A multi-agent boundary game method based on priority path planning

Abstract

This application proposes a multi-agent boundary game method based on priority path planning, comprising: calculating the relative attack time of the invading agent to the boundary attack point and the relative defense time of the defending agent to the boundary attack point; calculating the priority parameters of each defending agent based on the relative attack and defense times, optimizing the maximum matching algorithm using the priority parameters to determine the defense strategy combination; planning a path for each defending agent according to the defense strategy combination, and optimizing the global path planning cost function using the particle swarm optimization algorithm to generate the globally optimal path for the defending agent; and adjusting the motion state of the defending agent through a linear feedback control algorithm to make it move according to the globally optimal path, thereby achieving path tracking. This application solves the problem of multi-agent defense task allocation and path planning in complex obstacle environments by combining priority path planning and the maximum matching method, effectively improving the defense success rate and adaptability.

Description

Technical Field

[0001] This application relates to the field of intelligent control and path planning technology, and in particular to a multi-agent boundary game method based on priority path planning. Background Technology

[0002] In recent years, adversarial game problems in multi-agent systems have gradually become a research hotspot, such as chase-and-escape games, target-chasing defense games, and boundary game problems. Among them, boundary game problems are widely used in scenarios such as drone formation defense and marine unmanned surface vessel defense. The research objective is for the defender to prevent as many intruders as possible from crossing a designated boundary, while the intruders attempt to break through the defense to enter the target area.

[0003] Existing research on boundary game theory mostly employs maximum matching algorithms to assign defense tasks, assigning an intrusion target to each defending agent based on the probability of successful defense. However, these methods typically assume the absence of collisions between agents, which cannot fully meet the requirements in complex real-world applications. Especially in finite spaces, when a large number of agents are involved, collisions between agents become almost inevitable, and ignoring collisions can lead to defense task failure.

[0004] Furthermore, most existing research focuses on designing algorithms for simple, barrier-free environments, failing to fully consider the complexities of real-world scenarios, such as the presence of obstacles and the collision avoidance requirements of multiple autonomous agents. In practical applications such as island and reef defense and border patrol, defense scenarios often involve complex obstacle structures, requiring autonomous agents to achieve efficient defense while avoiding collisions, which places higher demands on traditional algorithms.

[0005] Therefore, in view of the shortcomings of existing algorithms, how to realize the defense task allocation and path planning of multi-agent systems in complex obstacle environments, and at the same time solve the collision problem between agents, has become an important issue that needs to be addressed in the current field of boundary game theory. Summary of the Invention

[0006] This application aims to at least partially address one of the technical problems in the related art.

[0007] Therefore, the first objective of this application is to propose a multi-agent boundary game method based on priority path planning.

[0008] The second objective of this application is to propose a multi-agent boundary game device based on priority path planning.

[0009] The third objective of this application is to propose an electronic device.

[0010] The fourth objective of this application is to provide a computer-readable storage medium.

[0011] The fifth objective of this application is to provide a computer program product.

[0012] To achieve the above objectives, the first aspect of this application proposes a multi-agent boundary game method based on priority path planning, comprising:

[0013] Based on the location, velocity direction, and shape of the defense zone boundary of the invading entity, calculate the projection point and attack point of the invasion direction, and combine the path length and maximum speed to calculate the relative attack time.

[0014] Based on the defensive agent's position and obstacle environment, a local path length cost function and a local collision cost function are constructed to calculate the optimal path length, and the relative defense time is calculated by combining the defensive agent's maximum speed.

[0015] Based on the relative attack time and the relative defense time, calculate the priority parameter of each defense autonomous entity, optimize the maximum matching algorithm using the priority parameter, and determine the combination of defense strategies.

[0016] Based on the defense strategy combination, a path is planned for each defense agent, and the global path planning cost function is optimized using the particle swarm optimization algorithm to generate the globally optimal path for the defense agent.

[0017] The motion state of the defensive agent is adjusted by a linear feedback control algorithm so that it moves along the globally optimal path, thereby achieving path tracking.

[0018] Optionally, the step of calculating the projection point and attack point of the intrusion direction based on the position, velocity direction, and boundary shape of the intruder, and calculating the relative attack time in combination with the path length and maximum speed, includes:

[0019] According to the intruding subject I j Current location and velocity direction vector u j =(cosφ j ,sinφ j ), determine the direction of the intrusion;

[0020] The projection point P of the line connecting the invasion direction and the target perimeter center O is... j As a reference point, the formula is:

[0021]

[0022] in, Indicates from the intruding self-subject I j Vector to the center O of the target region With velocity direction vector u j The inner product;

[0023] From the reference point P j Calculate the boundary assault point A of the target area along the invasion direction. j The formula is:

[0024] A j =P j -u j (R 2 -|OP j |) 1 / 2

[0025] Where R is the radius of the target region, |OP j | Indicates the distance from the center of the target area to the projection point;

[0026] Based on path length and intrusion self-subject I j Maximum speed The relative attack time is calculated using the following formula:

[0027]

[0028] in, For the intrusion of the self-subject I j To the border assault point A j The relative time for a surprise attack.

[0029] Optionally, the step of constructing a local path length cost function and a local collision cost function based on the defensive agent's position and obstacle environment to calculate the optimal path length, and combining this with the defensive agent's maximum speed to calculate the relative defense time, includes:

[0030] Constructing a local path length cost function The formula for calculating the path length from the current location of the defensive agent to the boundary assault point is:

[0031]

[0032] in, Let n be the continuous location points of the defensive autonomous entity in the path planning, and n be the total number of path points.

[0033] Constructing the local obstacle and collision avoidance cost function The formula used to handle obstacles and collision avoidance costs in the path is:

[0034]

[0035] like

[0036] in, To defend against the distance from the main body to the center of the obstacle, r cThe radius of the obstacle;

[0037] The local path planning cost function is solved using the particle swarm optimization algorithm to obtain the defensive agent D. i From initial position to boundary assault point A j The length of the shortest path planned is l ij The formula for the local path planning cost function is as follows:

[0038]

[0039] Where w1 and w2 are the weight parameters for local path length cost and local obstacle and collision avoidance cost, respectively;

[0040] Based on the length j of the shortest path ij and the maximum speed of the defending self The relative defense time is calculated using the following formula:

[0041]

[0042] in, To defend against self-subject D i To the border assault point A j The relative defense time.

[0043] Optionally, the step of calculating the priority parameter of each defensive autonomous entity based on the relative attack time and the relative defense time, and optimizing the maximum matching algorithm using the priority parameter to determine the defense strategy combination includes:

[0044] Based on the relative attack time and the relative defense time, the priority parameter of each defensive autonomous entity is calculated using the following formula:

[0045]

[0046] Where, ΔD i Indicates the defensive self D i Priority parameters;

[0047] The maximum matching algorithm prioritizes matching defensive and intrusive entities with lower priority parameters. The matching criteria are as follows:

[0048]

[0049] For defensive and intrusive agents that meet the matching conditions, tasks are assigned according to their priorities, and a combination of defense strategies is generated.

[0050] Optionally, the step of planning a path for each defensive agent based on the defense strategy combination and optimizing the global path planning cost function using a particle swarm optimization algorithm to generate the optimal path for the defensive agent includes:

[0051] Based on the aforementioned defense strategy combination, a global path planning cost function for the defense cluster is constructed. The formula is:

[0052]

[0053] Where Δ0 is the offset adjustment constant;

[0054] Furthermore, the global obstacle and collision avoidance cost function for constructing the defense cluster. The formula is:

[0055]

[0056] in, This represents the distance between the defensive entities, where r0 is the collision avoidance distance threshold.

[0057] The global path planning cost function is solved using the particle swarm optimization algorithm to generate the globally optimal path for the defensive autonomous agent. The formula for the global path planning cost function is as follows:

[0058]

[0059] Where w'1 and w'2 are the weight parameters for global path length cost and global obstacle and collision avoidance cost, respectively.

[0060] Optionally, adjusting the motion state of the defensive agent through a linear feedback control algorithm to make it move along the globally optimal path and achieve path tracking includes:

[0061] Design a linear feedback control algorithm, with the following formula:

[0062]

[0063] Where k is the feedback gain parameter;

[0064] The linear feedback control algorithm adjusts the motion state of the defensive agent, causing it to move along the globally optimal path, thus achieving path tracking.

[0065] To achieve the above objectives, a second aspect of this application proposes a multi-agent boundary game device based on priority path planning, comprising:

[0066] The assault time calculation module is used to calculate the projection point and assault point of the intrusion direction based on the position, speed direction and shape of the defense area boundary of the intruder, and to calculate the relative assault time by combining the path length and maximum speed.

[0067] The defense time calculation module is used to construct a local path length cost function and a local collision cost function based on the position of the defending agent and the obstacle environment, calculate the optimal path length, and combine it with the maximum speed of the defending agent to calculate the relative defense time.

[0068] The defense task allocation module is used to calculate the priority parameter of each defense autonomous entity based on the relative attack time and the relative defense time, and to optimize the maximum matching algorithm using the priority parameter to determine the combination of defense strategies.

[0069] The global path planning module is used to plan a path for each defensive autonomous entity based on the defense strategy combination, and to optimize the global path planning cost function using the particle swarm optimization algorithm to generate the global optimal path for the defensive autonomous entity.

[0070] The motion control module is used to adjust the motion state of the defensive agent through a linear feedback control algorithm, so that it moves along the globally optimal path and achieves path tracking.

[0071] To achieve the above objectives, a third aspect of this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0072] The memory stores computer-executed instructions;

[0073] The processor executes computer execution instructions stored in the memory to implement the method as described in any one of the first aspects.

[0074] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method as described in any one of the first aspects.

[0075] To achieve the above objectives, a fifth aspect of this application provides a computer program product that, when executed by a processor, implements the method described in any one of the first aspects.

[0076] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0077] By calculating relative defense time and relative attack time to set defense priorities, this application effectively improves the success rate of boundary defense; by combining obstacle and defense autonomous collision avoidance problems, this application is applicable not only to simple barrier-free environments, but also to complex environments with multiple obstacles; by implementing the overall planning and design of boundary defense through optimization methods, this application can make full use of existing optimization algorithms to efficiently solve defense paths.

[0078] Compared to existing technologies, this application proposes a defense strategy based on priority path planning for multi-agent boundary game problems, capable of controlling multiple agents to defend against intrusions from external agents. Existing algorithms mostly rely on the maximum matching approach for task allocation, assigning a defense target to each agent based on the probability of successful defense. This application significantly extends this approach by combining priority path planning with the maximum matching method, enabling the defense algorithm to adapt to complex obstacle environments. This coupled method has significant application value in multiple scenarios, such as in island and reef defense scenarios based on unmanned surface vessels, where the algorithm can efficiently complete defense tasks in complex situations with multiple reefs surrounding the island. Furthermore, this application solves the collision avoidance problem among defending agents by introducing a collision avoidance cost function into the path planning, which is particularly important in large-scale agent boundary defense problems within small areas. This feature enhances the practicality and reliability of the defense system in complex and dynamic environments.

[0079] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0080] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0081] Figure 1 A flowchart illustrating a multi-agent boundary game method based on priority path planning provided in this application embodiment;

[0082] Figure 2 This is a schematic diagram of a multi-agent boundary game device based on priority path planning provided in an embodiment of this application. Detailed Implementation

[0083] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0084] To address the problems existing in the prior art, this application provides a multi-agent boundary game method based on priority path planning. Figure 1 This is a flowchart illustrating a multi-agent boundary game method based on priority path planning, provided in an embodiment of this application.

[0085] Before describing the technical solution of this application, let me first describe the technical scenario in which this application is situated.

[0086] The scenario studied in this application is a two-dimensional planar region. The issue of border defense within the region Ω in The core region, containing important targets, is bounded by a circle with its center at O ​​= (0,0) and radius R. in The region Ω is a region containing Ω. in A larger region, whose boundary is also circular, with center O = (0,0) and radius R, and satisfies Define the buffer as Ω loop =Ω-Ω in This refers to the annular region between the two circles.

[0087] In this boundary game scenario, consider a multi-agent system with n agents, comprising two types: defensive agents and invading agents. The goal of the invading agents is to breach the buffer zone Ω. loop Entering the core area Ω in The goal of the defensive entity is to block intruders and ensure that they cannot enter the core area Ω. in .

[0088] This scenario is widely used in tasks such as drone patrol and defense, and maritime unmanned surface vessel island and reef defense. The research focuses on how to achieve effective interception of intruders by defenders in complex and dynamic environments through reasonable task allocation and path planning.

[0089] like Figure 1 As shown, the multi-agent boundary game method based on priority path planning presented in this application includes the following steps:

[0090] Step 101: Based on the location, speed direction, and shape of the defense zone boundary of the invading entity, calculate the projection point and attack point of the invading direction, and calculate the relative attack time by combining the path length and maximum speed.

[0091] In this embodiment of the application, firstly, based on the intrusion subject I j Current location and velocity direction vector u j =(cosφ j ,sinφ j This determines the direction of the invasion in order to further calculate its assault position in the target area.

[0092] In this embodiment, the intrusion direction is calculated based on the current position and velocity direction vector of the intruding autonomous entity. The velocity direction vector is represented by a unit vector and describes the movement direction of the intruding autonomous entity. The intrusion direction is the starting point for calculating the assault point and reflects the expected trajectory of the intruding autonomous entity.

[0093] Then, the projection point P of the line connecting the invasion direction and the target perimeter center O is... j As a reference point, the formula is:

[0094]

[0095] in, Indicates from the intruding self-subject I j Vector to the center O of the target region With velocity direction vector u j The inner product of.

[0096] In this embodiment, the projection point is the projected position of the target area's center on the straight line of the invasion direction. Its calculation formula utilizes the property of the vector inner product. By obtaining the projection relationship between the current position and the velocity direction, the path point most likely to approach the target area by the invading entity can be determined. The accuracy of the projection point calculation directly affects the accuracy of the assault point location.

[0097] Then, from the reference point P j Calculate the boundary assault point A of the target area along the invasion direction. j The formula is:

[0098] A j =P j -u j (R 2 -|OP f |) 1 / 2

[0099] Where R is the radius of the target region, |OP j | indicates the distance from the center of the target area to the projection point.

[0100] In this embodiment of the application, the assault point is the contact point of the invading subject on the boundary of the target area, which is calculated based on the position of the projection point and the geometric parameters (such as radius) of the target area.

[0101] Finally, based on path length and intrusion self-subject I j Maximum speed The formula for calculating the relative attack time is:

[0102]

[0103] in, For the intrusion of the self-subject I j To the border assault point A j The relative time for a surprise attack.

[0104] In this embodiment, the relative assault time is the time required for the intruding agent to reach the assault point from its current location. Its calculation combines path length and the intruding agent's maximum speed, and the formula reflects the shortest path time for the intruding agent under ideal conditions. Relative assault time is a core parameter in defense task allocation, providing important reference for prioritizing defensive agents and planning missions.

[0105] Step 102: Based on the position of the defending agent and the obstacle environment, construct the local path length cost function and the local collision cost function to calculate the optimal path length, and combine the maximum speed of the defending agent to calculate the relative defense time.

[0106] In this embodiment of the application, to simplify calculations, it is assumed that the defensive self-subject D... i Only at border assault point A j Blocking the intrusion of the host I j By ignoring the collision avoidance problem between defensive agents and simplifying the obstacle model, the path planning problem can be transformed into a path optimization problem for a single agent in an obstacle environment.

[0107] The specific steps are as follows:

[0108] First, construct the local path length cost function. The formula for calculating the path length from the current location of the defensive agent to the boundary assault point is as follows:

[0109]

[0110] in, represents the continuous position points of the defensive autonomous entity in path planning, n is the total number of path points, and represents the number of path segments.

[0111] This function describes the geometric length of a path and is the basic optimization objective of path planning, used to find the shortest path.

[0112] Secondly, construct the local obstacle and collision avoidance cost function. The formula used to handle obstacles and collision avoidance costs in the path is:

[0113]

[0114] like

[0115] in, To defend against the distance from the main body to the center of the obstacle, r c Let z be the radius of the obstacle, and z be the total number of obstacles.

[0116] Then, the local path length cost function and the local obstacle and collision avoidance cost function are combined to form a complete local path planning cost function. By incorporating obstacle constraints into path planning, it can be ensured that the defending agent avoids obstacles, thereby generating a feasible path. The formula for the local path planning cost function is as follows:

[0117]

[0118] Where w1 and w2 are the weight parameters for local path length cost and local obstacle and collision avoidance cost, respectively.

[0119] Subsequently, the local path planning cost function is solved using the particle swarm optimization algorithm to obtain the defensive agent D. i From initial position to boundary assault point A j The length of the shortest path planned is l ij And further based on the length l of the shortest path ij and the maximum speed of the defending self The formula for calculating relative defense time is:

[0120]

[0121] in, To defend against self-subject D i To the border assault point A j The relative defense time.

[0122] Understandably, the particle swarm optimization algorithm transforms the path planning problem into an optimization problem, finding the optimal solution to the cost function through global and local searches of particles.

[0123] Through the above steps, the relative defense time can be quickly calculated without ignoring the collision avoidance requirements between the defending agents. Furthermore, the introduction of the particle swarm optimization algorithm makes the path planning optimization more efficient, satisfying both the requirement of the shortest path and taking into account the impact of obstacles on the path.

[0124] Step 103: Based on the relative attack time and relative defense time, calculate the priority parameters of each defensive autonomous entity, optimize the maximum matching algorithm using the priority parameters, and determine the combination of defense strategies.

[0125] In this embodiment of the application, the priority parameter of each defensive autonomous entity is first calculated, using the following formula:

[0126]

[0127] Where, ΔD i Indicates the defensive self D i The priority parameter.

[0128] What is understandable is that if ΔD i If the value is greater than 0, it means that the defending entity can intercept the invading entity before it reaches the boundary assault point, and ΔD i The lower the value, the more urgent the defense mission.

[0129] Then, the defending agents and invading agents are mapped to a bipartite graph G, and a set is defined: a set D includes all defending agents D i Another set = including all intruding agents I j For any defensive agent D i With the intruding self-subject I j If the conditions are met If the defending entity can intercept the invading entity before it reaches the attack point, then add D to the bipartite graph G. i and I j The edges between them.

[0130] Then, in the constructed bipartite graph G, the maximum matching algorithm is run, prioritizing matching with the lower priority parameter ΔD. i The matching conditions for the defending agent and the intruding agent are:

[0131]

[0132] In this application embodiment, either a linear programming algorithm or the Hopcroft-Karp algorithm can be selected to calculate the maximum matching, and this application does not make specific limitations on this.

[0133] Finally, the optimal matching combination between the defending agent and the intruding agent is found in the bipartite graph G using the maximum matching algorithm, which is the maximum matching result. Based on the maximum matching result, a combination of defense strategies is generated.

[0134] Specifically, tasks are assigned to defensive and intrusive agents that meet the matching conditions; the intrusion target assigned to each defensive agent is its defense task; and the output defense strategy combination serves as input for path planning and trajectory tracking.

[0135] Step 104: Plan a path for each defensive autonomous entity based on the defense strategy combination, and use the particle swarm optimization algorithm to optimize the global path planning cost function to generate the global optimal path for the defensive autonomous entity.

[0136] In this embodiment, based on a combination of defense strategies, a path is planned for each defense agent, and a global path planning cost function is constructed for the defense cluster to optimize the path and generate a globally optimal path. This step aims to ensure that all defense agents achieve overall optimization of cluster movement while completing their assigned tasks.

[0137] The specific steps are as follows:

[0138] First, construct the global path planning cost function for the defense cluster. The formula used to describe the path optimization objective of the entire defense cluster is:

[0139]

[0140] Here, Δ0 is the offset adjustment constant, used to avoid excessive path weight due to an excessively small priority parameter.

[0141] Then, construct the global obstacle and collision avoidance cost function for the defense cluster. The formula used to handle the collision avoidance requirements between obstacles and defensive agents in a defense cluster is:

[0142]

[0143] like

[0144] in, This represents the distance between the defending entities, where r0 is the collision avoidance distance threshold.

[0145] Then, the global path length cost function and the global obstacle and collision avoidance cost function are combined to form the comprehensive cost function for global path planning, the formula of which is:

[0146]

[0147] Where w'1 and w'2 are the weight parameters for global path length cost and global obstacle and collision avoidance cost, respectively.

[0148] Finally, the global path planning cost function is solved using the particle swarm optimization algorithm to generate the globally optimal path for the defense cluster.

[0149] Understandably, the particle swarm optimization algorithm transforms the path planning problem into an optimization problem, finding the optimal solution to the cost function through global and local searches of particles.

[0150] Step 105: Adjust the motion state of the defensive agent through a linear feedback control algorithm to make it move along the globally optimal path, thereby achieving path tracking.

[0151] In this embodiment, a linear feedback control algorithm is first designed, and its formula is as follows:

[0152]

[0153] Where k is the feedback gain parameter, used to control the degree of adjustment; (x i y i () is the current position of the defensive autonomous entity. The coordinates of the target point on the globally optimal path

[0154] Then, a linear feedback control algorithm is used to adjust the movement state of the defensive agent in real time, so that it moves along the globally optimal path and approaches the target point on the planned path.

[0155] It should be noted that during the path tracking process, the defensive agent needs to dynamically update the target point: when the defensive agent approaches the current target point (the deviation distance is less than the threshold), the path planning system will update the next target point; the defensive agent sequentially completes all target points on the globally optimal path until it reaches the final target (boundary assault point).

[0156] Through a linear feedback control algorithm, the defensive agent can move precisely along the globally optimal path and achieve real-time path tracking in dynamic environments. This control strategy is characterized by its simple computation and rapid adjustment, making it suitable for the cooperative motion control of multiple agents. Combined with the results of the globally optimal path planning, the path tracking algorithm can significantly improve the execution accuracy and reliability of defense tasks, and has significant value in practical applications.

[0157] To implement the above embodiments, this application also proposes a multi-agent boundary game device based on priority path planning. Figure 2 This is a schematic diagram of a multi-agent boundary game device based on priority path planning, provided as an embodiment of this application. Figure 2 As shown, the device includes:

[0158] The assault time calculation module 100 is used to calculate the projection point and assault point of the intrusion direction based on the position, speed direction and shape of the defense area boundary of the intruder, and to calculate the relative assault time by combining the path length and maximum speed.

[0159] The defense time calculation module 200 is used to construct a local path length cost function and a local collision cost function based on the position of the defending agent and the obstacle environment, calculate the optimal path length, and combine the maximum speed of the defending agent to calculate the relative defense time.

[0160] The defense task allocation module 300 is used to calculate the priority parameters of each defense autonomous entity based on the relative attack time and relative defense time, and to optimize the maximum matching algorithm using the priority parameters to determine the combination of defense strategies.

[0161] The global path planning module 400 is used to plan a path for each defense autonomous entity based on the defense strategy combination, and to optimize the global path planning cost function using the particle swarm algorithm to generate the global optimal path for the defense autonomous entity.

[0162] The motion control module 500 is used to adjust the motion state of the defensive agent through a linear feedback control algorithm, so that it moves along the globally optimal path and achieves path tracking.

[0163] To implement the above embodiments, this application also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0164] To implement the above embodiments, this application also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0165] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0166] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0167] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0168] This application is intended to provide an implementation scheme for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0169] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0170] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0171] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0172] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0173] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0174] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0175] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0176] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

[0177] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0178] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A multi-agent boundary game method based on priority path planning, characterized in that, Includes the following steps: Based on the location, velocity direction, and shape of the defense zone boundary of the invading entity, calculate the projection point and attack point of the invasion direction, and combine the path length and maximum speed to calculate the relative attack time. Based on the defensive agent's position and obstacle environment, a local path length cost function and a local collision cost function are constructed to calculate the optimal path length, and the relative defense time is calculated by combining the defensive agent's maximum speed. Based on the relative attack time and the relative defense time, calculate the priority parameter of each defense autonomous entity, optimize the maximum matching algorithm using the priority parameter, and determine the combination of defense strategies. Based on the defense strategy combination, a path is planned for each defense agent, and the global path planning cost function is optimized using the particle swarm optimization algorithm to generate the globally optimal path for the defense agent. The motion state of the defensive agent is adjusted by a linear feedback control algorithm so that it moves along the globally optimal path, thus achieving path tracking. The step of planning a path for each defensive agent based on the defense strategy combination and optimizing the global path planning cost function using the particle swarm optimization algorithm to generate the optimal path for the defensive agent includes: Based on the aforementioned defense strategy combination, a global path planning cost function for the defense cluster is constructed. The formula is: in, Here, n is the offset adjustment constant, and n is the total number of path points. Indicates defensive self Priority parameters, These are the continuous location points of the defensive autonomous entity in path planning; Furthermore, the global obstacle and collision avoidance cost function for constructing the defense cluster. The formula is: in, Indicates the distance between the defending entities. The collision avoidance distance threshold; The global path planning cost function is solved using the particle swarm optimization algorithm to generate the globally optimal path for the defensive autonomous agent. The formula for the global path planning cost function is as follows: in, and These are the weight parameters for global path length cost and global obstacle and collision avoidance cost, respectively.

2. The method according to claim 1, characterized in that, The calculation of the projection point and attack point of the intrusion direction based on the position, velocity direction, and boundary shape of the defense area, and the calculation of the relative attack time in conjunction with the path length and maximum speed, includes: According to the intruding subject Current location and velocity direction vector Determine the direction of the intrusion; The intrusion direction and the target perimeter center Projection points of the connecting lines As a reference point, the formula is: in, Indicates from the invading self To the center of the target area vector With velocity direction vector The inner product; From the reference point Calculate the boundary assault points of the target area along the direction of invasion. The formula is: in, The radius of the target region. This represents the distance from the center of the target area to the projection point; Based on path length and invading the self Maximum speed The relative attack time is calculated using the following formula: in, For the purpose of invading the self to the border assault point The relative time for a surprise attack.

3. The method according to claim 2, characterized in that, The process involves constructing a local path length cost function and a local collision cost function based on the defensive agent's position and the obstacle environment to calculate the optimal path length, and combining this with the defensive agent's maximum speed to calculate the relative defense time, including: Constructing a local path length cost function The formula for calculating the path length from the current position of the defensive agent to the boundary assault point is as follows: = in, Let n be the continuous location points of the defensive autonomous entity in the path planning, and n be the total number of path points. Constructing the local obstacle and collision avoidance cost function This is used to handle obstacles and collision avoidance costs in the path, and the formula is: in, To defend against the distance from the main body to the center of the obstacle, The coordinates of the obstacle's center are... Where is the radius of the obstacle. The total number of obstacles; The local path planning cost function is solved using the particle swarm optimization algorithm to obtain the defensive agent. From initial position to the boundary assault point The length of the planned shortest path The formula for the local path planning cost function is as follows: in, and These are the weight parameters for local path length cost and local obstacle and collision avoidance cost, respectively. Based on the length of the shortest path and the maximum speed of the defending self The relative defense time is calculated using the following formula: in, To defend oneself to the border assault point The relative defense time.

4. The method according to claim 3, characterized in that, The process of calculating priority parameters for each defensive autonomous entity based on the relative attack time and the relative defense time, and using the priority parameters to optimize the maximum matching algorithm to determine the combination of defense strategies includes: Based on the relative attack time and the relative defense time, the priority parameter of each defensive autonomous entity is calculated using the following formula: in, Indicates defensive self Priority parameters; The maximum matching algorithm prioritizes matching defensive and intrusive entities with lower priority parameters. The matching criteria are as follows: For defensive and intrusive agents that meet the matching conditions, tasks are assigned according to their priorities, and a combination of defense strategies is generated.

5. The method according to claim 4, characterized in that, The step of adjusting the motion state of the defensive agent through a linear feedback control algorithm to make it move along the globally optimal path, thereby achieving path tracking, includes: Design a linear feedback control algorithm, with the following formula: in, For feedback gain parameters, The current position of the defensive autonomous entity; The linear feedback control algorithm adjusts the motion state of the defensive agent, causing it to move along the globally optimal path, thus achieving path tracking.

6. A multi-agent boundary game device based on priority path planning according to the method of any one of claims 1-5, characterized in that, Includes the following modules: The assault time calculation module is used to calculate the projection point and assault point of the intrusion direction based on the position, speed direction and shape of the defense area boundary of the intruder, and to calculate the relative assault time by combining the path length and maximum speed. The defense time calculation module is used to construct a local path length cost function and a local collision cost function based on the position of the defending agent and the obstacle environment, calculate the optimal path length, and combine it with the maximum speed of the defending agent to calculate the relative defense time. The defense task allocation module is used to calculate the priority parameter of each defense autonomous entity based on the relative attack time and the relative defense time, and to optimize the maximum matching algorithm using the priority parameter to determine the combination of defense strategies. The global path planning module is used to plan a path for each defensive autonomous entity based on the defense strategy combination, and to optimize the global path planning cost function using the particle swarm optimization algorithm to generate the global optimal path for the defensive autonomous entity. The motion control module is used to adjust the motion state of the defensive agent through a linear feedback control algorithm, so that it moves along the globally optimal path and achieves path tracking.

7. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-5.

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