Multi-agent cluster trajectory tracking control method and device

Abstract

The invention provides a multi-agent cluster trajectory tracking control method and device, and relates to the technical field of cluster control, and the method comprises the steps: obtaining the positions and speeds of a plurality of agents in a multi-agent cluster; based on the distance between the intelligent agents, the cohesion force and the damping force of the intelligent agents are determined, the cohesion force is used for avoiding collision between the intelligent agents, and the damping force is used for controlling the motion consistency of the multiple intelligent agents; determining a virtual center of the multi-agent cluster based on the positions and the speeds of the agents; based on the central speed and the central position of the virtual center, the navigation force of the multi-agent cluster is determined, and the navigation force is used for controlling the motion trail of the multi-agent cluster; and determining trajectory tracking control input of the intelligent agent according to the cohesive force, the damping force and the navigation force. The accuracy of multi-agent cluster trajectory tracking control can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of cluster control, and in particular to a multi-agent cluster trajectory tracking control method and device. Background Art

[0002] A multi-agent swarm is a distributed system composed of multiple autonomous agents, including drones, surface unmanned ships, underwater unmanned vehicles, unmanned vehicles, and mobile robots. These agents complete global tasks or achieve specific goals through local interactions and collaborations. Each agent usually has perception, decision-making, and execution capabilities. The swarm as a whole relies on local communication and feedback between individuals to achieve functions such as formation maintenance, target tracking, and obstacle avoidance.

[0003] Motion control is an important topic in the study of multi-agent swarms. As early as 1986, Reynolds proposed three basic principles for swarm motion: aggregation, each agent tries to get close to the neighboring agent; collision avoidance, agents avoid collisions; speed matching, try to keep the speed consistent with the neighboring agents. All swarm motion control methods should meet Reynolds' three principles. Trajectory tracking is an important aspect of swarm motion control. In the process of trajectory tracking, the cluster needs to meet the trajectory tracking accuracy of the cluster as a whole, while also being constrained by Reynolds' three principles.

[0004] Multi-agent swarm systems are a type of system that relies on distributed control and local interaction. Currently in such systems, multiple agents perform self-regulation and decision-making by sensing their surroundings and the states of neighboring agents. Unlike traditional centralized control systems, multi-agent swarms usually do not rely on a central controller, but instead achieve overall collaboration through local rules and communication between individuals. The decentralized architecture makes the system highly robust, flexible, and scalable, which makes it suitable for operation in unknown or dynamic environments. However, since the behavior of each agent is the result of distributed control, the overall behavior of the cluster is the superposition of individual behaviors. This superposition may lead to nonlinear behavior, resulting in unpredictable deviations or fluctuations in the trajectory tracking of the cluster, especially when the individual behaviors are not completely consistent. Therefore, a method is needed to improve the accuracy of trajectory tracking control of multi-agent swarms. Summary of the invention

[0005] The present application provides a multi-agent cluster trajectory tracking control method and device, which can improve the accuracy of multi-agent cluster trajectory tracking control.

[0006] In a first aspect of the present application, a multi-agent cluster trajectory tracking control method is provided, the method comprising:

[0007] Get the positions and velocities of multiple agents in a multi-agent cluster;

[0008] Determine the cohesion and damping force of the intelligent bodies based on the distance between the intelligent bodies, wherein the cohesion is used to avoid collision between the intelligent bodies, and the damping force is used to control the movement consistency of the plurality of intelligent bodies;

[0009] Determining a virtual center of the multi-agent cluster based on the position and velocity of the agent;

[0010] Determine the navigation force of the multi-agent cluster based on the center speed and center position of the virtual center, wherein the navigation force is used to control the motion trajectory of the multi-agent cluster;

[0011] The trajectory tracking control input of the intelligent body is determined according to the cohesive force, the damping force and the navigation force.

[0012] Optionally, the determining the cohesion and damping force of the intelligent agents based on the distance between the intelligent agents specifically includes:

[0013] Determine the neighboring intelligent entities of any intelligent entity according to the positions of each intelligent entity, wherein the neighboring intelligent entities are intelligent entities within a preset range of any intelligent entity;

[0014] Based on the speed of the neighboring agents, the damping force of any one of the agents is determined, and the damping force of any one of the agents is specifically expressed as follows:

[0015]

[0016] in, is the damping force of any agent, N i is a set of multiple neighboring agents, a ij is the interaction strength coefficient, p j is the velocity of the neighboring agent, p i is the speed of any of the agents;

[0017] Based on the position of the neighboring agent and the position of any one of the agents, the interaction strength coefficient is determined, which is specifically calculated by the following formula:

[0018]

[0019] Among them, a ij is the interaction strength coefficient, ‖q j -q i ‖ σ It means (q j -q i)’s σ norm, q i is the position of any agent, q j is the position of the jth neighboring agent, ρ h (z) is the window function, and the σ norm is defined as follows:

[0020]

[0021] Among them, the constant ε>0, ‖x‖ σ represents the σ norm of vector x, ‖x‖ represents the length of the vector, and x is a vector of any dimension.

[0022] Optionally, the determining the cohesion and damping force of the intelligent agents based on the distance between the intelligent agents specifically further includes:

[0023] Based on the position of any agent and the position of the neighboring agents, the cohesion of any agent is determined, wherein the neighboring agents are agents within a preset range of the any agent, and the cohesion of any agent is specifically expressed as follows:

[0024]

[0025] Among them, f i g is the cohesion of any agent, N i is a set of multiple neighboring agents, q i is the position of any agent, q j is the position of the jth neighboring agent, Φα is the distance function with distance z as the independent variable, n ij is the position vector of the position of any agent pointing to the position of the jth neighboring agent, which is calculated by the following formula:

[0026]

[0027] Among them, q i is the position of any agent, q j is the position of the jth neighboring agent.

[0028] Optionally, the distance function is determined based on the distance between any one of the agents and the neighboring agents, which is specifically expressed by the following formula:

[0029]

[0030] Among them, Φ α (z) is the distance function with distance z as the independent variable, q i is the position of any agent, q jis the position of the jth neighboring agent, r a is the normalized scale, d a is a constant offset.

[0031] Optionally, determining the navigation capability of the multi-agent cluster based on the center speed and center position of the virtual center specifically includes:

[0032] Obtaining a target speed and a target position of a preset target point, wherein the target point is any point on a preset motion trajectory of the multi-agent cluster;

[0033] The navigation force is determined based on the target speed, the target position, the center speed, and the center position, which is specifically expressed by the following formula:

[0034]

[0035] in, is the navigation capability of the multi-agent cluster, q i is the position of any agent, p i is the speed of any one of the agents, the neighboring agents are agents within the preset range of any one of the agents, e 1 、e 2 、e 3 and e 4 is the control parameter, q N+1 is the center position, p N+1 is the central velocity, q A is the target position, p A is the target speed.

[0036] Optionally, the trajectory tracking control input of the multi-agent cluster is determined according to the cohesion, the damping force and the navigation force, and the trajectory tracking control input of any agent is specifically expressed by the following formula:

[0037]

[0038] Where ui is the trajectory tracking control input of any agent, c 1 、c 2 and c 3 is the proportionality coefficient, is the cohesion of any of the agents, is the damping force of any of the agents, is the navigation capability of the multi-agent cluster.

[0039] Optionally, determining the virtual center of the multi-agent cluster based on the position and speed of the agent specifically includes:

[0040] According to the positions of each of the intelligent agents, the center position is determined, which is specifically calculated by the following formula:

[0041]

[0042] Among them, q N+1 is the central position, N is the number of the multiple agents, q i is the position of the ith agent;

[0043] The center speed is determined according to the speed of each of the intelligent agents, and is specifically calculated by the following formula:

[0044]

[0045] Among them, p N+1 is the center speed, N is the number of the multiple agents, p i is the speed of the ith agent;

[0046] The point corresponding to the center position is set as the virtual center, and the center speed is set as the speed of the virtual center.

[0047] In a second aspect of the present application, a multi-agent cluster trajectory tracking control device is provided, the device comprising an acquisition module, a processing module and a judgment module, wherein:

[0048] The acquisition module is used to acquire the positions and speeds of multiple agents in a multi-agent cluster;

[0049] The processing module is used to determine the cohesion and damping force of the intelligent bodies based on the distance between the intelligent bodies, wherein the cohesion is used to avoid collision between the intelligent bodies, and the damping force is used to control the movement consistency of the plurality of intelligent bodies;

[0050] The processing module is used to determine the virtual center of the multi-agent cluster based on the position and speed of the agent;

[0051] The processing module is used to determine the navigation force of the multi-agent cluster based on the center speed and center position of the virtual center, and the navigation force is used to control the motion trajectory of the multi-agent cluster;

[0052] The judgment module is used to determine the trajectory tracking control input of the intelligent body according to the cohesive force, the damping force and the navigation force.

[0053] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0054] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0055] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0056] 1. This application coordinates the motion behavior of intelligent agents from multiple levels by comprehensively considering the local interaction forces (cohesion and damping force) between intelligent agents and the global navigation force of the cluster as a whole. Cohesion ensures that the intelligent agents avoid collisions and maintain a certain formation, and the damping force makes the speeds of the intelligent agents gradually tend to be consistent, thereby enhancing the overall coordination of the cluster. The introduction of the navigation force based on the virtual center enables the cluster to better follow the target trajectory and avoid the deviation of individual behavior from the global goal. In this way, a balance is achieved between local stability and global accuracy, thereby improving the accuracy of trajectory tracking control of the multi-agent cluster.

[0057] 2. By introducing the interaction strength coefficient and norm, the damping force between agents is dynamically adjusted to achieve speed consistency. The interaction strength coefficient is calculated based on the distance between agents, ensuring that the closer the distance, the stronger the interaction between agents. The introduction of the norm further adjusts the effect of distance nonlinearly, so that the interaction between agents does not completely disappear even at a longer distance. The use of window functions can smoothly adjust the interaction strength within a certain range. Agents can dynamically adjust their speed according to the neighboring relationship to avoid excessive speed differences, thereby improving the overall coordination and consistency of the cluster and achieving more accurate trajectory tracking.

[0058] 3. By calculating cohesion, agents can maintain a reasonable distance and avoid collisions. Cohesion is calculated through the positional relationship of neighboring agents. It depends on the distance function and position vector, and can dynamically adjust the attraction or repulsion between agents. When two agents are close to each other, the cohesion increases, so that they move away from each other and avoid collisions; when the distance is far, the cohesion decreases, and the attraction dominates, making them close together, thereby maintaining the overall tightness of the formation. Through such a design, the agents in the cluster can avoid collisions while achieving motion consistency, improving the stability of the cluster and the effectiveness of task execution.

[0059] 4. By introducing navigation force, the multi-agent cluster is guided to move along the preset trajectory. The calculation of navigation force not only takes into account the individual position and speed of the agent, but also comprehensively considers the global position and speed of the virtual center, as well as the target position and target speed of the preset target point. By controlling the weighted balance of these factors by control parameters, the cluster as a whole is guided and adjusted. The effect of this solution is that it can enable the cluster as a whole to track the preset trajectory more accurately, reduce deviations, and improve the task execution efficiency and trajectory tracking accuracy of the cluster while ensuring the movement of individual agents. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of a multi-agent cluster trajectory tracking control method disclosed in an embodiment of the present application;

[0061] Figure 2 A schematic diagram of multi-agent cluster movement of a simulated intelligent agent at a moment disclosed in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of a multi-agent cluster motion simulation at time 0.5s disclosed in an embodiment of the present application;

[0063] Figure 4 A schematic diagram of a multi-agent cluster motion simulation at the 1st second moment disclosed in an embodiment of the present application;

[0064] Figure 5 A schematic diagram of a multi-agent cluster motion simulation at the 3rd second moment disclosed in an embodiment of the present application;

[0065] Figure 6 A schematic diagram of a multi-agent cluster motion simulation at the 7th second disclosed in an embodiment of the present application;

[0066] Figure 7 A schematic diagram of a multi-agent motion trajectory disclosed in an embodiment of the present application;

[0067] Figure 8 It is a module schematic diagram of a multi-agent cluster trajectory tracking control device disclosed in an embodiment of the present application;

[0068] Fig. 9 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0069] Explanation of the reference numerals: 801, acquisition module; 802, processing module; 803, judgment module; 901, processor; 902, communication bus; 903, user interface; 904, network interface; 905, memory. DETAILED DESCRIPTION

[0070] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0071] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0072] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0073] The multi-agent swarm system is composed of multiple autonomous agents, including drones, surface unmanned ships, mobile robots and other equipment, and completes global tasks through distributed control and local interaction. Motion control is its core research content, and it must follow the three principles proposed by Reynolds: aggregation, collision avoidance, and speed matching. Especially in the process of trajectory tracking, the cluster must not only ensure trajectory accuracy, but also meet these principles. Although the decentralized architecture improves the robustness and scalability of the system, the superposition of individual behaviors may lead to nonlinear deviations, which in turn affects the accuracy of trajectory tracking. Therefore, effective methods are needed to improve control accuracy.

[0074] This embodiment discloses a multi-agent cluster trajectory tracking control method, referring to Figure 1 , including the following steps S110-S150:

[0075] S110, obtaining the positions and speeds of multiple agents included in the multi-agent cluster.

[0076] The dynamic modeling of multi-agent clusters is the basis of the entire system, which describes the motion state of each agent and its changing rules. Assume a cluster composed of N agents, each of which has its own motion state (position, speed). Agents can interact with each other through perception or communication, and each agent has a certain perception of the surrounding environment (including other agents and obstacles).

[0077] For any agent, the following two dynamic equations are used to describe its motion state:

[0078]

[0079] Among them, q i Indicates position, p i Indicates speed, u i Represents the control input of any agent.

[0080] The agent's position q i is its speed p i In discrete time, this equation can be expressed as:

[0081] q i (t+1)=q i (t)+p i (t)·Δt

[0082] Where Δt is the time step. Similarly, the speed of the agent p i The control input u i Similarly, in discrete time, it can be expressed as:

[0083] p i (t+1)=p i (t)+u i (t)·Δt

[0084] The state of each agent is composed of its position qi and velocity pi, so the state of the i-th agent can be expressed as x i =[q i , p i ] T The state space of the entire multi-agent system is a high-dimensional space composed of the states of all agents. The state vector of the system can be expressed as X = [x 1 ,x 2 ,…,x N ] T .

[0085] The control input of each agent is determined by many factors, including the status of other agents, target location, obstacle avoidance requirements, etc. The control input design can use traditional control methods (such as PID control) or combine artificial intelligence methods (such as reinforcement learning) to design more intelligent control laws.

[0086] S120, determining the cohesion and damping force of the intelligent agents based on the distance between the intelligent agents.

[0087] In multi-agent swarm control, the interaction force between agents is a key factor in achieving swarm collaboration and motion control. Common interaction forces include cohesion, damping, and navigation. They are responsible for avoiding collisions, maintaining velocity consistency, and guiding the swarm to move along a predetermined trajectory, respectively. Cohesion is calculated based on the positional relationship of neighboring agents, and its goal is to bring the agents close together without collision. Damping force is an interaction force based on velocity difference, and its purpose is to control the velocity consistency between multiple agents. By applying damping force, the velocity difference between agents can be reduced, thereby avoiding the phenomenon of inconsistent velocity in the swarm.

[0088] The damping force is usually calculated by the speed difference of neighboring agents. For any agent in a multi-agent cluster, a preset range (also called perception radius r) is set according to the position of each agent in the cluster to define the neighboring area that an agent can perceive. Usually, this range is set according to the task requirements and environmental characteristics of the cluster. For any agent, by calculating the distance between it and other agents, it is determined which agents are within its preset range (i.e., distance || q j -q i ||≤r), the set of agents within the preset range is called the neighboring agent set of the agent.

[0089] Based on the velocities of neighboring agents, the damping force of the agent is calculated. The damping force is a function of the velocity difference between the agent and its neighbors and is used to control the consistency of the motion. The formula for the damping force is:

[0090]

[0091] in, is the damping force of any agent, N i is a set of multiple neighboring agents, a ij is the interaction strength coefficient, p j is the velocity of the neighboring agent, p i is the speed of any agent.

[0092] Based on the position of any agent and the positions of neighboring agents, the interaction strength coefficient is determined, which is calculated by the following formula:

[0093]

[0094] Among them, a ij is the interaction strength coefficient, which is used to represent the interaction strength between any agent i and the jth neighboring agent. The value varies in the interval [0,1]. h (z) is the window function. ‖q j -q i ‖ σ It means (q j -q i )'s σ norm q i is the position of any agent, q j is the position of the jth neighboring agent, and the σ norm is defined as follows:

[0095]

[0096] Among them, the constant ε>0, ‖x‖ σ represents the σ norm of vector x, ‖x‖ represents the vector length, and x is a vector of any dimension. ||x|| σ It is a norm of nonlinear transformation, which is suitable for scenarios where small-scale effects of large-distance transformation need to be considered. This norm definition introduces a small parameter ∈ to nonlinearly scale the standard Euclidean distance ||x||. When ∈ is very small, ||x|| σ Approximately equal to ||x||; when ∈ is large, the norm becomes more sensitive to large distances. When dealing with a wide range of distances, this nonlinear scaling can more accurately describe the impact of long distances without increasing the computational complexity due to excessive distances.

[0097] Window function ρ h (z) is defined as:

[0098]

[0099] This is a window function with smooth transition, which is used to adjust the range and intensity of interaction between agents. When the distance z is small, that is, when z is in the interval [0,h], the interaction intensity remains at the maximum (i.e. 1). When z is in the transition interval [h,1], the interaction intensity gradually decreases and smoothly transitions to zero. When z>1, the interaction intensity directly becomes zero, indicating that the distance between the agents is too large and they no longer interact with each other. This function simulates the smooth attenuation of the interaction force with distance in reality, ensuring that when the agents are far apart, the interaction gradually disappears, avoiding abrupt changes.

[0100] Cohesion is usually calculated by the position difference between neighboring agents. Based on the position of any agent and the positions of neighboring agents, the cohesion of any agent is determined. The cohesion of any agent is specifically expressed as follows:

[0101]

[0102] in, is the cohesion of any agent, N i is a set of multiple neighboring agents, q i is the position of any agent, q j is the position of the jth neighboring agent, Φα is the distance function with distance z as the independent variable, n ij is the position vector of any agent pointing to the position of the jth neighboring agent, which is calculated by the following formula:

[0103]

[0104] Among them, q i is the position of any agent, q j is the position of the jth neighboring agent.

[0105] This formula is used to avoid collisions between multiple agents, mainly considering the relative position difference. j -q i Calculate the position vector n ij , and then through the distance function Φ α Measures the influence of neighboring agents on the current agent. This part actually simulates a kind of "repulsion force". When the distance between agents is too close, the repulsion force increases, forcing them to stay away from each other and avoid collision.

[0106] In the formula, Φ α (||q j -q i || σ ) is a distance function, which is used to measure the distance effect between agents and is generally expressed by a Gaussian function or other forms. α (z) is the distance function with distance z as the independent variable:

[0107]

[0108] The distance function is used to measure the interaction force between two agents. It is obtained by a window function ρ h and another potential function Φ(zd α) is calculated by the product. It remains 1 when the distance is close, that is, it acts with full force; when the distance is moderate, it gradually weakens as the distance increases; when the distance is too far, the acting force is 0. This design can ensure that the agent only has a significant impact on nearby neighbors. The potential function Φ(z - d α ) represents the strength of the acting force, z represents the distance between two agents, and the constant offset d α is a threshold that controls the zero point of the acting force. By adjusting d α , the minimum allowable distance between agents can be defined.

[0109] Among them, the normalization scale r a and the constant offset d a are calculated as follows respectively:

[0110]

[0111]

[0112] Among them, the potential function Φ(z) is expressed as follows:

[0113]

[0114] Among them:

[0115]

[0116] The constants a and b satisfy 0 < a ≤ b, and c satisfies the following conditions:

[0117]

[0118] S130. Based on the positions and velocities of the agents, determine the virtual center of the multi-agent cluster.

[0119] First, obtain the positions of each agent (such as two-dimensional or three-dimensional space coordinates), and these position information are used to calculate the center position of the cluster. And obtain the velocities of each agent (i.e., velocity vectors), and these velocity information are used to calculate the center velocity of the cluster. Based on the positions and velocities of the agents, calculate the center position and center velocity of the virtual center. According to the positions of all agents, calculate the center position of the cluster, and its formula is:

[0120]

[0121] Among them, q N+1 is the center position, N is the number of multiple agents, q i is the position of the i-th agent.

[0122] According to the velocities of all agents, calculate the center velocity of the cluster, and its formula is:

[0123]

[0124] Among them, p N+1 is the center speed, N is the number of agents, p i is the speed of the ith agent.

[0125] The calculated center position is used as the position of the virtual center, and the center speed is used as the speed of the virtual center. It should be noted that the virtual center does not represent a real physical intelligent entity, but a mathematical reference point that guides the movement of the entire cluster.

[0126] S140, determining the navigation capability of the multi-agent cluster based on the center speed and center position of the virtual center.

[0127] The navigation force of a multi-agent swarm is used to control the motion trajectory of the swarm, ensuring that the entire swarm can move along the predetermined trajectory. The core idea to achieve this goal is to determine the navigation force based on the motion state of the virtual center and the motion state of the target point. By properly designing the navigation force, the swarm can effectively follow the target trajectory. First, the target position and target speed of the preset target point need to be obtained. The target point is any point on the trajectory that the swarm is to follow, and its position and speed determine the direction and speed of the swarm's movement at different times.

[0128] The purpose of the navigation force is to guide the cluster as a whole to move towards the target trajectory, ensuring that the cluster can smoothly follow the target trajectory while maintaining consistency. The formula for the navigation force is as follows:

[0129]

[0130] in, is the navigation capability of the multi-agent cluster, q i is the position of any agent, p i is the speed of any of the agents, the neighboring agents are agents within the preset range of any of the agents, e 1 、e 2 、e 3 and e 4 is the control parameter, q N+1 is the center position, p N+1 is the center velocity, q A is the target position, p A is the target speed.

[0131] In the above formula, the first term -e 1 (q i -q A ) represents the correction force when the position of the agent deviates from the target position. By adjusting e 1, which can adjust the degree to which the position of each agent in the cluster follows the target position. The second item -e 2 (p i -p A ) represents the correction force when the speed of the agent deviates from the target speed. It is used to adjust the speed of the agent to keep it consistent with the target speed. 3 (q N+1 -q A ) represents the correction force when the virtual center position deviates from the target position, which is used to adjust the position of the entire cluster so that the cluster as a whole tends to the target position. 4 (p N+1 -p A) The correction force representing the virtual center speed deviating from the target speed is used to adjust the speed of the cluster as a whole so that the cluster speed is consistent with the target speed. The combination of these four forces ensures that each agent in the cluster can adjust according to the target position and speed, so that the cluster as a whole can smoothly and consistently follow the preset trajectory.

[0132] The parameters in the formula determine the relative weights of the various forces in the navigation force. These parameters can be adjusted according to actual needs. For example, if stronger cluster consistency is required, the weights of e3 and e4 can be increased; if more emphasis is placed on the accuracy of individual agents, the weights of e1 and e2 can be increased. Guided by the above navigation force, the cluster can smoothly follow the preset motion trajectory. Whether in a static or dynamic environment, this navigation force can help the cluster maintain consistent movement and complete the predetermined task.

[0133] S150, determining the trajectory tracking control input of the intelligent body according to the cohesion force, the damping force and the navigation force.

[0134] The trajectory tracking control input of any agent is decomposed into three parts, each of which implements different functions. Cohesion refers to the attraction of an agent to neighboring agents in order to avoid collision. It ensures that agents do not get too close or collide by detecting the positional relationship of neighboring agents. Damping force is used to control the speed consistency of agents. It ensures that the speed difference between neighboring agents is reduced, and ultimately achieves speed synchronization of the entire cluster. Navigation force is used to guide the entire cluster to move along a predetermined trajectory. It takes into account the position and speed differences between the virtual center and the target point of the cluster.

[0135] In order to realize the trajectory tracking control input of each agent, the above three force items (cohesion, damping force and navigation force) are weighted and summed. The specific formula is as follows:

[0136]

[0137] Among them, ui is the trajectory tracking control input of any agent, c 1 、c 2 and c 3 is the proportionality coefficient, is the cohesion of any agent, is the damping force of any agent, f i n Navigation capability of multi-agent swarms.

[0138] These coefficients are used to adjust the degree of influence of cohesion, damping and navigation forces on the final control input. In practical applications, these coefficients need to be adjusted according to specific tasks and environments to balance the stability of the cluster and the accuracy of motion tracking.

[0139] The final result calculated is the control input acting on the agent, which will be passed to the agent's motion controller to adjust the agent's speed and direction, thereby achieving coordinated movement of the entire cluster.

[0140] The entire control process can be divided into the following steps: Each agent senses its own state (position and speed) and the state of neighboring agents through sensors. Based on the position and speed of neighboring agents, the cohesion and damping forces are calculated. At the same time, based on the information of the virtual center and the target point, the navigation force is calculated. Each force item is synthesized into a control input according to a certain weight. Based on the calculated control input, the agent adjusts its motion state (speed and direction) to ensure that the cluster as a whole can follow the predetermined trajectory.

[0141] In a possible implementation, a multi-agent cluster trajectory tracking control method disclosed in the present application is used to perform a multi-agent cluster trajectory tracking simulation verification. In the Cartesian coordinate system of the XY plane, a cluster with N=30 agents is studied to track a preset trajectory. The positions of the agents are randomly and uniformly distributed in the rectangular area of ​​x∈[0,50], y∈[0,50], and the speeds are all 0. The parametric equation of the preset tracking trajectory is x(t)=50+50t; y(t)=200-50sin(t). The simulation duration is set to 7s, and the simulation step size is set to 0.01s. Simulation parameters related to aggregation force: ε=0.1, a=b=4.5, c=0, h=0.2, d=16, r=20. Simulation parameters related to navigation force: e 1 =1000,e 2 =100, e 3 =1000,e 4 =100. Proportional coefficient c 1 =c 2 =c 3 =1. Reference Figure 2, the figure shows the cluster position of the simulated intelligent agent at any moment. Simulated intelligent agent, 30 intelligent agents randomly appear in the rectangular area x∈[0,50], y∈[0,50]. Figure 3 , the figure shows the cluster position, cluster trajectory and target trajectory at the 0.5s simulation time. The cluster is approaching the target trajectory. Figure 4 , the figure shows the cluster position, cluster trajectory and target trajectory in the first second of simulation. The cluster has basically reached the target position. Figure 5 The figure shows the cluster position, cluster trajectory and target trajectory at the 3rd second of the simulation. The cluster continues to track the target trajectory with a high level of tracking accuracy. Figure 6 ,The figure shows the cluster position, cluster trajectory and target trajectory at the 7th second of the simulation. It can be seen that the convergence process of the entire tracking process is that the cluster is far away from the target trajectory at the beginning, then approaches the target and continues to accurately track the target. Figure 7 , the figure shows the motion trajectories of all agents during the simulation process.

[0142] By adopting the technical solution of the present application, the local interaction forces (cohesion and damping force) between intelligent agents and the global navigation force of the cluster as a whole are comprehensively considered, and the movement behaviors of the intelligent agents are coordinated from multiple levels. Cohesion ensures that the intelligent agents avoid collisions and maintain a certain formation, and the damping force makes the speeds of the intelligent agents gradually tend to be consistent, thereby enhancing the overall coordination of the cluster. The introduction of the navigation force based on the virtual center enables the cluster to better follow the target trajectory and avoid the deviation of individual behavior from the global goal. In this way, a balance is achieved between local stability and global accuracy, thereby improving the accuracy of trajectory tracking control of the multi-agent cluster.

[0143] This embodiment also discloses a multi-agent cluster trajectory tracking control device, referring to Figure 8 The device includes an acquisition module 801, a processing module 802 and a judgment module 803, wherein:

[0144] The acquisition module 801 is used to obtain the positions and speeds of multiple agents included in the multi-agent cluster.

[0145] The processing module 802 is used to determine the cohesion and damping force of the intelligent agents based on the distance between the intelligent agents, wherein the cohesion is used to avoid collisions between the intelligent agents, and the damping force is used to control the movement consistency of multiple intelligent agents.

[0146] The processing module 802 is used to determine the virtual center of the multi-agent cluster based on the position and speed of the agent.

[0147] The processing module 802 is used to determine the navigation force of the multi-agent cluster based on the center speed and center position of the virtual center, and the navigation force is used to control the movement trajectory of the multi-agent cluster.

[0148] The judgment module 803 is used to determine the trajectory tracking control input of the intelligent body according to the cohesion force, the damping force and the navigation force.

[0149] In a possible implementation, the determination module 803 is used to determine neighboring agents of any agent according to the positions of the agents, wherein the neighboring agents are agents within a preset range of any agent.

[0150] The processing module 802 is used to determine the damping force of any agent based on the speed of the neighboring agents. The damping force of any agent is specifically expressed as follows:

[0151]

[0152] in, is the damping force of any agent, N i is a set of multiple neighboring agents, a ij is the interaction strength coefficient, p j is the velocity of the neighboring agent, p i is the speed of any agent.

[0153] The processing module 802 is used to determine the interaction strength coefficient based on the positions of the neighboring agents and the position of any agent, which is specifically calculated by the following formula:

[0154]

[0155] Among them, a ij is the interaction strength coefficient, ‖q j -q i ‖ σ It means (q j -q i )’s σ norm, q i is the position of any agent, q j is the position of the jth neighboring agent, ρ h (z) is the window function, and the σ norm is defined as follows:

[0156]

[0157] Among them, the constant ε>0, ‖x‖ σ represents the σ norm of vector x, ‖x‖ represents the length of the vector, and x is a vector of any dimension.

[0158] In a possible implementation, the processing module 802 is used to determine the cohesion of any agent based on the position of any agent and the positions of neighboring agents, wherein the neighboring agents are agents within a preset range of any agent, and the cohesion of any agent is specifically expressed as follows:

[0159]

[0160] in, is the cohesion of any agent, N i is a set of multiple neighboring agents, q i is the position of any agent, q j is the position of the jth neighboring agent, Φα is the distance function with distance z as the independent variable, n ij is the position vector of any agent pointing to the position of the jth neighboring agent, which is calculated by the following formula:

[0161]

[0162] Among them, q i is the position of any agent, q j is the position of the jth neighboring agent.

[0163] In a possible implementation, the processing module 802 is used to determine a distance function based on the distance between any agent and its neighboring agents, which is specifically represented by the following formula:

[0164]

[0165] Among them, Φ α (z) is the distance function with distance z as the independent variable, q i is the position of any agent, q j is the position of the jth neighboring agent, r a is the normalized scale, d a is a constant offset.

[0166] In a possible implementation, the acquisition module 801 is used to acquire a target speed and a target position of a preset target point, where the target point is any point on a preset motion trajectory of the multi-agent cluster.

[0167] The processing module 802 is used to determine the navigation force based on the target speed, the target position, the center speed and the center position, which is specifically expressed by the following formula:

[0168]

[0169] in, is the navigation capability of the multi-agent cluster, q i is the position of any agent, p i is the speed of any agent, the neighboring agents are the agents within the preset range of any agent, e 1 、e 2 、e 3 and e 4 is the control parameter, q N+1 is the center position, p N+1 is the center velocity, q A is the target position, p A is the target speed.

[0170] In a possible implementation, the processing module 802 is used to determine the trajectory tracking control input of the multi-agent cluster according to the cohesion, the damping force, and the navigation force. The trajectory tracking control input of any agent is specifically expressed by the following formula:

[0171]

[0172] Among them, ui is the trajectory tracking control input of any agent, c 1 、c 2 and c 3 is the proportionality coefficient, f i g is the cohesion of any agent, is the damping force of any agent, f i n Navigation capability of multi-agent swarms.

[0173] In a possible implementation, the processing module 802 is used to determine the center position according to the positions of each agent, specifically by calculating the following formula:

[0174]

[0175] Among them, q N+1 is the center position, N is the number of agents, q i is the position of the ith agent.

[0176] The processing module 802 is used to determine the center speed according to the speed of each agent, which is specifically calculated by the following formula:

[0177]

[0178] Among them, p N+1 is the center speed, N is the number of agents, p i is the speed of the ith agent.

[0179] The judgment module 803 is used to set the point corresponding to the center position as the virtual center, and set the center speed as the speed of the virtual center.

[0180] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0181] This embodiment also discloses an electronic device, referring to Fig. 9 The electronic device may include: at least one processor 901 , at least one communication bus 902 , a user interface 903 , a network interface 904 , and at least one memory 905 .

[0182] The communication bus 902 is used to realize the connection and communication between these components.

[0183] The user interface 903 may include a display screen (Display) and a camera (Camera), and the optional user interface 903 may also include a standard wired interface and a wireless interface.

[0184] The network interface 904 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0185] Among them, the processor 901 may include one or more processing cores. The processor 901 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 905, and calling data stored in the memory 905. Optionally, the processor 901 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 901 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 901, and it can be implemented by a single chip.

[0186] Among them, the memory 905 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 905 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 905 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 905 may also be at least one storage device located away from the aforementioned processor 901. The memory 905 as a computer storage medium may include an operating system, a network communication module, a user interface 903 module, and an application program for a multi-agent cluster trajectory tracking control method.

[0187] exist Fig. 9In the electronic device shown, the user interface 903 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 901 can be used to call an application program for a multi-agent cluster trajectory tracking control method stored in the memory 905. When executed by one or more processors 901, the electronic device executes one or more methods in the above-mentioned embodiments.

[0188] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0189] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0190] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0191] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0192] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0193] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 905 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 905 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0194] The present application also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores instructions. When executed by one or more processors 901, the electronic device executes one or more of the methods described in the above embodiments.

[0195] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A multi-agent cluster trajectory tracking control method, characterized in that: The method comprises: Get the positions and velocities of multiple agents in a multi-agent cluster; Determine the cohesion and damping force of the intelligent bodies based on the distance between the intelligent bodies, wherein the cohesion is used to avoid collision between the intelligent bodies, and the damping force is used to control the movement consistency of the plurality of intelligent bodies; Determining a virtual center of the multi-agent cluster based on the position and velocity of the agent; Determine the navigation force of the multi-agent cluster based on the center speed and center position of the virtual center, wherein the navigation force is used to control the motion trajectory of the multi-agent cluster; The trajectory tracking control input of the intelligent body is determined according to the cohesive force, the damping force and the navigation force.

2. A multi-agent cluster trajectory tracking control method according to claim 1, characterized in that: The determining of the cohesion and damping force of the intelligent bodies based on the distance between the intelligent bodies specifically includes: Determine the neighboring intelligent entities of any intelligent entity according to the positions of each intelligent entity, wherein the neighboring intelligent entities are intelligent entities within a preset range of any intelligent entity; Based on the speed of the neighboring agents, the damping force of any one of the agents is determined, and the damping force of any one of the agents is specifically expressed as follows: Among them, f i d is the damping force of any agent, N i is a set of multiple neighboring agents, a ij is the interaction strength coefficient, p j is the velocity of the neighboring agent, p i is the speed of any of the agents; Based on the position of the neighboring agent and the position of any one of the agents, the interaction strength coefficient is determined, which is specifically calculated by the following formula: Among them, a ij is the interaction strength coefficient, ‖q j -q i ‖ σ It means (q j -q i )’s σ norm, q i is the position of any agent, q j is the position of the jth neighboring agent, ρ h (z) is the window function, and the σ norm is defined as follows: Among them, the constant ε>0, ‖x‖ σ represents the σ norm of vector x, ‖x‖ represents the length of the vector, and x is a vector of any dimension.

3. A multi-agent cluster trajectory tracking control method according to claim 1, characterized in that: The determining of the cohesion and damping force of the intelligent bodies based on the distance between the intelligent bodies specifically includes: Based on the position of any agent and the position of the neighboring agents, the cohesion of any agent is determined, wherein the neighboring agents are agents within a preset range of the any agent, and the cohesion of any agent is specifically expressed as follows: Among them, f i g is the cohesion of any agent, N i is a set of multiple neighboring agents, q i is the position of any agent, q j is the position of the jth neighboring agent, Φα is the distance function with distance z as the independent variable, n ij is the position vector of the position of any agent pointing to the position of the jth neighboring agent, which is calculated by the following formula: Among them, q i is the position of any agent, q j is the position of the jth neighboring agent.

4. A multi-agent cluster trajectory tracking control method according to claim 3, characterized in that: The distance function is determined based on the distance between any one of the agents and the neighboring agents, and is specifically expressed by the following formula: Among them, Φ α (z) is the distance function with distance z as the independent variable, q i is the position of any agent, q j is the position of the jth neighboring agent, r a is the normalized scale, d a is a constant offset.

5. A multi-agent cluster trajectory tracking control method according to claim 1, characterized in that: Determining the navigation capability of the multi-agent cluster based on the center speed and center position of the virtual center specifically includes: Obtaining a target speed and a target position of a preset target point, wherein the target point is any point on a preset motion trajectory of the multi-agent cluster; The navigation force is determined based on the target speed, the target position, the center speed, and the center position, which is specifically expressed by the following formula: f i n =-e1(q i -q A )-e2(p i -p A )-e3(q N+1 -q A )-e4(p N+1 -p A ) Among them, f i n is the navigation capability of the multi-agent cluster, q i is the position of any agent, p i is the speed of any one of the agents, the neighboring agents are agents within the preset range of any one of the agents, e1, e2, e3 and e4 are control parameters, q N+1 is the center position, p N+1 is the central velocity, q A is the target position, p A is the target speed.

6. A multi-agent cluster trajectory tracking control method according to claim 1, characterized in that: The trajectory tracking control input of the multi-agent cluster is determined according to the cohesive force, the damping force and the navigation force. The trajectory tracking control input of any agent is specifically expressed by the following formula: u i =c1f i g +c2f i d +c3f i n Where ui is the trajectory tracking control input of any of the agents, c1, c2 and c3 are proportional coefficients, and f i g is the cohesion of any agent, f i d is the damping force of any agent, f i n is the navigation capability of the multi-agent cluster.

7. A multi-agent cluster trajectory tracking control method according to claim 1, characterized in that: Based on the position and speed of the agent, determining the virtual center of the multi-agent cluster specifically includes: According to the positions of each of the intelligent agents, the center position is determined, which is specifically calculated by the following formula: Among them, q N+1 is the central position, N is the number of the multiple agents, q i is the position of the ith agent; The center speed is determined according to the speed of each of the intelligent agents, and is specifically calculated by the following formula: Among them, p N+1 is the center speed, N is the number of the multiple agents, p i is the speed of the ith agent; The point corresponding to the center position is set as the virtual center, and the center speed is set as the speed of the virtual center.

8. A multi-agent cluster trajectory tracking control device, characterized in that: The device comprises an acquisition module (801), a processing module (802) and a judgment module (803), wherein: The acquisition module (801) is used to acquire the positions and speeds of multiple agents in a multi-agent cluster; The processing module (802) is used to determine the cohesion and damping force of the intelligent bodies based on the distance between the intelligent bodies, wherein the cohesion is used to avoid collision between the intelligent bodies, and the damping force is used to control the movement consistency of multiple intelligent bodies; The processing module (802) is used to determine the virtual center of the multi-agent cluster based on the position and speed of the agent; The processing module (802) is used to determine the navigation force of the multi-agent cluster based on the center speed and center position of the virtual center, and the navigation force is used to control the movement trajectory of the multi-agent cluster; The judgment module (803) is used to determine the trajectory tracking control input of the intelligent body according to the cohesive force, the damping force and the navigation force.

9. An electronic device, characterized in that: The electronic device comprises a processor (901), a communication bus (902), a memory (905), a user interface (903), a network interface (904) and a memory (905), wherein the memory (905) is used to store instructions, the user interface (903) and the network interface (904) are both used to communicate with other devices, and the processor (901) is used to execute the instructions stored in the memory (905) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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