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CN-119740660-B - Multi-autonomous boundary game method based on priority path planning

CN119740660BCN 119740660 BCN119740660 BCN 119740660BCN-119740660-B

Abstract

The application provides a multi-autonomous boundary game method based on priority path planning, which comprises the steps of calculating relative assault time for an invaded self-body to reach a boundary assault point and relative defend time for the defend self-body to reach the boundary assault point, calculating priority parameters of each defend self-body based on the relative assault time and the relative defend time, optimizing a maximum matching algorithm by using the priority parameters, determining defend strategy combination, planning a path for each defend self-body according to the defend strategy combination, optimizing a global path planning cost function by using a particle swarm algorithm, generating a global optimal path of the defend self-body, and adjusting the motion state of the defend self-body through a linear feedback control algorithm to enable the defend self-body to move according to the global optimal path so as to realize path tracking. The application solves the problems of multi-autonomous defense task allocation and path planning in complex obstacle environments by combining the priority path planning and the maximum matching method, and effectively improves the defense success rate and the adaptability.

Inventors

  • MENG ZIYANG
  • DU ZIYU

Assignees

  • 清华大学

Dates

Publication Date
20260505
Application Date
20241206

Claims (9)

  1. 1. A multi-autonomous boundary gaming method based on prioritized path planning, comprising the steps of: According to the position, the speed direction and the boundary shape of the defending area of the intrusion self-body, calculating the projection point and the assault point of the intrusion direction, and calculating the relative assault time by combining the path length and the maximum speed; Constructing a local path length cost function and a local collision cost function according to the position of the defending self-body and the obstacle environment, calculating the optimal path length, and calculating the relative defending time by combining the maximum speed of the defending self-body; Calculating a priority parameter of each defending self-body based on the relative assault time and the relative defending time, and determining a defending strategy combination by utilizing the priority parameter optimization maximum matching algorithm; Planning a path for each defending self-body according to the defending strategy combination, optimizing a global path planning cost function by utilizing a particle swarm algorithm, and generating a global optimal path of the defending self-body; the motion state of the defending self-body is adjusted through a linear feedback control algorithm, so that the defending self-body moves according to a global optimal path, and path tracking is realized; The path planning method comprises the steps of planning a path for each defending self-body according to the defending strategy combination, optimizing a global path planning cost function by utilizing a particle swarm algorithm, and generating an optimal path of the defending self-body, wherein the method comprises the following steps: based on the defense strategy combination, constructing a global path planning cost function of a defense cluster The formula is: Wherein, the For the offset adjustment constant, n is the total number of path points, Representing defensive self-principals Is set to be a priority parameter of (1), Defend from the consecutive position point of the main body in the route planning; and constructing a global obstacle and collision prevention cost function of the defending cluster The formula is: Wherein, the Representing the distance between the defenses from the subjects, Is a collision prevention distance threshold; Solving a global path planning cost function through a particle swarm algorithm to generate a global optimal path defending a self-body, wherein the formula of the global path planning cost function is as follows Wherein, the And (3) with The weight parameters of the global path length cost and the global obstacle and collision prevention cost are respectively.
  2. 2. The method of claim 1, wherein calculating the projected points and the assault points of the intrusion direction based on the position, the speed direction, and the defending area boundary shape of the intrusion from the subject, and calculating the relative assault time in combination with the path length and the maximum speed, comprises: according to invasion from the host Is the current position of (2) And velocity direction vector Determining an invasion direction; centering the intrusion direction and target perimeter Projection point of connecting line As a reference point, the formula is: Wherein, the Representing slave intrusion from a subject To the center of the target area Vector of (3) And velocity direction vector Is an inner product of (2); From the datum point Calculating boundary impact points of a target area along an intrusion direction The formula is: Wherein, the For the radius of the target area, Representing the distance from the center of the target area to the projection point; according to path length And intrusion into an autonomous body Maximum speed of (2) And calculating the relative assault time, wherein the formula is as follows: Wherein, the For invading the host To the boundary assault point Is a relative assault time of (2).
  3. 3. The method of claim 2, wherein constructing a local path length cost function and a local collision cost function to calculate an optimal path length based on the position of the defending self-body and the obstacle environment, and calculating a relative defending time in combination with a maximum speed of the defending self-body, comprises: Construction of local path length cost function The path length of the defending self-body from the current position to the boundary impact point is calculated by the following formula: = Wherein, the Continuous position points defended from a main body in path planning are provided, and n is the total number of path points; Construction of local obstacle and collision prevention cost function The method is used for processing the obstacle and collision prevention cost in the path, and the formula is as follows: Wherein, the To defend against the distance from the body to the center of the obstacle, As the center coordinates of the obstacle, In order to be a radius of the obstacle, Is the total number of obstacles; solving a local path planning cost function through a particle swarm algorithm to obtain a defending autonomous body From the initial position to the boundary impact point Length of planned shortest path of (c) Wherein, the formula of the local path planning cost function is as follows: Wherein, the And (3) with The weight parameters are respectively the local path length cost and the local obstacle and collision prevention cost; According to the length of the shortest path And maximum speed defending from the subject Calculating the relative defense time, wherein the formula is as follows: Wherein, the To defend against autonomy To the boundary assault point Is a relatively defensive time of (a).
  4. 4. The method of claim 3, wherein the computing a priority parameter for each of the defending self-principals based on the relative assault time and the relative defending time, optimizing a maximum matching algorithm using the priority parameters, determining a defending policy combination, comprises: based on the relative assault time and the relative defending time, calculating a priority parameter of each defending self-body, wherein the formula is as follows: Wherein, the Representing defensive self-principals Priority parameters of (a); through the maximum matching algorithm, the defending self-body and the intrusion self-body with low priority parameters are preferentially matched, and the matching conditions are as follows: And distributing tasks to the defending self-body and the invading self-body which meet the matching condition according to the priority thereof, and generating a defending strategy combination.
  5. 5. The method of claim 4, wherein said adjusting the motion state of the defending self-body by the linear feedback control algorithm to move according to the global optimum path, and realizing the path tracking, comprises: Designing a linear feedback control algorithm, wherein the formula is as follows: Wherein, the In order to feed back the gain parameter, To defend against the current location of the subject; And adjusting the motion state of the defending self-body through the linear feedback control algorithm to enable the defending self-body to move according to the global optimal path so as to realize path tracking.
  6. 6. A multiple autonomous path planning based boundary gaming device based on the method of any of claims 1-5, comprising the following modules: the assault time calculation module is used for calculating projection points and assault points of the invasion direction according to the position, the speed direction and the boundary shape of the defending area of the invasion self-body, and calculating relative assault time by combining the path length and the maximum speed; The defending time calculation module is used for constructing a local path length cost function and a local collision cost function according to the position of the defending self-body and the obstacle environment to calculate the optimal path length, and calculating the relative defending time by combining the maximum speed of the defending self-body; The defending task allocation module is used for calculating the priority parameter of each defending self-body based on the relative assault time and the relative defending time, and determining defending strategy combination by utilizing the priority parameter to optimize the maximum matching algorithm; The global path planning module is used for planning a path for each defending self-body according to the defending strategy combination, optimizing a global path planning cost function by utilizing a particle swarm algorithm, and generating a global optimal path of the defending self-body; And the motion control module is used for adjusting the motion state of the defending self-body through a linear feedback control algorithm so as to enable the defending self-body to move according to a global optimal path and realize path tracking.
  7. 7. An electronic device comprising a processor and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-5.
  8. 8. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-5.
  9. 9. A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-5.

Description

Multi-autonomous boundary game method based on priority path planning Technical Field The application relates to the technical field of intelligent control and path planning, in particular to a multi-autonomous boundary game method based on priority path planning. Background In recent years, the challenge game problem in multi-player systems has become a research hotspot, such as chase-back game, target chase-back defense game, and boundary game problem. The boundary game problem is widely applied to unmanned aerial vehicle formation defense, marine unmanned ship defense and other scenes, and the research aim is that defenders prevent as many invaders as possible from breaking through a designated boundary, and the invaders try to break through the defense to enter a target area. In the existing boundary game research, the distribution of defending tasks is mostly realized through a maximum matching algorithm, namely, an intrusion target is distributed to each defending self-body according to the possibility of successful defending. However, these methods generally assume that there is no collision problem between the self bodies, and the requirements cannot be fully satisfied in a complicated practical application. Particularly in a limited space, when a large number of self-bodies are involved, collision between the self-bodies is almost unavoidable, and neglecting the collision problem may cause failure of the defending task. In addition, most of the existing researches are directed to simple barrier-free environment design algorithms, and complexity in actual scenes, such as existence of barriers and collision avoidance requirements of multiple self-bodies, are not fully considered. In practical applications such as island defense and boundary patrol, the defense scene often contains a complex obstacle structure, and the autonomous body needs to finish high-efficiency defense while avoiding collision, which puts forward higher requirements on the traditional algorithm. Therefore, aiming at the defects of the existing algorithm, how to realize the defending task allocation and path planning of the multi-self-body system under the complex obstacle environment and solve the collision problem among the self-bodies at the same time becomes an important subject to be solved in the current boundary game field. Disclosure of Invention The present application aims to solve at least one of the technical problems in the related art to some extent. To this end, a first object of the present application is to propose a multi-autonomous boundary gaming method based on prioritized path planning. A second object of the present application is to provide a multi-autonomous boundary gaming device based on prioritized path planning. A third object of the present application is to propose an electronic device. A fourth object of the present application is to propose a computer readable storage medium. A fifth object of the application is to propose a computer programme product. To achieve the above objective, an embodiment of a first aspect of the present application provides a multi-autonomous boundary game method based on priority path planning, including: According to the position, the speed direction and the boundary shape of the defending area of the intrusion self-body, calculating the projection point and the assault point of the intrusion direction, and calculating the relative assault time by combining the path length and the maximum speed; Constructing a local path length cost function and a local collision cost function according to the position of the defending self-body and the obstacle environment, calculating the optimal path length, and calculating the relative defending time by combining the maximum speed of the defending self-body; Calculating a priority parameter of each defending self-body based on the relative assault time and the relative defending time, and determining a defending strategy combination by utilizing the priority parameter optimization maximum matching algorithm; Planning a path for each defending self-body according to the defending strategy combination, optimizing a global path planning cost function by utilizing a particle swarm algorithm, and generating a global optimal path of the defending self-body; and adjusting the motion state of the defending self-body through a linear feedback control algorithm to enable the defending self-body to move according to the global optimal path so as to realize path tracking. Optionally, the calculating the projection point and the assault point of the intrusion direction according to the position, the speed direction and the boundary shape of the defending area of the intrusion self-body, and calculating the relative assault time by combining the path length and the maximum speed includes: From the current position of the subject I j in response to intrusion And a velocity direction vector u j＝(cosφj,sinφj), determining an intrusion direction; tak