A multi-robot distributed surrounding hierarchical method, device, equipment, medium and product

Abstract

This application discloses a multi-robot distributed encirclement layering method, apparatus, device, medium, and product, relating to the field of multi-robot distributed motion planning. The method includes: acquiring information data; constructing a chaser task sample; assigning tasks to chasers based on the chaser task sample and information data, and determining whether secondary assignment is needed to obtain the assignment result; employing a distributed motion planning algorithm based on the optimal following gap method to determine the chaser's final heading angle based on the assignment result; using the ORCA algorithm based on the final heading angle for speed adjustment processing to obtain the adjusted speed; updating the position information of the escapee and the chaser; and determining whether the encirclement phase is underway based on the position information and the conditions for successful encirclement. This application can improve the efficiency of multi-chaser encirclement of escapees and shorten the encirclement time.

Description

Technical Field

[0001] This application relates to the field of distributed motion planning for multi-robots, and in particular to a method, apparatus, device, medium, and product for distributed encirclement and layering of multi-robots. Background Technology

[0002] Multi-robot Pursuit-Evasion Game (MPE) is widely used in typical task scenarios such as area surveillance, target tracking, and wildlife monitoring. In this game, a group of robots consists of multiple pursuers and one escapee. The pursuers must adopt a cooperative strategy to capture the adversarial escapee, while the escapee will try to evade the pursuit of multiple pursuers. As a typical application scenario of MPE, pursuers need to cooperate to capture an escapee in a dense environment with multiple obstacles. The main challenge is the cooperation among pursuers, which requires dynamic reallocation of the capture target point, real-time motion planning to complete collision and obstacle avoidance, rapid approach to limit the movement of the adversarial escapee, and safe capture of the escapee. However, in complex environments with dense obstacles, multiple pursuers are prone to deadlock when planning the capture path, and the computational complexity of existing global planners is not sufficient for real-time requirements, making it difficult to achieve efficient capture of multiple pursuers. Meanwhile, existing control methods for capturing adversarial escapees place stringent requirements on the initial position and speed of the pursuers. If these constraints are weakened, it becomes extremely difficult to successfully capture the escapees. Existing methods struggle to surround randomly appearing escapees, making efficient capture by the pursuers impossible. Therefore, there is an urgent need for a distributed capture algorithm that considers the lack of initial position constraints for the pursuers, while simultaneously integrating task allocation and motion planning layers to achieve real-time dynamic task allocation and optimal path planning for efficient capture.

[0003] In existing technologies, when using control methods to implement a multi-robot chase-escape game, the capture success rate depends on the strict initial position and velocity conditions of all pursuers when the escapee is faster and more intelligent. That is, the pursuers' current position and velocity at the start of their pursuit of the escapee. However, in reality, the initial position distribution of pursuers is not uniformly dispersed around the escapee, exhibiting significant randomness. Therefore, there is a lack of research on the impact of the pursuers' initial position and real-time velocity on the capture effectiveness.

[0004] Furthermore, in environments with dense obstacles, multiple pursuers are more likely to fail in encircling an escapee, and the deadlock problem between pursuers or between pursuers and obstacles remains a challenge. How to safely and efficiently plan the movement trajectories of multiple pursuers to achieve rapid encirclement and capture remains to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a multi-robot distributed encirclement layering method, apparatus, device, medium and product, which can improve the efficiency of multiple pursuers in capturing escapees and shorten the capture time.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] In a first aspect, this application provides a multi-robot distributed encirclement and layering method, the multi-robot distributed encirclement and layering method comprising:

[0008] Acquire information data; the information data includes: the movement data of the pursuer, the movement data of the escapee, and the movement data of the obstacles; the movement data includes: position and velocity; the initial position in the movement data is randomly generated based on a motion model; the motion model is a mathematical model constructed based on the movement data; the velocity of the obstacles is always zero;

[0009] Construct a hunter task sample; the hunter task sample is determined based on the number of hunters, the speed thresholds of hunters and escapees, the capture radius of escapees, and the conditions for successful encirclement; the conditions for successful encirclement are: the distance between all hunters and the designated encirclement target point is less than a set distance, and the encirclement target point will be evenly distributed in the encirclement area with the escapee as the center and the encirclement radius as Rc; the speed thresholds of the hunters are not all greater than the speed threshold of the escapee;

[0010] Based on the chaser task sample and the information data, the chaser is assigned a task, and it is determined whether to perform a secondary assignment to obtain the assignment result; wherein, the judgment condition for performing a secondary assignment includes: the existence of path intersections;

[0011] A distributed motion planning algorithm based on the optimal follow gap method is used to determine the final heading angle of the pursuer according to the allocation result;

[0012] Based on the final heading angle, the ORCA algorithm is used to adjust the speed, resulting in the adjusted speed.

[0013] Update the position information of the escapee and the pursuer; the position information of the pursuer is determined based on the final heading angle and the adjusted speed; the position information of the escapee is determined based on the escapee's breakout strategy.

[0014] Based on the location information, a determination is made as to whether the operation is in the encirclement phase, based on the conditions for successful encirclement, and a determination result is obtained;

[0015] If the determination result is yes, then return to the step of "assigning tasks to the pursuers based on the pursuer task sample and the information data, and determining whether to perform secondary assignment to obtain the assignment result";

[0016] If the determination result is negative, the encirclement and capture mission ends.

[0017] Optionally, the motion model specifically includes:

[0018]

[0019] P = [x, y] T ;

[0020] Where P is the position vector; x is the x-coordinate of the position vector; y is the y-coordinate of the position vector; and T is the transpose. The first derivative of x; y is the first derivative; v is the linear velocity; w is the angular velocity; θ is the direction angle; It is the first derivative of θ.

[0021] Optionally, based on the pursuer task sample and the information data, tasks are assigned to the pursuers, and it is determined whether to perform secondary assignment to obtain the assignment result. Specifically, this includes:

[0022] Based on the chaser task samples and the information data, the chasers are initially assigned tasks based on the overall marginal reward function to obtain the initial assignment results; the overall marginal reward function is determined by a dynamic reallocation review consensus algorithm based on the distance discount factor and the relative speed factor.

[0023] Determine whether there is an intersection in the initial allocation results; the intersection is an intersection between pursuers or between a pursuer and the target point.

[0024] If so, the allocated target points will be pre-exchanged, and the overall marginal gain after pre-exchanging will be compared with the overall marginal gain before pre-exchanging to obtain the comparison result.

[0025] Based on the comparison results, determine whether to perform a secondary allocation, and obtain the allocation result;

[0026] If the comparison result shows that the overall marginal gain increases after the exchange, then the exchange process is performed; otherwise, the corresponding assigned capture target points are not exchanged; the overall marginal gain is determined based on the overall marginal reward function.

[0027] Optionally, a distributed motion planning algorithm based on the optimal follow-gap method is employed to determine the final heading angle of the pursuer according to the allocation result, specifically including:

[0028] The gap angle between adjacent obstacles within the pursuer's field of vision is determined based on the linear expansion coefficient; the linear expansion coefficient is negatively correlated with the distance between the pursuer and the adjacent obstacle.

[0029] Determine the adaptive safety factor;

[0030] The target heading angle is determined based on the clearance angle and the adaptive safety factor;

[0031] Determine whether the pursuer is close to the escapee and obtain a judgment result; the criterion for the closeness judgment is that, under the target heading angle, the distance between the pursuer and the escapee at the next moment is not greater than the distance between the pursuer and the escapee at the current moment;

[0032] If the judgment result is negative, then return "Determine the gap angle between adjacent obstacles within the pursuer's field of vision based on the linear expansion coefficient";

[0033] If the judgment result is yes, then the final heading angle is determined based on the target heading angle; the formula for calculating the final heading angle is:

[0034]

[0035] Where, φ final d is the final heading angle; α is the adaptive safety factor; d min φ is the minimum distance between the pursuer and an adjacent obstacle. gap_c φ is the gap angle; β is the target weight coefficient; goal The target heading angle.

[0036] Optionally, the expression for the adjustment speed is:

[0037]

[0038] in, To adjust the speed; Let v be the deflection velocity of pursuer A; v is the linear velocity. Let d be the set of allowed velocities of pursuer A within time τ; A|B (v) represents the distance from v to v The distance of any region's edge symbol.

[0039] Optionally, the escape strategy for the fleeing individual specifically includes:

[0040] If there are no pursuers within the escapee's line of sight, the escapee can move randomly; if a pursuer appears, the escapee will move away from the pursuer; in this case, the corresponding escape strategy for the first escapee is:

[0041]

[0042] If multiple pursuers appear around the escapee, the escapee will move away from the geometric center of all pursuers; in this case, the corresponding escape strategy for the second escapee is:

[0043]

[0044] When multiple pursuers surround the escapee, the escapee breaks out based on the angle bisector; at this time, the corresponding escape strategy of the third escapee is:

[0045]

[0046] Where, φ goall P represents the target heading angle of the escapee corresponding to the first escapee's breakout strategy; te P represents the relative positions of the escapee and the pursuer in the first escapee's breakout strategy. e The location of the escapee; P represents the geometric center of multiple pursuers; te ′ represents the relative positions of the escapee and the pursuer in the second escapee's breakout strategy; r e x is the radius of the escapee; pi Let x be the x-coordinate of the pursuer i; y be the x-coordinate of the pursuer i. pi ρ is the ordinate of the pursuer i's position; dir is the direction vector from the escapee to the center of the pursuer group; N Pe φ represents the number of escapees; i represents the pursuer ID index; φ represents the number of escapees. goal2 φ is the target heading angle of the escapee corresponding to the second escapee's breakout strategy. goal3 α represents the target heading angle of the escapee corresponding to the third escapee's breakout strategy. i β is the gap angle; i The angle of overlap.

[0047] Secondly, this application provides a multi-robot distributed encirclement and layering device, the multi-robot distributed encirclement and layering device comprising:

[0048] An information data acquisition module is used to acquire information data, which includes: the movement data of the pursuer, the movement data of the escapee, and the movement data of obstacles; the movement data includes: position and velocity; the initial position in the movement data is randomly generated based on a motion model; the motion model is a mathematical model constructed based on the movement data; the velocity of the obstacles is always zero.

[0049] The sample construction module is used to construct the pursuer task sample. The pursuer task sample is determined based on the number of pursuers, the speed thresholds of pursuers and escapees, the capture radius of escapees, and the conditions for successful encirclement. The conditions for successful encirclement are: the distance between all pursuers and the designated encirclement target point is less than a set distance, and the encirclement target point will be evenly distributed in the encirclement area with the escapee as the center and the encirclement radius as Rc. The speed thresholds of the pursuers are not all greater than the speed threshold of the escapees.

[0050] The allocation module is used to allocate tasks to the pursuers based on the pursuer task samples and the information data, and to determine whether to perform secondary allocation to obtain the allocation result; wherein, the judgment condition for secondary allocation includes: the existence of path intersections;

[0051] The final heading angle determination module is used to determine the final heading angle of the pursuer based on the allocation result using a distributed motion planning algorithm based on the optimal following gap method.

[0052] The adjustment module is used to perform speed adjustment processing based on the final heading angle using the ORCA algorithm to obtain the adjusted speed.

[0053] An update module is used to update the position information of the escapee and the pursuer; the position information of the pursuer is determined based on the final heading angle and the adjusted speed; the position information of the escapee is determined based on the escapee's breakout strategy.

[0054] The determination module is used to determine whether the area is in the encirclement stage based on the location information and the successful encirclement conditions, and to obtain a determination result.

[0055] The return module is used to return the "allocation module" when the determination result is yes;

[0056] The termination module is used to end the capture mission if the determination result is negative.

[0057] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-robot distributed encirclement layering method described above.

[0058] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-robot distributed encirclement and layering method described above.

[0059] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the multi-robot distributed encirclement and layering method described above.

[0060] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0061] This application provides a multi-robot distributed encirclement layering method, apparatus, device, medium, and product. By allocating tasks to pursuers based on task samples and information data, and determining whether secondary allocation is necessary, the allocation result is obtained, resolving the conflict problem of pathpoint intersections during multi-robot task allocation. A distributed motion planning algorithm based on the optimal following gap method is employed. The final heading angle of the pursuers is determined based on the allocation result, and then the ORCA algorithm is used for speed adjustment to obtain the adjusted speed. The position information of the escapee and pursuers is updated, and then the determination of whether the encirclement phase is underway is made based on the conditions for successful encirclement. This application introduces a linear expansion coefficient and an adaptive safety factor to determine a safe and fast optimal encirclement gap, smoothing the pursuer's movement trajectory. It combines FOGM with ORCA to solve the problem of missing velocity domain in FOGM, while also addressing the deadlock problem of ORCA in complex environments. By combining real-time dynamic task allocation and distributed motion planning, a layering method is constructed to quickly solve the problem of multiple pursuers encircling an escapee. Therefore, this application can improve the efficiency of multi-robot encirclement of escapees and shorten the encirclement time. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 A flowchart of a multi-robot distributed enclosing layered method;

[0064] Figure 2 A schematic diagram illustrating the overall process of implementing the method mentioned in this application;

[0065] Figure 3 A diagram showing intersecting paths between pursuers or between pursuers and the target point;

[0066] Figure 4 This is a schematic diagram of the allocation results of the secondary allocation;

[0067] Figure 5 This diagram illustrates the positions of the pursuer, the expanded obstacle, and the escapee at the optimal gap.

[0068] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0069] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0070] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0071] This application addresses the challenge of swarm encirclement by integrating real-time task allocation and motion planning at the multi-chaser level. At the task allocation layer, a Dynamic Reallocate Review Consensus (DRRC) algorithm is proposed. This algorithm performs full sampling of the task set, introduces a speed discount factor to optimize the objective function, and balances distance and speed factors to optimize the allocation of encirclement points, obtaining the task allocation result that maximizes the reward for each chaser. Furthermore, to address the issue of overlapping task points between adjacent chasers during task allocation, the encirclement target point of the overlapping chasers is changed without reducing the overall reward, effectively utilizing the allocation results from the previous time step to optimize the allocation of task points in the next time step, thus improving real-time allocation performance. At the motion planning layer, FOGM_ORCA (Follow the Optimal Gap Method - Optimal Reciprocal Collision Avoidance) is proposed. This method improves the following gap method through geometric analysis, introduces a linear expansion coefficient and an adaptive safety factor, and combines it with Optimal Reciprocal Collision Avoidance (ORCA) to provide feasible velocities for local planning. This allows multiple pursuers to simultaneously possess optimal angle and velocity domains, effectively shortening the capture time and improving capture efficiency. It effectively connects task allocation and motion planning, reducing the requirements for the initial positions of pursuers in the early stages of capture, balancing the impact of initial positions on capture performance, and enabling efficient, real-time capture of an escaped pursuer by multiple pursuers.

[0072] Through geometric analysis, a linear expansion coefficient and an adaptive safety factor are introduced to control the obstacle avoidance curvature of the pursuers, ensuring it remains within a safe gap region and maximizing the safety of the planned path while considering the impact of the target's heading. Simultaneously, the ORCA algorithm is used to achieve collision avoidance among pursuers within the team, addressing the velocity domain deficiency issue in FOGM. Furthermore, the optimal heading obtained from FOGM is used to optimize the high-dimensional solution in ORCA, allowing for obstacle avoidance at maximum speed even against static obstacles, thus improving planning efficiency. By combining dynamic task redistribution and distributed motion planning layers, the challenges of initial position and velocity constraints for pursuers are resolved, improving the efficiency of multiple pursuers in capturing escapees and shortening the capture time.

[0073] like Figure 1 As shown in the figure, this application provides a multi-robot distributed encirclement layering method, which includes:

[0074] Step 100: Acquire information data. Information data includes: the pursuer's motion data, the escapee's motion data, and the obstacle's motion data; motion data includes: position and velocity; the initial position in the motion data is randomly generated based on the motion model; the motion model is a mathematical model constructed based on the motion data; the obstacle's velocity is always zero.

[0075] Step 200: Construct the pursuer task sample. The pursuer task sample is determined based on the number of pursuers, the speed thresholds of pursuers and escapees, the capture radius of escapees, and the conditions for successful encirclement. The conditions for successful encirclement are: all pursuers are less than the set distance from the designated encirclement target point, and the encirclement target point will be evenly distributed in the encirclement area with the escapee as the center and the encirclement radius as Rc. The speed thresholds of pursuers are not all greater than the speed thresholds of escapees.

[0076] Step 300: Based on the pursuer's task samples and information data, assign tasks to the pursuers and determine whether to perform secondary assignment to obtain the assignment results. The criteria for secondary assignment include the existence of path intersections.

[0077] Step 400: Using a distributed motion planning algorithm based on the optimal gap following method, determine the final heading angle of the pursuer based on the allocation results.

[0078] Step 500: Based on the final heading angle, use the ORCA algorithm to perform speed adjustment processing to obtain the adjusted speed.

[0079] Step 600: Update the position information of the escapee and the pursuer. The pursuer's position information is determined based on the final heading angle and adjustment speed; the escapee's position information is determined based on the escapee's breakout strategy.

[0080] Step 700: Based on the location information and the conditions for successful encirclement, determine whether the encirclement is in progress and obtain the determination result.

[0081] Step 800: If the determination result is yes, return to step 300.

[0082] Step 900: If the result is negative, the encirclement mission ends.

[0083] Motion models, specifically including:

[0084]

[0085] P = [x, y] T ;

[0086] Where P is the position vector; x is the x-coordinate of the position vector; y is the y-coordinate of the position vector; and T is the transpose. The first derivative of x; y is the first derivative; v is the linear velocity; w is the angular velocity; θ is the direction angle; It is the first derivative of θ.

[0087] In one embodiment, tasks are assigned to the pursuers based on the pursuer task samples and information data, and it is determined whether a secondary assignment is needed to obtain the assignment result. Specifically, this includes:

[0088] Based on the task samples and information data of the pursuers, the pursuers are initially assigned tasks based on the overall marginal reward function to obtain the initial assignment results; the overall marginal reward function is determined by a dynamic reallocation review consensus algorithm based on the distance discount factor and the relative speed factor.

[0089] Determine if there are any intersections in the initial allocation results; an intersection is when there are intersecting paths between pursuers or between a pursuer and the target point.

[0090] If so, the allocated target points will be pre-exchanged, and the overall marginal gain after pre-exchange will be compared with the overall marginal gain before pre-exchange to obtain the comparison result.

[0091] Based on the comparison results, determine whether to perform a secondary allocation and obtain the allocation result.

[0092] If the comparison result shows that the overall marginal gain increases after the exchange, then the exchange is performed; otherwise, the corresponding assigned capture target points are not exchanged. The overall marginal gain is determined based on the overall marginal reward function.

[0093] In one embodiment, a distributed motion planning algorithm based on the optimal follow-gap method is used to determine the final heading angle of the pursuer according to the allocation result, specifically including:

[0094] The gap angle between adjacent obstacles within the pursuer's field of vision is determined based on the linear expansion coefficient; the linear expansion coefficient is negatively correlated with the distance between the pursuer and adjacent obstacles.

[0095] Determine the adaptive safety factor; determine the target heading angle based on the gap angle and the adaptive safety factor. Determine whether the pursuer is approaching the escapee and obtain the judgment result; the benchmark for the approach judgment result is that, under the target heading angle, the distance between the pursuer and the escapee at the next moment is not greater than the distance between the pursuer and the escapee at the current moment.

[0096] If the result is negative, return to the step "Determine the gap angle between adjacent obstacles within the pursuer's field of vision based on the linear expansion coefficient".

[0097] If the judgment result is yes, then the final heading angle is determined based on the target heading angle; the formula for calculating the final heading angle is:

[0098]

[0099] Where, φ final d is the final heading angle; α is the adaptive safety factor; d min φ is the minimum distance between the pursuer and an adjacent obstacle. gap_c φ is the gap angle; β is the target weight coefficient; goal The target heading angle.

[0100] The expression for adjusting the speed is:

[0101]

[0102] in, To adjust the speed; Let v be the deflection velocity of pursuer A; v is the linear velocity. Let d be the set of allowed velocities of pursuer A within time τ; A|B (v) represents the distance from v to v The distance of any region's edge symbol.

[0103] As an optional implementation method, the escapee breakout strategy specifically includes:

[0104] If there are no pursuers within the escapee's line of sight, the escapee can move randomly; if a pursuer appears, the escapee will move away from the pursuer; in this case, the corresponding escape strategy for the first escapee is:

[0105]

[0106] If multiple pursuers appear around the escapee, the escapee will move away from the geometric center of all pursuers; in this case, the corresponding escape strategy for the second escapee is:

[0107]

[0108] When multiple pursuers surround the escapee, the escapee breaks out based on the angle bisector; at this time, the corresponding escape strategy of the third escapee is:

[0109]

[0110] Where, φ goal1 P represents the target heading angle of the escapee corresponding to the first escapee's breakout strategy; te P represents the relative positions of the escapee and the pursuer in the first escapee's breakout strategy. e The location of the escapee; P represents the geometric center of multiple pursuers; te ′ represents the relative positions of the escapee and the pursuer in the second escapee's breakout strategy; r e x is the radius of the escapee; pi Let x be the x-coordinate of the pursuer i; y be the x-coordinate of the pursuer i. pi ρ is the ordinate of the pursuer i's position; dir is the direction vector from the escapee to the center of the pursuer group; N Pe φ represents the number of escapees; i represents the pursuer ID index; φ represents the number of escapees. goal2 φ is the target heading angle of the escapee corresponding to the second escapee's breakout strategy. goal3 α represents the target heading angle of the escapee corresponding to the third escapee's breakout strategy. i β is the gap angle; i The angle of overlap.

[0111] Based on the same inventive concept, this application also provides a multi-robot distributed encirclement and layering device for implementing the multi-robot distributed encirclement and layering method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the multi-robot distributed encirclement and layering device provided below can be found in the limitations of the multi-robot distributed encirclement and layering method described above, and will not be repeated here.

[0112] In one embodiment, a multi-robot distributed encirclement and layering device is provided, the device comprising:

[0113] The information data acquisition module is used to acquire information data, including: the movement data of the pursuer, the movement data of the escapee, and the movement data of the obstacles; the movement data includes: position and speed; the initial position in the movement data is randomly generated based on the movement model; the movement model is a mathematical model constructed based on the movement data; the speed of the obstacles is always zero.

[0114] The sample construction module is used to construct the pursuer task sample. The pursuer task sample is determined based on the number of pursuers, the speed thresholds of pursuers and escapees, the capture radius of escapees, and the conditions for successful encirclement. The conditions for successful encirclement are: the distance between all pursuers and the designated encirclement target point is less than a set distance, and the encirclement target point will be evenly distributed in the encirclement area with the escapee as the center and the encirclement radius as Rc. The speed thresholds of the pursuers are not all greater than the speed threshold of the escapee.

[0115] The allocation module is used to allocate tasks to the pursuers based on the pursuer task samples and information data, and to determine whether to perform secondary allocation to obtain the allocation result; among them, the judgment condition for secondary allocation includes: the existence of path intersection points.

[0116] The final heading angle determination module is used to determine the final heading angle of the pursuer based on the allocation result using a distributed motion planning algorithm based on the optimal follow gap method.

[0117] The adjustment module is used to perform speed adjustment processing based on the final heading angle using the ORCA algorithm to obtain the adjusted speed.

[0118] The update module is used to update the position information of the escapee and the pursuer; the position information of the pursuer is determined based on the final heading angle and adjustment speed; the position information of the escapee is determined based on the escapee's breakout strategy.

[0119] The determination module is used to determine whether the area is in the encirclement phase based on the location information and the conditions for successful encirclement, and to obtain the determination result.

[0120] The return module is used to return the "allocation module" if the determination result is yes.

[0121] The termination module is used to end the capture mission if the determination result is negative.

[0122] Existing collaborative pursuit methods fail to consider issues such as the random initial position setting of pursuers, the non-uniform speeds of pursuers and escapees, and the real-time task allocation for pursuers. This paper addresses these issues by analyzing the escapee's real-time state, performing distributed task allocation and real-time motion planning for pursuers, and selecting safe directions with target-oriented movement. This results in a more scalable pursuer pursuit strategy applicable to various complex environments, such as those with dense obstacles or where the escapee is in a narrow passage. This enhances the pursuer's adaptability, improving the success rate of multi-pursuer collaborative pursuits and shortening the pursuit time. The overall implementation process is as follows: Figure 2 As shown.

[0123] The escapee's speed is 0.6 m / s, and the pursuer's speed can be set within the range of 0.6 m / s to 1 m / s. Both the escapee and the pursuer have the same field of vision radius R. c =0.25m, the distance for a successful encirclement is d c =0.1m, set the safe distance between the pursuer and the obstacle to d0=2m, set the entire simulation area to 50m×50m, set the field of vision range of the pursuer and the escapee to S=10m, and randomly generate the initial positions of the pursuer and the escapee. The following implementation methods are all completed in MATLAB, with the aim of realizing the cooperative encirclement and capture of the escapee by multiple pursuers without initial position constraints.

[0124] like Figure 2 As shown, the specific implementation steps are as follows:

[0125] Step 1: Randomly generate the initial positions of the pursuer, the escapee, and the obstacles, and determine the initial speeds of the pursuer and the escapee.

[0126] Considering the need for the pursuer to have agile steering and reversing capabilities, the motion models of both the pursuer and the escapee are simplified. Neither the pursuer nor the escapee is constrained by a turning radius. The pursuer's position and velocity vectors are respectively: P = [x, y] T and The direction angles of the pursuer and the escapee are θ, and the linear velocity and angular velocity of the pursuer are v and w, respectively. Construct the motion models of the pursuer and the escapee:

[0127]

[0128] Among them, the sampling time t of the pursuer and the escapee is determined. s Calculate the position and direction of the pursuer at the next time step after time k, which is time k+1:

[0129]

[0130] Determine the number of escapees and pursuers. Based on the number of pursuers, determine the encirclement formation for the escapees. Define the maximum speeds of both the pursuers and escapees. The escapees and pursuers will evade and pursue at their maximum speeds. Note that the speed of the pursuers is not necessarily always faster than that of the escapees.

[0131] Both the pursuer and the escapee have the same field of vision, making it easy to identify adjacent obstacles and other pursuers. A capture radius is set for the escapee. Successful capture is achieved when all pursuers are less than a set distance d from the designated target point. min The target points for the manhunt will be evenly distributed around a circle centered on the escapee, with a radius of R. c Within the encirclement area.

[0132] Step 2: Construct task samples for the pursuers, perform the initial task assignment for the pursuers, and check for any overlap or secondary reassignment. Combine historical data to ensure that the current optimal assignment result always remains optimally consistent.

[0133] Step 2.1: Perform the initial task assignment for all pursuers.

[0134] P i ={P1, P2, P3, ... P n} represents the set of pursuers who need to quickly reach their assigned virtual target points, i.e., encirclement points. These encirclement points are evenly distributed around the escapee's center. An encirclement point can be represented as: T i ={T1, T2, T3, ..., T j ,…,T n Each pursuer can only be assigned one encirclement point during the sampling time, and all encirclement points have the same priority and importance factor q. j =1, T represents the set of task points. To minimize the travel distance between the pursuer and the escapee, the influence of their relative speed on the pursuer distribution in the dynamic environment is also considered. Therefore, a distance discount factor and a relative speed factor are introduced, denoted as λ = 0.95 and χ = 0.95, respectively. The total reward value f for pursuer i to reach the assigned target is... i (T i This can be represented as:

[0135]

[0136] in, It is the path between pursuer i and the encirclement point j. Let represent the relative velocities of pursuer i and the escapee. To obtain the assignment result for pursuer i, the unassigned target m is added to set T. i Therefore, the marginal reward ω of objective m is added. i(m) is shown in formula (4).

[0137]

[0138] Therefore, the marginal reward ω i (m) can be represented as shown in formula (5).

[0139]

[0140] Due to the marginal reward ω i The impact of (m) is considered, and since the objective function is a submodular function, the marginal reward always diminishes. Therefore, the overall marginal reward function f(T) for dynamic task redistribution can be expressed as:

[0141]

[0142] Once the task assignment is determined by the pursuer i, the optimal assignment will be removed from the current task list until all tasks have been completely assigned.

[0143] N a T represents the set of task samples for the pursuers. i This represents the task currently assigned to pursuer i. Each pursuer can dynamically update T in each iteration. i No communication is required. At each sampling time t s j * It can provide the highest marginal reward ω for the pursuer i. * The task is to assign a target i to a target i, where i is the set of IDs of all pursuers. The Dynamic Reassignment Review Consensus (DRRC) algorithm consists of two distinct phases: an auction phase and a consensus phase.

[0144] During the bidding phase, to obtain the globally optimal solution as much as possible, the sampling frequency for each pursuer is set to P=1. This means that each pursuer will consider all tasks to refine its respective task sample set N. a Calculate the maximum marginal gain for each pursuer until all pursuers have completed the auction process. During this phase, pursuers share their ID and the best task j. * Marginal gain ω * The current location and speed are compared with those of nearby pursuers. Simultaneously, pursuers can obtain useful information from their neighbors. Only once both parties confirm that they have received all the shared information will the pursuers proceed to the next consensus phase.

[0145] During the consensus phase, once all pursuers have received all the shared information, they negotiate to determine which pursuer will obtain the current globally optimal task. The corresponding maximum marginal gain ω is then selected. *The pursuer a*. If a* = i, then the ID of the pursuer i and the corresponding task will be deleted. If there are multiple tasks with the same maximum marginal gain, the remaining tasks will be reallocated until all tasks are assigned.

[0146] In the reallocation of the review consistency strategy, a is obtained through the MaxCons function. * and Best Task Directly assigned to the pursuer a * Other pursuers with different optimal tasks are not considered in this allocation. Otherwise, those with the same optimal task... The pursuers will be compared based on their marginal gains, and the pursuer with the highest marginal gain will be assigned a task.

[0147] Step 2.2: After all the tasks of the pursuers have been assigned, check whether there are any intersections in the assignment results between all adjacent pursuers, that is, whether there are intersecting paths between pursuers or between pursuers and the target point. Figure 3 As shown, if there is a conflict in task allocation, the allocated target points are swapped. The overall marginal gain after the swap is compared with the overall marginal gain before the swap. If the overall marginal gain increases after the swap, the task swap is considered optimal; otherwise, the corresponding allocated target points are not swapped.

[0148] Step 2.3: Store the current optimal allocation result for the pursuer. The next allocation will combine the historical optimal allocation results, that is, repeat steps 2.1 and 2.2 to complete the task allocation, considering whether the deviation between the current marginal gain and the value after the previous allocation result is minimized, while still requiring the total sum of marginal rewards to be maximized, ensuring the consistency of the optimal allocation result each time. Figure 4 As shown.

[0149] Store the current optimal allocation result for the pursuer. The next allocation will combine the historical optimal allocation result value to continue the allocation. That is, it will consider whether the value of the current marginal gain combined with the previous allocation result has decreased. At the same time, it is still necessary to maximize the total marginal reward to ensure the consistency of the optimal allocation result each time.

[0150] Step 3: Select an appropriate clearance angle based on FOGM to surround the area, and after determining the safe zone, always move at the maximum speed, integrating ORCA to provide the speed domain.

[0151] Considering the collision and obstacle avoidance problems between the pursuer and static and dynamic obstacles, a distributed motion planning algorithm based on the Follow-the-Gap Method (FOGM) is proposed. This algorithm improves upon the original Follow-the-Gap Method (FGM), which is a geometric obstacle avoidance method, by combining an adaptive safety factor and a linear expansion coefficient. A series of gap matrices are constructed by acquiring adjacent obstacles, and the optimal heading for the pursuer is calculated by identifying the point of maximum gap. The original FGM is compared with the gap angle φ. gap_c Target angle φ goal Safety factor α, target weight coefficient β, and minimum distance d between the pursuer and adjacent obstacles. min It involves five indicators. The formula for FGM is as follows:

[0152]

[0153] Step 3.1: Since FGM needs to calculate the clearance angle between adjacent obstacles for each pursuer, this angle is related to the obstacle radius. Considering the pursuer's obstacle avoidance safety and reliability, the obstacles need to be inflated; therefore, a linear expansion coefficient is introduced. The linear expansion coefficient is negatively correlated with the distance between the pursuer and adjacent obstacles, which helps to meet the safety margin during actual movement. The linear expansion coefficient is shown below:

[0154] r' = r_min + (r_max - r_min) * (d-D_min) / (D_max-D_min) (8)

[0155] Where D_min = 1.2 and D_max = 2.4.

[0156] Therefore, the gap angle will change as follows:

[0157]

[0158] Where P oi and P oi+1 P represents the coordinates of the tangent point between the center of the pursuer at the optimal gap and the expanded obstacle. e Indicates the current location of the escapee. For example... Figure 5 As shown. Identify all obstacles within the pursuer's field of vision, obtain the obstacle indices, calculate the angles between the expanded obstacles and the pursuer in sequence, and sort the gap angles from largest to smallest.

[0159] Step 3.2: When the pursuer surrounds the escapee, as the escapee moves rapidly, the pursuer will change its current gap angle according to the changes in adjacent obstacles. To adapt to the rapid changes in the environment, the pursuer's safety factor α needs to be adjusted to an adaptive safety factor α'. However, the existing method sets the safety factor to a fixed value, which does not meet the performance requirements of real-time surround capture and increases the possibility of collision avoidance between pursuers or between pursuers and obstacles. Therefore, the safety factor needs to be adjusted according to the pursuer's real-time planned trajectory to ensure safety. The goal is to keep the pursuer within the central safe area of ​​the gap as much as possible. The adaptive variable coefficient safety factor α' is based on the current position P. i The target location P of the pursuer gi and the center of tangency (P) after adaptively expanding the obstacle. Oi1 +P Oi2 ) / 2 is calculated in real time, such as Figure 5 As shown. α′ i For the adaptive safety factor of pursuer i, the adaptive safety factor α' can be determined according to α′. i Sure.

[0160]

[0161] Step 3.3: Calculate the target heading angle φ goal Combined with formula (7), determine the final heading angle φ of each pursuer at the next moment. final .

[0162] Step 3.4: According to φ final Calculate the position of each pursuer at the next moment. If the position will be far away from the escapee, return to step 3.1 and take the gap angle at the second largest gap as the optimal gap angle. Repeat this process until the optimal gap angle that is safe and suitable for encirclement is determined, and then determine the final heading angle.

[0163] Step 4: Adjust speed in real time using the ORCA algorithm.

[0164] The improved FOGM algorithm only considers the pursuer moving at a constant maximum speed, lacking real-time speed domain adjustment. When there are many obstacles or other pursuers within the pursuer's field of vision, collisions or deadlocks are highly likely to occur, leading to the failure of the capture mission. To improve the algorithm's real-time performance, FOGM is combined with ORCA, utilizing ORCA to provide the real-time speed domain missing in FOGM. The necessity of introducing ORCA is clarified by planning the real-time speeds of two pursuers A and B or one escapee C, with the radii corresponding to pursuers A and B being r respectively. A and r B When pursuer A and pursuer B appear within each other's respective danger avoidance ranges, a speed obstacle zone will appear, the formula of which is:

[0165]

[0166] Among them, P A This is the location of pursuer A, P. B That is the location of pursuer B, when At time τ, pursuer A and pursuer B will collide. Therefore, it is necessary to obtain the set of allowed velocities for pursuer A. v AB Let be the relative speed between pursuer A and pursuer B.

[0167] and It can be represented as:

[0168]

[0169] Therefore, by combining formulas (13) and (14), we can explain the optimal new speed for the pursuer.

[0170]

[0171] in, d represents the yaw velocity of pursuer A. A|B (v) indicates from v to The distance of any region's edge symbol.

[0172] Step 5: Design escape strategies for the escapees.

[0173] The complexity of the environment requires escapees to adopt various escape strategies and choose the best one based on the current situation.

[0174] (1) When there are no pursuers within the escapee's line of sight, the escapee may move randomly. If a pursuer appears, the escapee will move away from the pursuer.

[0175]

[0176] (2) When multiple pursuers appear around the escapee, the escapee will move away from the geometric center of all pursuers.

[0177]

[0178] (3) When multiple pursuers surround the escapee, the escapee will choose the angle bisector of the largest gap between the pursuers to break through.

[0179]

[0180] The escapee effectively verified the scalability of the method mentioned in this application by employing multiple strategies to move in dense environments.

[0181] Step 6: Update the movement status of the pursuer and the escapee. Based on the positional relationship between the pursuer and the escapee, and combined with the conditions for successful encirclement, determine the encirclement phase. If the encirclement is still ongoing, the pursuer needs to perform dynamic task reallocation and distributed movement planning in real time; if the encirclement fails or succeeds, the encirclement mission ends.

[0182] This application addresses the conflict problem of pathpoint intersections during multi-chaser task allocation by obtaining the optimal task allocation solution in real time. It combines historical optimal allocation data to determine the current optimal allocation relationship, ensuring the consistency of the optimal allocation solution at any given time. It improves the original following gap method by introducing a linear expansion coefficient and an adaptive safety factor to determine a safe and fast optimal encirclement gap, smoothing the chaser's movement trajectory. It combines FOGM with ORCA to solve the problem of missing velocity domain in FOGM and the deadlock problem in ORCA in complex environments. It combines real-time dynamic task allocation with distributed motion planning to construct a hierarchical method, quickly solving the problem of multiple chasers surrounding an escapee. Based on the proposed hierarchical method, it solves the initial constraint problem for chasers in dense environments.

[0183] This application discloses a novel hierarchical framework for multiple robots to surround an escapee, effectively solving the encirclement problem in dense environments with multiple pursuers and no pre-assigned initial position constraints. To obtain an approximate global optimum in dynamic task reallocation, a dynamic task allocation algorithm based on Dynamic Reassignment Review Consensus (DRRC) is proposed to resolve conflicts in all tasks. Each pursuer can dynamically calculate its total marginal reward based on the relationship between the allocation schemes of the previous and current time steps. This allocation process is highly correlated with the previous time step, ensuring consistency of the optimal result for each allocation and significantly improving allocation efficiency. A distributed motion planning algorithm based on the Optimal Gap Following Method (FOGM) is integrated with an adaptive safety factor and Optimal Reciprocal Collision Avoidance (ORCA). Through geometric analysis of pursuers and escapees in dense environments, a linear expansion coefficient and an adaptive safety factor are introduced to control the curvature of obstacle avoidance by pursuers, smoothing their motion trajectories and solving the velocity domain deficiency problem in FOGM. The algorithm plans the encirclement trajectory of multiple pursuers in real time, ultimately achieving efficient encirclement of an escapee.

[0184] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-robot distributed encirclement and layering method.

[0185] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0186] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0187] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0188] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0189] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0190] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0191] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0192] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0193] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A multi-robot distributed encirclement and layering method, characterized in that, The multi-robot distributed encirclement and layering method includes: Acquire information data; the information data includes: the movement data of the pursuer, the movement data of the escapee, and the movement data of the obstacles; the movement data includes: position and velocity; the initial position in the movement data is randomly generated based on a motion model; the motion model is a mathematical model constructed based on the movement data; the velocity of the obstacles is always zero; Construct a sample of pursuer tasks; this sample is determined based on the number of pursuers, the speed thresholds of pursuers and escapees, the capture radius of escapees, and the conditions for successful encirclement; the conditions for successful encirclement are: all pursuers are less than a set distance from the designated target point, and the target point will be evenly distributed around the escapee as the center, with a capture radius of [missing information]. Rc Within the encirclement area; the speed threshold of the pursuers is not always greater than the speed threshold of the escapee; Based on the chaser task sample and the information data, the chaser is assigned a task, and it is determined whether to perform a secondary assignment to obtain the assignment result; wherein, the judgment condition for performing a secondary assignment includes: the existence of path intersections; A distributed motion planning algorithm based on the optimal follow gap method is used to determine the final heading angle of the pursuer according to the allocation result; Based on the final heading angle, the ORCA algorithm is used to adjust the speed, resulting in the adjusted speed. Update the position information of the escapee and the pursuer; the position information of the pursuer is determined based on the final heading angle and the adjusted speed; the position information of the escapee is determined based on the escapee's breakout strategy. Based on the location information, a determination is made as to whether the operation is in the encirclement phase, based on the conditions for successful encirclement, and a determination result is obtained; If the determination result is yes, then return "Based on the chaser task sample and the information data, assign tasks to the chaser, and determine whether to perform secondary assignment to obtain the assignment result"; If the determination result is negative, the encirclement and capture mission ends.

2. The multi-robot distributed encirclement and layering method according to claim 1, characterized in that, The motion model specifically includes: ； ； in, It is a position vector; The x-coordinate corresponding to the position vector; The ordinate of the position vector; For transpose; for The first derivative; for The first derivative; Linear velocity; Angular velocity; It is the direction angle; for The first derivative.

3. The multi-robot distributed encirclement and layering method according to claim 1, characterized in that, Based on the pursuer task samples and the information data, tasks are assigned to the pursuers, and it is determined whether a secondary assignment is needed to obtain the assignment result. Specifically, this includes: Based on the chaser task samples and the information data, the chasers are initially assigned tasks based on the overall marginal reward function to obtain the initial assignment results; the overall marginal reward function is determined by a dynamic reallocation review consensus algorithm based on the distance discount factor and the relative speed factor. Determine whether there is an intersection in the initial allocation results; the intersection is an intersection between pursuers or between a pursuer and the target point. If so, the allocated target points will be pre-exchanged, and the overall marginal gain after pre-exchanging will be compared with the overall marginal gain before pre-exchanging to obtain the comparison result. Based on the comparison results, determine whether to perform a secondary allocation, and obtain the allocation result; If the comparison result shows that the overall marginal gain increases after the exchange, then the exchange process is performed; otherwise, the corresponding assigned capture target points are not exchanged; the overall marginal gain is determined based on the overall marginal reward function.

4. The multi-robot distributed encirclement and layering method according to claim 1, characterized in that, A distributed motion planning algorithm based on the optimal follow-gap method is used to determine the final heading angle of the pursuer according to the allocation result, specifically including: The gap angle between adjacent obstacles within the pursuer's field of vision is determined based on the linear expansion coefficient; the linear expansion coefficient is negatively correlated with the distance between the pursuer and the adjacent obstacle. Determine the adaptive safety factor; The target heading angle is determined based on the clearance angle and the adaptive safety factor; Determine whether the pursuer is close to the escapee and obtain a judgment result; the criterion for the closeness judgment result is that, under the target heading angle, the distance between the pursuer and the escapee at the next moment is not greater than the distance between the pursuer and the escapee at the current moment; If the judgment result is negative, return to the step "Determine the gap angle between adjacent obstacles within the pursuer's field of vision based on the linear expansion coefficient"; If the judgment result is yes, then the final heading angle is determined based on the target heading angle; the formula for calculating the final heading angle is: ； in, The final heading angle; An adaptive safety factor; The minimum distance between the pursuer and an adjacent obstacle; It is the gap angle; The target weight coefficient; The target heading angle.

5. The multi-robot distributed encirclement and layering method according to claim 1, characterized in that, The expression for the speed adjustment is: ； in, To adjust the speed; Let A be the deflection velocity of the pursuer. Linear velocity; For pursuer A in time The allowed velocity set within; From arrive The distance of any region's edge value; For the pursuers A and the pursuers B In time The allowed speed set within.

6. The multi-robot distributed encirclement and layering method according to claim 1, characterized in that, The escape strategies for the fleeing individuals specifically include: If there are no pursuers within the escapee's line of sight, the escapee can move randomly; if a pursuer appears, the escapee will move away from the pursuer; in this case, the corresponding escape strategy for the first escapee is: ； If multiple pursuers appear around the escapee, the escapee will move away from the geometric center of all pursuers; in this case, the corresponding escape strategy for the second escapee is: ； ； When multiple pursuers surround the escapee, the escapee breaks out based on the angle bisector; at this time, the corresponding escape strategy of the third escapee is: ； in, The target heading angle of the escapee corresponding to the first escapee's breakout strategy; The relative positions of the escapee and the pursuer corresponding to the first escapee's breakout strategy; The location of the escapee; The geometric center of multiple pursuers; The relative positions of the escapee and the pursuer in the second escapee's breakout strategy; The radius of the escapee; Let x be the x-coordinate of the pursuer i's position; Let be the ordinate of the pursuer's position i; The direction vector from the escapee to the center of the pursuer group; The number of pursuers; For the pursuer's ID index number; The target heading angle of the escapee corresponding to the second escapee's breakout strategy; The target heading angle of the escapee corresponding to the third escapee's breakout strategy; The gap angle between the pursuers; The angle of overlap between the pursuers; This is an adaptive safety factor.

7. A multi-robot distributed encirclement and layering device, characterized in that, The multi-robot distributed encirclement and layering device includes: An information data acquisition module is used to acquire information data, which includes: the movement data of the pursuer, the movement data of the escapee, and the movement data of obstacles; the movement data includes: position and velocity; the initial position in the movement data is randomly generated based on a motion model; the motion model is a mathematical model constructed based on the movement data; the velocity of the obstacles is always zero. The sample construction module is used to construct chaser task samples. These chaser task samples are determined based on the number of chasers, the speed thresholds of both chasers and escapees, the capture radius of the escapees, and the conditions for successful encirclement. The successful encirclement condition is that the distance between all chasers and the designated encirclement target point is less than a set distance, and the encirclement target point will be evenly distributed around the escapee as the center, with an encirclement radius of [missing information]. Rc Within the encirclement area; the speed threshold of the pursuers is not always greater than the speed threshold of the escapee; The allocation module is used to allocate tasks to the pursuers based on the pursuer task samples and the information data, and to determine whether to perform secondary allocation to obtain the allocation result; wherein, the judgment condition for secondary allocation includes: the existence of path intersections; The final heading angle determination module is used to determine the final heading angle of the pursuer based on the allocation result using a distributed motion planning algorithm based on the optimal following gap method. The adjustment module is used to perform speed adjustment processing based on the final heading angle using the ORCA algorithm to obtain the adjusted speed. An update module is used to update the position information of the escapee and the pursuer; the position information of the pursuer is determined based on the final heading angle and the adjusted speed; the position information of the escapee is determined based on the escapee's breakout strategy. The determination module is used to determine whether the area is in the encirclement stage based on the location information and the successful encirclement conditions, and to obtain a determination result. The return module is used to return the "allocation module" when the determination result is yes; The termination module is used to end the capture mission if the determination result is negative.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the multi-robot distributed encirclement layering method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the multi-robot distributed encirclement layering method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the multi-robot distributed encirclement layering method as described in any one of claims 1-6.

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