Cluster artificial potential field improvement method and device based on regional threat degree

Abstract

The embodiment of the application discloses a cluster artificial potential field improvement method and device based on a region threat degree; the method comprises the following steps: acquiring a static obstacle position; establishing a multi-agent cluster structure, and adopting a second-order consistency algorithm to form following; when a moving queue is a desired queue, obstacle avoidance control is performed through an artificial potential field method to obtain an overall following path of the cluster; according to the obstacle avoidance path, the second-order consistency algorithm is adopted to perform path following to control the cluster to avoid obstacles and reach a target point; by introducing second obstacle position information and threat weight information, the size and direction of repulsion force suffered by the cluster are adjusted; by introducing position information and threat weight information of obstacles in a farther range, the size and direction of repulsion force suffered by the cluster are adjusted, so that the moving direction of the cluster is changed, and the cluster is prevented from falling into a local minimum value trap region.

Description

Technical Field

[0001] This application relates to the field of obstacle avoidance path technology, and in particular to a method and apparatus for improving clustered artificial potential fields based on regional threat levels. Background Technology

[0002] In simulated or actual combat operations using unmanned systems, it is necessary to first analyze and calculate known environmental threats and make targeted, predictive avoidances to prevent attacks on high-threat areas and avoid getting trapped in local minima or unreachable targets. Summary of the Invention

[0003] This application provides a method and apparatus for improving a clustered artificial potential field based on regional threat level, which achieves obstacle avoidance path planning and solves local optimization by sensing and predicting obstacles in advance.

[0004] In a first aspect, embodiments of this application provide a method for improving clustered artificial potential fields based on regional threat levels, the method comprising the following steps:

[0005] Obtain the position of static obstacles;

[0006] Establish a multi-agent cluster structure and use a second-order consensus algorithm to form a follower;

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

[0008] Based on the obstacle avoidance path, a second-order consensus algorithm is used for path following to control the cluster to avoid obstacles and reach the target point;

[0009] By introducing the location information of a second obstacle and threat weight information, the magnitude and direction of the repulsive force on the cluster are adjusted.

[0010] Furthermore, when the moving queue is the desired queue, obstacle avoidance control is performed using an artificial potential field method, including:

[0011] Define the first target as Define the second target as ;

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

[0013] ;

[0014] Based on the consensus protocol, the first target follower and formation maintenance are performed, resulting in the following input:

[0015] ;

[0016] In the formula, and These are weighting coefficients. For elements of the adjacency matrix, The expected distance between the first and second intelligent agents;

[0017] The artificial potential field method is used to determine the path of the first target in the cluster.

[0018] The repulsive field in the artificial potential field method is:

[0019] ;

[0020] In the formula, The distance between two points Position of the first target The repulsion coefficient is... This is the repulsion gain coefficient.

[0021] Furthermore, the adjustment of the magnitude and direction of the repulsive force on the cluster by introducing second obstacle location information and threat weight information includes:

[0022] The method involves introducing second obstacle location information and threat weight information;

[0023] A threat weight matrix is ​​introduced to determine the size and threat level of different obstacles, and then the repulsive force of different obstacles on the agent cluster is calculated.

[0024] When more than three obstacles are detected within the detection range, a polygon exclusion strategy is established based on the three obstacles with the highest threat weight within the range; wherein, the quadrilateral is composed of the cluster's first target and the three obstacles;

[0025] The additional repulsive potential field exerted by the quadrilateral strategy is:

[0026] ;

[0027] in, The weighted center position is the location of the three obstacles with the highest weights: obstacle A, obstacle B, and obstacle C. It is the repulsion coefficient; , and These are the designated identification areas.

[0028] Furthermore, it also includes:

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

[0030] Axis displacement distance for:

[0031] ;

[0032] ;

[0033] In the formula, It is the offset caused by the i-th obstacle in the dual-axis shift detection range. , , , , and These are the weighting coefficients.

[0034] Furthermore, it also includes:

[0035] All the first agents involved are modeled as first-order integrator models:

[0036] ;

[0037] In the formula, This represents the position of the first intelligent agent in the navigation coordinate system. It is the control input received by the first intelligent agent, and it is also equal to the resultant force received. , The definition of is:

[0038] ;

[0039] ;

[0040] In the formula, The sum of the potential fields experienced by the first intelligent agent. The gravitational potential field brought about by the target point The sum of the repulsive forces experienced. for The negative gradient.

[0041] Furthermore, obtaining the position of the static obstacle includes:

[0042] Create a planar map based on the actual operating environment and mark the locations of static obstacles.

[0043] In a second aspect, embodiments of this application also provide a cluster artificial potential field improvement device based on regional threat level, comprising:

[0044] The location acquisition module is used to acquire the location of static obstacles;

[0045] The cluster establishment module is used to establish a multi-agent cluster structure and uses a second-order consensus algorithm to form a follower.

[0046] The path acquisition module is used to obtain the overall following path of the cluster by performing obstacle avoidance control using the artificial potential field method when the moving queue is the desired queue.

[0047] The obstacle avoidance control module is used to follow the path using a second-order consensus algorithm based on the obstacle avoidance path, so as to control the cluster to avoid obstacles and reach the target point.

[0048] The cluster adjustment module is used to adjust the magnitude and direction of the repulsive force on the cluster by introducing the location information of the second obstacle and the threat weight information.

[0049] In a third aspect, embodiments of this application also provide a computer device, including: a memory and one or more processors;

[0050] The memory is used to store one or more programs;

[0051] 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 level as described above.

[0052] In a fourth aspect, embodiments of this application also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a cluster artificial potential field improvement method based on regional threat level as described above.

[0053] Fifthly, embodiments of this application provide a computer program product, the computer program product including instructions, which, when executed by a computer, cause the computer to perform the method described above.

[0054] This application embodiment obtains the position of static obstacles; establishes a multi-agent cluster structure and uses a second-order consensus algorithm to form a following pattern; when the moving queue is the desired queue, obstacle avoidance control is performed using an artificial potential field method to obtain the overall following path of the cluster; based on the obstacle avoidance path, a second-order consensus algorithm is used for path following to control the cluster to avoid obstacles and reach the target point; by introducing the position information of a second obstacle and threat weight information, the magnitude and direction of the repulsive force on the cluster are adjusted; by introducing the position information and threat weight information of obstacles in a more distant range, the magnitude and direction of the repulsive force on the cluster are adjusted, thereby changing its movement direction and avoiding falling into the local minimum trap region. Attached Figure Description

[0055] Figure 1 This is a flowchart of a method for improving a clustered artificial potential field based on regional threat level, provided in an embodiment of this application.

[0056] Figure 2This is a schematic diagram of a cluster artificial potential field improvement device based on regional threat level provided in an embodiment of this application;

[0057] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application;

[0058] Figure 4 This is a schematic diagram of the attractive and repulsive forces of the artificial potential field method provided in the embodiments of this application;

[0059] Figure 5 This is a schematic diagram illustrating the problem of the artificial potential field method falling into a local minimum trap, as provided in the embodiments of this application.

[0060] Figure 6 This is a schematic diagram of a quadrilateral repulsion strategy based on an improved artificial potential field method provided in an embodiment of this application;

[0061] Figure 7 This is a diagram showing the specific force direction and range of action in the quadrilateral strategy provided in the embodiments of this application;

[0062] Figure 8 This is a schematic diagram of the dual circular axis-shifting strategy provided in an embodiment of this application;

[0063] Figure 9 This is a comparison diagram before and after the axis shift of the circular axis shifting strategy provided in the embodiments of this application. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing 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 operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0065] This application establishes a cluster artificial potential field improvement method based on regional threat level, which achieves obstacle avoidance path planning and solves local optimization by sensing and predicting obstacles in advance.

[0066] The method for improving a clustered artificial potential field based on regional threat level provided in this embodiment can be executed by a device for improving a clustered artificial potential field based on regional threat level. This device can be implemented through software and / or hardware and integrated into a device for improving a clustered artificial potential field based on regional threat level. The device for improving a clustered artificial potential field based on regional threat level can be a computer or similar device.

[0067] Figure 1 A flowchart illustrating a method for improving clustered artificial potential fields based on regional threat levels, provided in an embodiment of this application. (Reference) Figure 1 The method includes the following steps:

[0068] 100. Obtain the location of static obstacles.

[0069] Optionally, a planar map can be created based on the actual operating environment, marking the locations of static obstacles.

[0070] 200. Establish a multi-agent cluster structure and use a second-order consensus algorithm to form a follower.

[0071] 300. When the moving queue is the desired queue, obstacle avoidance control is performed using the artificial potential field method to obtain the overall following path of the cluster.

[0072] 400. Based on the obstacle avoidance path, a second-order consensus algorithm is used for path following to control the cluster to avoid obstacles and reach the target point.

[0073] 500. By introducing the location information of the second obstacle and the threat weight information, the magnitude and direction of the repulsive force on the cluster are adjusted.

[0074] For example, the first target is the leader, and the second target is the follower. Please refer to [reference needed]. Figure 4 A multi-agent formation is introduced, selecting a leader and followers, and using a consensus protocol to control follower tracking of the leader and formation control. When the multi-agents move in the desired queue, an improved artificial potential field method is used to constrain the leader's movement to obtain the cluster movement path.

[0075] In the improved artificial potential field method, a threat level weight matrix for obstacles is introduced to determine the three obstacles with the highest threat level, forming a quadrilateral. A quadrilateral repulsion strategy is then added, such as... Figure 6 As shown, this allows the navigator to choose a safer route when selecting a path; the magnitude and direction of the additional repulsive force applied are calculated based on the quadrilateral repulsion strategy, such as... Figure 7 As shown, the total net force acting on the cluster during its movement is obtained, and the velocity at the next moment is obtained, thus enabling the cluster to move.

[0076] In some embodiments, please refer to Figure 4 When the moving queue is the desired queue, obstacle avoidance control is performed using an artificial potential field method, including:

[0077] Define the first target as Define the second target as ;

[0078] Define the distance between the first agent and the second target as:

[0079] ;

[0080] Based on the consensus protocol, the first target follower and formation maintenance are performed, resulting in the following input:

[0081] ;

[0082] In the formula, and These are weighting coefficients. For elements of the adjacency matrix, The expected distance between the first and second intelligent agents;

[0083] The artificial potential field method is used to determine the path of the first target in the cluster.

[0084] The repulsive field in the artificial potential field method is:

[0085] ;

[0086] In the formula, The distance between two points Position of the first target The repulsion coefficient is... This is the repulsion gain coefficient.

[0087] In some embodiments, adjusting the magnitude and direction of the repulsive force on the cluster by introducing second obstacle location information and threat weight information includes:

[0088] The method involves introducing second obstacle location information and threat weight information;

[0089] A threat weight matrix is ​​introduced to determine the size and threat level of different obstacles, and then the repulsive force of different obstacles on the agent cluster is calculated.

[0090] When more than three obstacles are detected within the detection range, a polygon exclusion strategy is established based on the three obstacles with the highest threat weight within the range; wherein, the quadrilateral is composed of the cluster's first target and the three obstacles;

[0091] The additional repulsive potential field exerted by the quadrilateral strategy is:

[0092] ;

[0093] in, The weighted center position is the location of the three obstacles with the highest weights: obstacle A, obstacle B, and obstacle C. It is the repulsion coefficient; , and They are respectively Figure 7 The area marked with signs.

[0094] In some embodiments, please refer to Figure 8 and Figure 9 We designed a quadrilateral repulsion force function based on high-threat distribution areas and introduced a double circular axis-shifting strategy to increase the probability of selecting trajectories with fewer obstacles during robot obstacle avoidance, thereby optimizing global path planning.

[0095] Axis displacement distance for:

[0096] ;

[0097] ;

[0098] In the formula, It is the offset caused by the i-th obstacle in the dual-axis shift detection range. , , , , and These are the weighting coefficients.

[0099] In some embodiments, Figure 5 The situation described here is a local minimum problem that occurs when the traditional artificial potential field method faces a specific environment. The repulsive force and the attractive force of the target cause the agent to stay in place or within a very small range. An improved method can solve this problem.

[0100] All the first agents involved are modeled as first-order integrator models:

[0101] ;

[0102] In the formula, This represents the position of the first intelligent agent in the navigation coordinate system. It is the control input received by the first intelligent agent, and it is also equal to the resultant force received. , The definition of is:

[0103] ;

[0104] ;

[0105] In the formula, The sum of the potential fields experienced by the first intelligent agent. The gravitational potential field brought about by the target point The sum of the repulsive force fields experienced. for The negative gradient.

[0106] As described above, the embodiments of this application obtain the static obstacle positions; establish a multi-agent cluster structure and use a second-order consensus algorithm to form a following pattern; when the moving queue is the desired queue, obstacle avoidance control is performed using an artificial potential field method to obtain the overall following path of the cluster; based on the obstacle avoidance path, a second-order consensus algorithm is used for path following to control the cluster to avoid obstacles and reach the target point; by introducing second obstacle position information and threat weight information, the magnitude and direction of the repulsive force on the cluster are adjusted; by introducing obstacle position information and threat weight information from a more distant range, the magnitude and direction of the repulsive force on the cluster are adjusted, thereby changing its movement direction and avoiding falling into the local minimum trap area; by perceiving and predicting obstacles in advance, the obstacle avoidance path planning achieves the effect of solving local optima at the same time.

[0107] Based on the above embodiments, please refer to Figure 2 This application provides a cluster artificial potential field improvement device based on regional threat level. The cluster artificial potential field improvement device based on regional threat level specifically includes: a location 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.

[0108] The system includes the following modules: Position Acquisition Module 201, which acquires the position of static obstacles; Cluster Establishment Module 202, which establishes a multi-agent cluster structure and uses a second-order consensus algorithm to form a following group; Path Acquisition Module 203, which, when the moving queue is the desired queue, uses an artificial potential field method to perform obstacle avoidance control and obtain the overall following path of the cluster; Obstacle Avoidance Control Module 204, which, based on the obstacle avoidance path, uses a second-order consensus algorithm to perform path following, thereby controlling the cluster to avoid obstacles and reach the target point; and Cluster Adjustment Module 205, which adjusts the magnitude and direction of the repulsive force on the cluster by introducing the position information of a second obstacle and threat weight information.

[0109] As described above, the embodiments of this application obtain the position of static obstacles; establish a multi-agent cluster structure and use a second-order consensus algorithm to form a following pattern; when the moving queue is the desired queue, obstacle avoidance control is performed using an artificial potential field method to obtain the overall following path of the cluster; based on the obstacle avoidance path, a second-order consensus algorithm is used for path following to control the cluster to avoid obstacles and reach the target point; by introducing the position information of a second obstacle and threat weight information, the magnitude and direction of the repulsive force on the cluster are adjusted; by introducing the position information and threat weight information of obstacles in a more distant range, the magnitude and direction of the repulsive force on the cluster are adjusted, thereby changing its movement direction and avoiding falling into the local minimum trap region.

[0110] The cluster artificial potential field improvement device based on regional threat level provided in this application embodiment can be used to execute the cluster artificial potential field improvement method based on regional threat level provided in the above embodiment, and has corresponding functions and beneficial effects.

[0111] This application also provides a computer device that can integrate the cluster artificial potential field improvement device based on regional threat level provided in this application. Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. (Reference) Figure 3 The computer device includes an input device 33, an output device 34, a memory 32, and one or more processors 31. The memory 32 stores 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 cluster artificial potential field improvement method based on regional threat level provided in the above embodiments. The input device 33, output device 34, memory 32, and processors 31 can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0112] 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, thereby realizing the above-mentioned method for improving the cluster artificial potential field based on regional threat level.

[0113] The computer equipment provided above can be used to execute the cluster artificial potential field improvement method based on regional threat level provided in the above embodiments, and has corresponding functions and beneficial effects.

[0114] This application embodiment also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to execute a method for improving a clustered artificial potential field based on regional threat level. The method includes: adding scheduling information through a scheduling system backend, wherein the scheduling information includes location, path, and action information; automatically generating and assigning a running path to the robot based on the robot's starting point and target point; and adjusting the running path in real time according to the elevator's real-time task status and real-time working status, combined with the robot's real-time action information, and controlling the robot to run.

[0115] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROM, floppy disk, or magnetic tape devices; computer device memory or random access memory, such as DRAM, DDRRAM, SRAM, EDORAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. Storage medium may also include other types of memory or combinations thereof. Furthermore, storage medium may reside in a first computer device in which a program is executed, or it may reside in a different second computer device connected to the first computer device via 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 (e.g., in different computer devices connected via a network). Storage medium may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0116] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the cluster artificial potential field improvement method based on regional threat level as described above, but can also perform related operations in the cluster artificial potential field improvement method based on regional threat level provided in any embodiment of this application.

[0117] This application also provides a computer program product. The methods described in the various embodiments of this 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 whole or in part, as a computer program product. 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 various embodiments of this 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 equipment, a core network device, an OAM (Open Application Model), or other programmable devices.

[0118] The cluster artificial potential field improvement device, storage medium, and computer equipment based on regional threat level provided in the above embodiments can execute the cluster artificial potential field improvement method based on regional threat level provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the cluster artificial potential field improvement method based on regional threat level provided in any embodiment of this application.

[0119] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A method for improving clustered artificial potential fields based on regional threat levels, characterized in that, The method includes the following steps: Obtain the position of static obstacles; Establish a multi-agent cluster structure and use a second-order consensus algorithm to form a follower; When the moving queue is the desired queue, obstacle avoidance control is performed using the artificial potential field method to obtain the overall following path of the cluster; Based on the obstacle avoidance path, a second-order consensus algorithm is used for path following to control the cluster to avoid obstacles and reach the target point; By introducing the location information of the second obstacle and threat weight information, the magnitude and direction of the repulsive force on the cluster are adjusted; The method of adjusting the magnitude and direction of the repulsive force on the cluster by introducing the location information of the second obstacle and threat weight information includes: The method involves introducing second obstacle location information and threat weight information; A threat weight matrix is ​​introduced to determine the size and threat level of different obstacles, and then the repulsive force of different obstacles on the agent cluster is calculated. When more than three obstacles are detected within the detection range, a polygon exclusion strategy is established based on the three obstacles with the highest threat weight within the range; wherein, the quadrilateral is composed of the cluster's first target and the three obstacles; The additional repulsive potential field exerted by the quadrilateral strategy is: ； in, The weighted center position is the location of the three obstacles with the highest weights: obstacle A, obstacle B, and obstacle C. It is the repulsion coefficient; , and These are the designated identification areas.

2. The method for improving clustered artificial potential fields based on regional threat level according to claim 1, characterized in that, When the moving queue is the desired queue, obstacle avoidance control is performed using an artificial potential field method, including: Define the first target as Define the second target as ; Define the distance between the first agent and the second target as: ； Based on the consensus protocol, the first target follower and formation maintenance are performed, resulting in the following input: ； In the formula, and These are weighting coefficients. For elements of the adjacency matrix, The expected distance between the first and second intelligent agents; The artificial potential field method is used to determine the path of the first target in the cluster. The repulsive field in the artificial potential field method is: ； In the formula, The distance between two points Position of the first target The repulsion coefficient is... This is the repulsion gain coefficient.

3. The improved method for clustered artificial potential fields based on regional threat level according to claim 2, characterized in that, Also includes: Based on the design of a quadrilateral repulsive force function in high-threat distribution areas, a double circular axis-shifting strategy is introduced. Axis displacement distance for: ； ； In the formula, It is the offset caused by the i-th obstacle in the dual-axis shift detection range. , , , , and These are the weighting coefficients.

4. The improved method for clustered artificial potential fields based on regional threat level according to claim 1, characterized in that, Also includes: All the first agents involved are modeled as first-order integrator models: ； In the formula, This represents the position of the first intelligent agent in the navigation coordinate system. It is the control input received by the first intelligent agent, and it is also equal to the resultant force received. , The definition of is: ； ； In the formula, The sum of the potential fields experienced by the first intelligent agent. The gravitational potential field brought about by the target point The sum of the repulsive force fields experienced. for The negative gradient.

5. The method for improving clustered artificial potential fields based on regional threat level according to claim 1, characterized in that, The process of obtaining the location of static obstacles includes: Create a planar map based on the actual operating environment and mark the locations of static obstacles.

6. A cluster artificial potential field improvement device based on regional threat level, characterized in that, include: The location acquisition module is used to acquire the location of static obstacles; The cluster establishment module is used to establish a multi-agent cluster structure and uses a second-order consensus algorithm to form a follower. The path acquisition module is used to obtain the overall following path of the cluster by performing obstacle avoidance control using the artificial potential field method when the moving queue is the desired queue. The obstacle avoidance control module is used to follow the path using a second-order consensus algorithm based on the obstacle avoidance path, so as to control the cluster to avoid obstacles and reach the target point. The cluster adjustment module is used to adjust the magnitude and direction of the repulsive force on the cluster by introducing the location information of the second obstacle and the threat weight information; The method of adjusting the magnitude and direction of the repulsive force on the cluster by introducing the location information of the second obstacle and threat weight information includes: The method involves introducing second obstacle location information and threat weight information; A threat weight matrix is ​​introduced to determine the size and threat level of different obstacles, and then the repulsive force of different obstacles on the agent cluster is calculated. When more than three obstacles are detected within the detection range, a polygon exclusion strategy is established based on the three obstacles with the highest threat weight within the range; wherein, the quadrilateral is composed of the cluster's first target and the three obstacles; The additional repulsive potential field exerted by the quadrilateral strategy is: ； in, The weighted center position is the location of the three obstacles with the highest weights: obstacle A, obstacle B, and obstacle C. It is the repulsion coefficient; , and These are the designated identification areas.

7. 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 level as described in any one of claims 1-5.

8. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform a cluster artificial potential field improvement method based on regional threat level as described in any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1-5.

Images