repulsive fields, F sum For U sum The negative gradient of .

6. The method for improving cluster artificial potential field based on regional threat degree according to claim 1 is characterized in that: The obtaining of the static obstacle position comprises: Create a plane map based on the actual operating environment and mark the locations of static obstacles.

7. A device for improving cluster artificial potential field based on regional threat degree, characterized in that: include: Position acquisition module, used to obtain the position of static obstacles; The cluster building module is used to build a multi-agent cluster structure and use a second-order consensus algorithm to form a follow-up; The path acquisition module is used to perform obstacle avoidance control through the artificial potential field method when the moving queue is the expected queue, and obtain the overall following path of the cluster; The obstacle avoidance control module is used to follow the path according to the obstacle avoidance path using a second-order consistency algorithm to control the cluster to avoid obstacles and reach the target point; The cluster adjustment module is used to adjust the size and direction of the repulsive force on the cluster by introducing the second obstacle position information and threat weight information.

8. A computer device, characterized in that: include: memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a cluster artificial potential field improvement method based on regional threat degree as described in any one of claims 1-6.

9. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute a cluster artificial potential field improvement method based on regional threat degree as described in any one of claims 1-6 when executed by a computer processor.

10. A computer program product, characterized in that The computer program product comprises instructions which, when executed by a computer, cause the computer to implement the method according to any one of claims 1-6.

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