Method and device for autonomous tracking of multiple dynamic targets by a drone swarm

Abstract

The application relates to a method and device for autonomous tracking of multiple dynamic targets by a UAV cluster. The method comprises: acquiring cluster information, environment information and behavior data information of the UAV cluster, constructing a scene model and a cluster behavior model of the UAV cluster, and generating path planning information of the cluster behavior model through a cluster path planning strategy based on the scene model; identifying distance information between the UAV cluster and each dynamic target cluster through the scene model based on the path planning information, and generating a dynamic tracking control strategy for the UAV cluster through a multi-dynamic-target tracking algorithm based on the scene model when the distance information meets a preset distance condition; and controlling the UAV cluster to track each dynamic target cluster based on the dynamic tracking control strategy, thereby completing the autonomous tracking of multiple dynamic targets by the UAV cluster. The method can improve the efficiency of tracking multiple dynamic targets.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method and device for autonomously tracking multiple dynamic targets in a drone cluster. Background Art

[0002] With the development of intelligent technology, autonomous unmanned swarm systems are gaining increasing attention. Unmanned aerial vehicles (UAVs) are widely used due to their low cost, high maneuverability, and environmental adaptability. The task of tracking multiple dynamic targets in a UAV swarm is characterized by high complexity and nonlinearity. Traditional path planning methods, such as the Artificial Potential Field (APF) method, can calculate a preliminary path, but struggle to achieve high success rates in real-time state assessment and motion decisions, and are susceptible to interference from modeling accuracy and environmental noise. Therefore, improving the accuracy of multiple dynamic target tracking in UAV swarms is a current research priority.

[0003] Existing research typically uses end-to-end reinforcement learning methods for tracking multiple dynamic targets in UAV swarms. Due to the large reachable map and the high maneuverability of UAVs, end-to-end MARL has a very large state space. This makes it difficult to train the algorithm to obtain a usable policy, and the reward function has difficulty converging. It is difficult to generate a tracking policy that meets the success rate target within a limited timeframe. This results in low efficiency for tracking multiple dynamic targets. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for autonomous tracking of multiple dynamic targets in a drone cluster to address the above technical problems.

[0005] In a first aspect, the present application provides a method for autonomously tracking multiple dynamic targets in a drone swarm, comprising:

[0006] Acquire cluster information of a drone cluster, environmental information of the drone cluster, and behavioral data information of the drone cluster, and construct a scenario model of the drone cluster based on the cluster information and the environmental information;

[0007] Based on the behavioral data information of the drone cluster, a cluster behavior model of the drone cluster is constructed, and based on the scenario model, path planning information of the cluster behavior model is generated through a cluster path planning strategy;

[0008] Based on the path planning information, the distance information between the drone cluster and each dynamic target cluster is identified through the scenario model, and when the distance information meets a preset distance condition, a dynamic tracking control strategy for the drone cluster is generated based on the scenario model and through a multi-dynamic target tracking algorithm;

[0009] Based on the dynamic tracking control strategy, the drone cluster is controlled to track each of the dynamic target clusters to complete the multi-dynamic target autonomous tracking task of the drone cluster.

[0010] Optionally, constructing the scene model of the drone cluster based on the cluster information and the environmental information includes:

[0011] Based on the environmental information, identifying information about each restriction in the environment in which the drone cluster is located, and initial target position information of a dynamic target cluster in the environment in which the drone cluster is located, and based on the cluster information, identifying initial drone position information of each drone in the drone cluster, and cluster target position information of cluster targets of the dynamic target cluster;

[0012] Based on the information of each of the restricting objects, a scene plane model of the drone cluster is constructed, and based on the initial target position information of the dynamic target cluster, the initial drone position information of each of the drones, and the cluster target position information of the cluster targets of the dynamic target cluster, drone position parameters of the drone cluster, target position parameters of the dynamic target cluster, and cluster target position parameters of the cluster targets are identified in the scene plane model;

[0013] The drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster target are added to the scene plane model to obtain the scene model of the drone cluster.

[0014] Optionally, constructing a cluster behavior model of the drone cluster based on the behavior data information of the drone cluster includes:

[0015] For each drone in the drone cluster, identifying the speed information and the location information of the drone based on the behavior data information of the drone;

[0016] Based on the speed information of the UAV, a motion model of the UAV is constructed by a motion modeling strategy, and based on the position information of the UAV, position state progressive information of the UAV is generated by a position state algorithm;

[0017] The motion model of the drone and the progressive information of the position state of the drone are used as the drone behavior model of the drone, and all the drone behavior models are used as the cluster behavior model of the drone cluster.

[0018] Optionally, generating path planning information of the cluster behavior model based on the scenario model through a cluster path planning strategy includes:

[0019] Identifying drone position distribution information of the drone cluster in the scene model, target position distribution information of the dynamic target cluster in the scene model, and restricted position information of each restricted object in the scene model;

[0020] Based on the UAV position distribution information, the target position distribution information, and each of the restricted position information, a cluster path planning strategy is used to generate path planning information of the cluster behavior model of each UAV.

[0021] Optionally, identifying distance information between the drone cluster and each dynamic target cluster through the scene model based on the path planning information includes:

[0022] Based on the path planning information, the target tracking process of the cluster behavior model is simulated through the scenario model to obtain new drone position distribution information of the drone cluster and new target position distribution information of the dynamic target cluster;

[0023] Based on the new target position distribution information and the new drone position distribution information, the target center position information of the dynamic target cluster and the drone center position information of the drone cluster are identified through the center position division algorithm, and based on the target center position information and the drone center position information, the distance information between the drone cluster and each dynamic target cluster is calculated.

[0024] Optionally, when the distance information meets a preset distance condition, generating a dynamic tracking control strategy for the drone cluster based on the scene model through a multi-dynamic target tracking algorithm includes:

[0025] When the distance information is lower than a preset distance threshold, determining that the distance information satisfies a preset distance condition, and updating the scene model based on the new drone position distribution information of each drone and the new target position distribution information of the dynamic target cluster to obtain a new scene model;

[0026] Based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target, a new tracking route for each drone in the drone cluster is generated by a multi-dynamic target tracking algorithm;

[0027] Based on the new tracking routes of the drones, the new scene model is updated to obtain the current drone position distribution information of the drone cluster and the current target position distribution information of the dynamic target cluster. When there is no current drone position information that overlaps with the current target position distribution information of the dynamic target cluster, the new drone position distribution information of the drone cluster is replaced by the current drone position distribution information of the drone cluster, and the current target position distribution information of the dynamic target cluster is replaced by the current target position distribution information of the dynamic target cluster.

[0028] Return to the step of generating a new tracking route for each drone in the drone cluster based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target through a multi-dynamic target tracking algorithm, until the current position information of the drone exists and overlaps with the current target position distribution information of the dynamic target cluster. Then, the new tracking route obtained in each iteration is summarized in the generation time order of each new tracking route to obtain the dynamic tracking control strategy of the drone cluster.

[0029] In a second aspect, the present application also provides a drone swarm multi-dynamic target autonomous tracking device, comprising:

[0030] An acquisition module is used to obtain cluster information of a drone cluster, environmental information of the drone cluster, and behavioral data information of the drone cluster, and to construct a scene model of the drone cluster based on the cluster information and the environmental information;

[0031] A generation module, configured to construct a cluster behavior model of the drone cluster based on the behavior data information of the drone cluster, and generate path planning information of the cluster behavior model through a cluster path planning strategy based on the scenario model;

[0032] an identification module for identifying distance information between the UAV cluster and each dynamic target cluster based on the path planning information and the scenario model, and generating a dynamic tracking control strategy for the UAV cluster based on the scenario model and a multi-dynamic target tracking algorithm when the distance information meets a preset distance condition;

[0033] The control module is used to control the UAV cluster based on the dynamic tracking control strategy, track each dynamic target cluster, and complete the multi-dynamic target autonomous tracking task of the UAV cluster.

[0034] Optionally, the acquisition module is specifically configured to:

[0035] Based on the environmental information, identifying information about each restriction in the environment in which the drone cluster is located, and initial target position information of a dynamic target cluster in the environment in which the drone cluster is located, and based on the cluster information, identifying initial drone position information of each drone in the drone cluster, and cluster target position information of cluster targets of the dynamic target cluster;

[0036] Based on the information of each of the restricting objects, a scene plane model of the drone cluster is constructed, and based on the initial target position information of the dynamic target cluster, the initial drone position information of each of the drones, and the cluster target position information of the cluster targets of the dynamic target cluster, drone position parameters of the drone cluster, target position parameters of the dynamic target cluster, and cluster target position parameters of the cluster targets are identified in the scene plane model;

[0037] The drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster target are added to the scene plane model to obtain the scene model of the drone cluster.

[0038] Optionally, the generating module is specifically configured to:

[0039] For each drone in the drone cluster, identifying the speed information and the location information of the drone based on the behavior data information of the drone;

[0040] Based on the speed information of the UAV, a motion model of the UAV is constructed by a motion modeling strategy, and based on the position information of the UAV, position state progressive information of the UAV is generated by a position state algorithm;

[0041] The motion model of the drone and the progressive information of the position state of the drone are used as the drone behavior model of the drone, and all the drone behavior models are used as the cluster behavior model of the drone cluster.

[0042] Optionally, the generating module is specifically configured to:

[0043] Identifying drone position distribution information of the drone cluster in the scene model, target position distribution information of the dynamic target cluster in the scene model, and restricted position information of each restricted object in the scene model;

[0044] Based on the UAV position distribution information, the target position distribution information, and each of the restricted position information, a cluster path planning strategy is used to generate path planning information of the cluster behavior model of each UAV.

[0045] Optionally, the identification module is specifically configured to:

[0046] Based on the path planning information, the target tracking process of the cluster behavior model is simulated through the scenario model to obtain new drone position distribution information of the drone cluster and new target position distribution information of the dynamic target cluster;

[0047] Based on the new target position distribution information and the new drone position distribution information, the target center position information of the dynamic target cluster and the drone center position information of the drone cluster are identified through the center position division algorithm, and based on the target center position information and the drone center position information, the distance information between the drone cluster and each dynamic target cluster is calculated.

[0048] Optionally, the identification module is specifically configured to:

[0049] When the distance information is lower than a preset distance threshold, determining that the distance information satisfies a preset distance condition, and updating the scene model based on the new drone position distribution information of each drone and the new target position distribution information of the dynamic target cluster to obtain a new scene model;

[0050] Based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target, a new tracking route for each drone in the drone cluster is generated by a multi-dynamic target tracking algorithm;

[0051] Based on the new tracking routes of the drones, the new scene model is updated to obtain the current drone position distribution information of the drone cluster and the current target position distribution information of the dynamic target cluster. When there is no current drone position information that overlaps with the current target position distribution information of the dynamic target cluster, the new drone position distribution information of the drone cluster is replaced by the current drone position distribution information of the drone cluster, and the current target position distribution information of the dynamic target cluster is replaced by the current target position distribution information of the dynamic target cluster.

[0052] Return to the step of generating a new tracking route for each drone in the drone cluster based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target through a multi-dynamic target tracking algorithm, until the current position information of the drone exists and overlaps with the current target position distribution information of the dynamic target cluster. Then, the new tracking route obtained in each iteration is summarized in the generation time order of each new tracking route to obtain the dynamic tracking control strategy of the drone cluster.

[0053] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0055] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0056] The above-mentioned method and device for autonomous tracking of multiple dynamic targets in a drone cluster obtains cluster information of the drone cluster, environmental information of the drone cluster, and behavioral data information of the drone cluster, and constructs a scene model of the drone cluster based on the cluster information and the environmental information; constructs a cluster behavior model of the drone cluster based on the behavioral data information of the drone cluster, and generates path planning information of the cluster behavior model based on the scene model through a cluster path planning strategy; identifies the distance information between the drone cluster and each dynamic target cluster based on the path planning information through the scene model, and generates a dynamic tracking control strategy for the drone cluster based on a multi-dynamic target tracking algorithm based on the scene model when the distance information meets a preset distance condition; controls the drone cluster based on the dynamic tracking control strategy, tracks each of the dynamic target clusters, and completes the autonomous tracking task of multiple dynamic targets of the drone cluster. This solution first generates path planning information for a swarm behavior model using a swarm path planning strategy. It then applies a multi-dynamic target tracking algorithm to generate a dynamic tracking control strategy for the swarm. In the initial phase, the drones autonomously explore the environment. Due to their poor strategies, the environmental reward feedback they receive is sparse. Using reinforcement learning, training a viable strategy would be time-consuming. To avoid meaningless exploration in this initial phase, this solution utilizes a swarm path planning strategy in the initial phase, enabling the swarm to rapidly approach the moving target cluster while avoiding obstacles. Then, when each drone rapidly approaches the target cluster, reaching a certain distance threshold, the task becomes less challenging. At this point, the multi-dynamic target tracking algorithm is employed to fully explore the space of feasible strategies, enabling each drone to accurately track the target cluster. This significantly reduces the drone tracking time during the initial exploration phase and allows the drones to accurately track the target cluster during the later target tracking phase. This ensures accurate tracking of the target cluster while significantly reducing the initial exploration time. This ensures that the success rate is achieved within a limited timeframe, comprehensively improving the efficiency of multi-dynamic target tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 Schematic diagram of a flow chart of a method for autonomously tracking multiple dynamic targets in a drone swarm according to one embodiment;

[0059] Figure 2 is a visual schematic diagram of a scene model in one embodiment;

[0060] Figure 3 A schematic diagram of a search for a drone swarm in the first phase of an embodiment;

[0061] Figure 4 A convergence comparison diagram between the tracking method of this solution and the traditional tracking method in one embodiment;

[0062] Figure 5 A flowchart of autonomous tracking of multiple dynamic targets in a drone swarm according to an embodiment;

[0063] Figure 6 A flowchart of an example of autonomous tracking of multiple dynamic targets in a drone swarm according to an embodiment;

[0064] Figure 7 This is a structural block diagram of a device for autonomously tracking multiple dynamic targets in a drone swarm according to one embodiment;

[0065] Figure 8 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0067] The method for autonomous tracking of multiple dynamic targets in a swarm of drones provided in embodiments of the present application can be applied in an application environment involving autonomous tracking of multiple dynamic targets in a swarm of drones. The method can be applied to a terminal, a server, or a system comprising a terminal and a server, and implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, mid-range computers, and the like. The terminal first generates path planning information for a swarm behavior model according to a swarm path planning strategy, and then generates a dynamic tracking control strategy for a swarm of unmanned aerial vehicles (UAVs) using a multi-dynamic target tracking algorithm. In the initial phase, the drones autonomously explore the environment. Because the strategies at this stage are poor, the resulting environmental reward feedback is very sparse. If reinforcement learning methods are used, the training process from the initial stages to obtaining a usable strategy would be very time-consuming. To avoid meaningless exploration in the initial phase, the present solution utilizes a swarm path planning strategy in the initial phase, enabling the drone swarm to rapidly approach a swarm of moving targets while avoiding obstacles. Then, when each drone rapidly approaches the target swarm and reaches a certain distance threshold, the task difficulty decreases. At this point, the multi-dynamic target tracking algorithm is employed to fully explore the feasible strategy space, enabling each drone to accurately and ultimately reach the target swarm. This greatly reduces the tracking time of the drone in the early exploration stage, and enables the accurate tracking of the target cluster in the later target tracking stage. While ensuring the tracking accuracy of the target cluster, it greatly reduces the early exploration time, thereby ensuring that the success rate index is achieved within a limited time, and comprehensively improving the efficiency of multi-dynamic target tracking.

[0068] In an exemplary embodiment, Figure 1 As shown, a method for autonomous tracking of multiple dynamic targets in a drone swarm is provided, which is described by taking the method applied to a terminal as an example, and includes the following steps S101 to S104. Among them:

[0069] Step S101: obtain cluster information of the drone cluster, environmental information of the drone cluster, and behavioral data information of the drone cluster, and construct a scene model of the drone cluster based on the cluster information and environmental information.

[0070] In this embodiment, in response to a worker's information upload operation, the terminal obtains cluster information of the drone swarm, information about the environment in which the drone swarm is located, and behavioral data about the drone swarm. The cluster information includes the location distribution of each drone in the swarm, while the environmental information includes the location distribution of the target cluster, the location of the target point to which the target cluster needs to travel (i.e., the cluster target corresponding to the target cluster), and the location information of each obstacle in the environment. This location information includes the flight boundaries of the drones and each obstacle in the environment. The behavioral data of the drone swarm includes the speed information (angular velocity, linear velocity) and the orientation information of the drones. The terminal then constructs a scenario model of the drone swarm based on the cluster information and environmental information. The specific construction process will be described in detail later.

[0071] Step S102: constructing a cluster behavior model of the drone cluster based on the behavior data information of the drone cluster, and generating path planning information of the cluster behavior model through a cluster path planning strategy based on the scenario model.

[0072] In this embodiment, the terminal constructs a cluster behavior model for the drone cluster based on the behavioral data of the drone cluster. Based on the scenario model, the terminal generates path planning information for the cluster behavior model using a cluster path planning strategy. The cluster behavior model includes a behavior model for each drone, including its motion model and its position state progression information. The position state progression information serves as a prediction equation for the next moment in time. The path planning information for the cluster behavior model is the exploration path planning information used by each drone during the first phase of exploration to find the target cluster. The cluster path planning strategy, based on artificial potential field (APF) path planning, can calculate a preliminary path, but it struggles to achieve a high success rate in real-time state assessment and motion decisions. The specific generation process will be described in detail later.

[0073] In step S103, based on the path planning information, the distance information between the drone cluster and each dynamic target cluster is identified through the scene model. When the distance information meets the preset distance condition, a dynamic tracking control strategy for the drone cluster is generated based on the scene model through the multi-dynamic target tracking algorithm.

[0074] In this embodiment, based on path planning information and a scenario model, the terminal identifies the distance information between the drone cluster and each dynamic target cluster. When the distance information meets a preset distance condition, the terminal generates a dynamic tracking control strategy for the drone cluster using a multi-dynamic target tracking algorithm based on the scenario model. The distance information refers to the distance between the center position of the drone cluster and the center position of the dynamic target cluster, and the preset distance condition is that the distance information is below a distance threshold preset in the terminal. The specific process for determining whether the distance information meets the preset distance condition will be described in detail later. This dynamic target tracking algorithm uses a multi-agent reinforcement learning (MARL) method combined with a neural network structure. It has certain feasibility for generating nonlinear, large-scale collaborative strategies for multiple unmanned platforms and has certain generalization capabilities under different random initializations. The dynamic tracking control strategy includes the dynamic tracking control strategy of the drone cluster during each iterative tracking process.

[0075] In step S104, based on the dynamic tracking control strategy, the drone cluster is controlled to track each dynamic target cluster to complete the multi-dynamic target autonomous tracking task of the drone cluster.

[0076] In this embodiment, the terminal uses a dynamic tracking control strategy to guide each drone in the drone cluster to iteratively track the dynamic target cluster until the dynamic target cluster is tracked, and determines that the multi-dynamic target autonomous tracking task of the drone cluster is completed.

[0077] Based on the above scheme, path planning information for a swarm behavior model is first generated using a swarm path planning strategy. A dynamic tracking control strategy for a swarm of unmanned aerial vehicles (UAVs) is then generated using a multi-dynamic target tracking algorithm. Initially, the UAVs autonomously explore the environment. Due to their poor strategies, the resulting environmental reward feedback is sparse. Using reinforcement learning methods, training a viable strategy would be time-consuming. To avoid meaningless exploration in this initial phase, this scheme utilizes a swarm path planning strategy in the initial phase, enabling the swarm to rapidly approach a moving target cluster while avoiding obstacles. Then, when each UAV rapidly approaches the target cluster, reaching a certain distance threshold, the task becomes less challenging. At this point, the multi-dynamic target tracking algorithm is employed to fully explore the space of feasible strategies, enabling each UAV to accurately track the target cluster. This significantly reduces the tracking time of the UAVs in the initial exploration phase and allows them to accurately track the target cluster in the later target tracking phase. This ensures accurate tracking of the target cluster while significantly reducing the initial exploration time. This ensures that the success rate is achieved within a limited timeframe, comprehensively improving the efficiency of multi-dynamic target tracking.

[0078] Optionally, based on cluster information and environmental information, a scene model of a drone cluster is constructed, including: based on the environmental information, identifying the information of each restriction in the environment in which the drone cluster is located, and the initial target position information of the dynamic target cluster in the environment in which the drone cluster is located, and based on the cluster information, identifying the initial drone position information of each drone in the drone cluster, and the cluster target position information of the cluster targets of the dynamic target cluster; based on the information of each restriction, a scene plane model of the drone cluster is constructed, and based on the initial target position information of the dynamic target cluster, the initial drone position information of each drone, and the cluster target position information of the cluster targets of the dynamic target cluster, in the scene plane model, identifying the drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster targets; adding the drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster targets to the scene plane model to obtain the scene model of the drone cluster.

[0079] In this embodiment, the terminal identifies information about various restrictive objects in the drone cluster's environment based on environmental information, as well as initial target position information for the dynamic target cluster in the drone cluster's environment. Furthermore, based on cluster information, the terminal identifies initial drone position information for each drone in the drone cluster and cluster target position information for the dynamic target cluster. The restrictive object information includes drone boundary information and obstacle position information for each obstacle.

[0080] The terminal then constructs a scene plane model of the drone cluster based on the information about each restriction. Based on the initial target position information of the dynamic target cluster, the initial drone position information of each drone, and the cluster target position information of the cluster targets of the dynamic target cluster, the terminal identifies the drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster targets in the scene plane model. The scene plane model is a two-dimensional plane model, and each position parameter is two-dimensional position information within the two-dimensional plane model.

[0081] Finally, the terminal adds the drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster target to the scene plane model to obtain the scene model of the drone cluster.

[0082] Based on the above scheme, by first modeling the scene plane, and then adding the drone cluster, dynamic target cluster, and cluster target to the scene plane model, the scene model of the drone cluster is obtained, which improves the applicability and accuracy of the scene model construction.

[0083] Optionally, based on the behavioral data information of the drone cluster, a cluster behavior model of the drone cluster is constructed, including: for each drone in the drone cluster, based on the drone's behavioral data information, identifying the drone's speed information and the drone's position information; based on the drone's speed information, constructing the drone's motion model through a motion model modeling strategy, and based on the drone's position information, generating the drone's position state progressive information through a position state algorithm; using the drone's motion model and the drone's position state progressive information as the drone behavior model of the drone, and using all drone behavior models as the cluster behavior model of the drone cluster.

[0084] In this embodiment, the terminal identifies the speed and location of each drone in the drone cluster based on its behavioral data. The terminal then constructs a motion model based on the speed information using a motion modeling strategy. Based on the drone's location information, the terminal generates progressive position state information using a position state algorithm.

[0085] Specifically, a single UAV The motion model is shown below.

[0086]

[0087] in and UAV along The speed in the axis direction, is the angular velocity of the UAV at this moment, For its The angle in the positive direction of the axis, is the linear velocity, is the angular velocity.

[0088] The position state equation of the UAV at the next moment (i.e., the position state progressive information) is as follows:

[0089]

[0090] Finally, the terminal uses the motion model of the drone and the progressive information of the drone's position status as the drone behavior model of the drone, and uses all the drone behavior models as the cluster behavior model of the drone cluster.

[0091] Based on the above solution, by individually modeling each drone, we ensure that the route of each drone can be accurately controlled when actually planning and tracking the route, thereby improving the accuracy of drone route planning.

[0092] Optionally, based on the scene model, path planning information of the cluster behavior model is generated through a cluster path planning strategy, including: identifying the drone position distribution information of the drone cluster in the scene model, the target position distribution information of the dynamic target cluster in the scene model, and the restricted position information of each restriction in the scene model; based on the drone position distribution information, the target position distribution information, and the restricted position information, the path planning information of the cluster behavior model of each drone is generated through a cluster path planning strategy.

[0093] In this embodiment, the terminal identifies the drone position distribution information of the drone cluster within the scene model, the target position distribution information of the dynamic target cluster within the scene model, and the restricted position information of each restriction object within the scene model. Then, based on this drone position distribution information, target position distribution information, and each restricted position information, the terminal uses a cluster path planning strategy to generate path planning information for each drone's cluster behavior model.

[0094] Specifically, the terminal imitates the concept of electric potential and electric potential field in the electrostatic field, and constructs gravitational fields at the target point and the obstacle position respectively. and repulsive field ,in,

[0095] The drone will be attracted to move towards the target point. Helps drones avoid obstacles. In the potential field, the drone is treated as a point mass. Under the action of the net force at its current position, it moves a specified unit step. After reaching the next position, the potential field is updated in real time to carry out the next path planning. One of the significant advantages of APF is its strong real-time performance, which can better cope with target changes and dynamic obstacles in the environment. The net force on the drone in the virtual potential field obtained by the APF method is:

[0096]

[0097] in For the joint efforts, The attraction given to the drone by the target point, The repulsive force exerted by obstacles in the environment on the drone. Each drone performs path planning in the initial stage under the action of its own combined force.

[0098] Based on this approach, we directly incorporated expert experience from the APF strategy into the initial strategy design. Using the APF approach in this initial phase enabled the UAV swarm to quickly approach the moving target cluster while avoiding obstacles. This improved the search efficiency of each UAV during the first phase.

[0099] Optionally, based on the path planning information, the distance information between the drone cluster and each dynamic target cluster is identified through the scene model, including: based on the path planning information, the target tracking process of the cluster behavior model is simulated through the scene model to obtain new drone position distribution information of the drone cluster and new target position distribution information of the dynamic target cluster; based on the new target position distribution information and the new drone position distribution information, the target center position information of the dynamic target cluster and the drone center position information of the drone cluster are identified through the center position division algorithm, and based on the target center position information and the drone center position information, the distance information between the drone cluster and each dynamic target cluster is calculated.

[0100] In this embodiment, the terminal simulates the target tracking process of the cluster behavior model based on the path planning information through the scene model to obtain new drone position distribution information of the drone cluster and new target position distribution information of the dynamic target cluster.

[0101] Specifically, when , that is, the distance information between the drone cluster and the dynamic target cluster is greater than a preset distance threshold When , it is considered to be in the first stage. In this stage, the rewards are sparse and no reinforcement learning method is used. The drone uses the traditional path planning method to directly and weakly randomly approach the moving target quickly to facilitate subsequent tracking.

[0102] During this phase, the line connecting the virtual pursuer and the virtual navigator divides the cluster into two parts. The drones in these two parts no longer chase the virtual navigator of the moving target cluster, but instead pursue an equivalent target point on a circle with it as the center and radius r. The drones on either side of the circle chase the equivalent target point on their side during this phase. By "expanding" the target point left and right, an encirclement trend is formed.

[0103] The tracking strategy at this stage is as follows Figure 3 The adjustable parameters are , r.

[0104] Then, based on the new target location distribution information and the new drone location distribution information, the terminal uses the center location division algorithm to identify the target center location information of the dynamic target cluster and the drone center location information of the drone cluster. Based on the target center location information and the drone center location information, the terminal calculates the distance information between the drone cluster and each dynamic target cluster. The specific process of calculating each center location information is as follows:

[0105] The terminal defines the location of the "virtual tracker" :

[0106] (4)

[0107] in For the The proposed “virtual tracker” can characterize the center position of the drone cluster to a certain extent.

[0108] Similarly, the observed center position of the moving target cluster can be defined as , called the "virtual navigator," which represents the center position of the mobile target cluster. The relative distance between the coordinate centers of the "virtual tracker" and the "virtual navigator" clusters is calculated to obtain the distance information between the drone cluster and each dynamic target cluster.

[0109] Based on the above solution, by defining the center position of each cluster, the distance information between two clusters is calculated, thereby improving the calculation accuracy and adaptability of the distance information.

[0110] Optionally, when the distance information meets the preset distance condition, based on the scene model, a dynamic tracking control strategy for the drone cluster is generated through a multi-dynamic target tracking algorithm, including: when the distance information is lower than a preset distance threshold, determining that the distance information meets the preset distance condition, and based on the new drone position distribution information of each drone and the new target position distribution information of the dynamic target cluster, updating the scene model to obtain a new scene model; based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target, generating a new tracking route for each drone in the drone cluster through a multi-dynamic target tracking algorithm; based on the new tracking route of each drone, updating the new scene model to obtain the current drone position distribution information of the drone cluster and the current target position distribution information of the dynamic target cluster, and when there is no When the current position information of the UAV overlaps with the current target position distribution information of the dynamic target cluster, the current UAV position distribution information of the UAV cluster replaces the new UAV position distribution information of the UAV cluster, and the current target position distribution information of the dynamic target cluster replaces the current target position distribution information of the dynamic target cluster; return to execute the new UAV position distribution information of the UAV cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target, and generate a new tracking route step for each UAV in the UAV cluster through a multi-dynamic target tracking algorithm until the current position information of the UAV overlaps with the current target position distribution information of the dynamic target cluster, and the new tracking route obtained in each iteration is summarized in the generation time order of each new tracking route to obtain the dynamic tracking control strategy of the UAV cluster.

[0111] In this embodiment, when the distance information is lower than the preset distance threshold, the terminal determines that the distance information meets the preset distance condition, and updates the scene model based on the new drone position distribution information of each drone and the new target position distribution information of the dynamic target cluster to obtain a new scene model.

[0112] Then, the terminal generates a new tracking route for each drone in the drone cluster through a multi-dynamic target tracking algorithm based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target.

[0113] Afterwards, the terminal updates the new scene model based on the new tracking routes of each drone, obtains the current drone position distribution information of the drone cluster, and the current target position distribution information of the dynamic target cluster, and when there is no current position information of the drone and it overlaps with the current target position distribution information of the dynamic target cluster, the current drone position distribution information of the drone cluster is replaced by the new drone position distribution information of the drone cluster, and the current target position distribution information of the dynamic target cluster is replaced by the current target position distribution information of the dynamic target cluster.

[0114] Finally, the terminal returns to execute the new UAV position distribution information based on the UAV cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target, and generates a new tracking route step for each UAV in the UAV cluster through the multi-dynamic target tracking algorithm until the current position information of the UAV exists and overlaps with the current target position distribution information of the dynamic target cluster. The new tracking route obtained in each iteration is summarized in the generation time order of each new tracking route to obtain the dynamic tracking control strategy of the UAV cluster.

[0115] Specifically, the terminal can integrate any existing MARL algorithm through a phased framework, such as the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and MADDPG with Dual Delay (MATD3). MADDPG, based on the actor-critic framework, is a multi-drone version of the DDPG algorithm based on a "centralized training + decentralized decision-making" architecture, capable of handling continuous action spaces. MATD3 is an improved version of MADDPG, resulting in a dynamic target tracking algorithm. In actual tracking, for drone swarms, given the mission's focus on tracking all targets within the shortest possible mission time and keeping the tracked position as far away from the target point as possible, the reward function is not simply determined by whether the target is ultimately tracked successfully. Reward shaping can be used to design phased rewards during the intermediate stages, providing relatively dense reward information to accelerate training.

[0116] drones The reward function is , considering collision rewards for successful tracking, distance rewards for dynamic threats (moving targets), distance rewards for other drones and static obstacles (obstacle avoidance rewards), etc.

[0117] Tracking successful collision reward: When the drone Distance to any moving target The distance is less than the set threshold When , the tracking is considered successful, and the drone and the target exit the environment at the same time. Get collision rewards as follows:

[0118] (5)

[0119] in For a larger completion reward, is an indicator function, and its value is 1 when the condition in the independent variable is true, otherwise it is 0. This reward item encourages the drone to track any moving target.

[0120] Distance negative reward: In order to make the drone complete the tracking step as few as possible, set a distance negative reward ,as follows:

[0121] (6)

[0122] in, is the magnitude adjustment parameter for the distance reward, Indicates distance The nearest moving target, for The geometric distance to the nearest moving target, The maximum distance between two points on the map. As the distance between the drone and its nearest moving target decreases, it gradually approaches - , on the contrary, the farther the distance is (the lower the tracking success rate is) This reward essentially encourages the drone to track the nearest moving target as quickly as possible, thereby generating a better fast tracking strategy.

[0123] Obstacle avoidance reward: In order to allow the drone to complete tracking safely, set an obstacle avoidance reward ,as follows:

[0124] (7)

[0125] in:

[0126] (8)

[0127] (9)

[0128] in, 、 is the magnitude adjustment parameter for the obstacle avoidance reward, for The geometric distance to the nearest other drone. The smaller this value is, the greater the probability of collision with its companions. The bigger; for The geometric distance to the nearest environmental obstacle. The smaller this value is, the greater the probability of collision with the obstacle. The larger the value, the greater the reward. This reward essentially encourages drones to stay away from other drones and obstacles to ensure the safety of the tracking process.

[0129] Based on the above discussion, the total reward function for the task is designed as follows. The optimization goal of MARL is to maximize the cumulative reinforcement learning reward function.

[0130] (10)

[0131] In this stage, by using MARL for a sufficient number of rounds of training, a policy neural network suitable for the UAV cluster multi-dynamic target tracking task can be obtained from the simplified task formed in the first stage. In the reasoning stage of the actual task execution, it is only necessary to input the real-time observations of each UAV into the trained policy network to execute the feasible policy obtained through MARL training.

[0132] like Figure 4 The following is the overall implementation process of this program.

[0133] In specific implementation, Figure 5 The figure shows a curve comparing the number of training rounds and the reward obtained from the environment during each round, with an upper limit of approximately 60. The baseline algorithm is MATD3. The phased algorithm uses APF in the first phase and MATD3 in the second phase, with a boundary parameter of 1.1 between the two phases. It can be seen that the MSRL method converges after 5000 rounds of training, while the baseline MATD3 algorithm only approaches convergence after 7000 rounds of training.

[0134] Based on the above scheme, by combining the advantages of traditional methods and learning methods, the performance is significantly better than the single APF method and the end-to-end MARL method. Under the same task scale, it can significantly improve the algorithm's improvement and convergence speed while ensuring the tracking success rate. By simply adjusting the distance threshold parameter of the MSRL method proposed in this invention, the number of algorithm convergence rounds can be reduced to about 72% of the end-to-end MARL method in the presence of a target (e.g. Figure 5 ), and achieves reinforcement learning rewards and tracking task success rates comparable to end-to-end MARL upon final convergence.

[0135] This application also provides an example of autonomous tracking of multiple dynamic targets in a drone cluster, such as Figure 6 As shown, the specific processing process includes the following steps:

[0136] Step S601: Acquire the cluster information of the drone cluster, the environment information of the drone cluster, and the behavior data information of the drone cluster.

[0137] Step S602: Based on the environmental information, identify the information of each restriction in the environment where the drone cluster is located, and the initial target position information of the dynamic target cluster in the environment where the drone cluster is located, and based on the cluster information, identify the initial drone position information of each drone in the drone cluster, and the cluster target position information of the cluster target of the dynamic target cluster.

[0138] Step S603: Based on the information of each restriction object, a scene plane model of the drone cluster is constructed, and based on the initial target position information of the dynamic target cluster, the initial drone position information of each drone, and the cluster target position information of the cluster target of the dynamic target cluster, the drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster target are identified in the scene plane model.

[0139] In step S604, the drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster target are added to the scene plane model to obtain a scene model of the drone cluster.

[0140] In step S605 , for each drone in the drone cluster, the speed information and the location information of the drone are identified based on the behavior data information of the drone.

[0141] Step S606: Based on the speed information of the drone, a motion model of the drone is constructed through a motion modeling strategy, and based on the position information of the drone, progressive position state information of the drone is generated through a position state algorithm.

[0142] In step S607, the motion model of the drone and the position state progressive information of the drone are used as the drone behavior model of the drone, and all the drone behavior models are used as the cluster behavior model of the drone cluster.

[0143] Step S608 , identifying the drone position distribution information of the drone cluster in the scene model, the target position distribution information of the dynamic target cluster in the scene model, and the restricted position information of each restricted object in the scene model.

[0144] Step S609 : Based on the UAV position distribution information, the target position distribution information, and the restricted position information, a cluster path planning strategy is used to generate path planning information for the cluster behavior model of each UAV.

[0145] In step S610 , based on the path planning information, the target tracking process of the cluster behavior model is simulated through the scene model to obtain new drone position distribution information of the drone cluster and new target position distribution information of the dynamic target cluster.

[0146] Step S611, based on the new target position distribution information and the new drone position distribution information, the center position division algorithm is used to identify the target center position information of the dynamic target cluster and the drone center position information of the drone cluster, and based on the target center position information and the drone center position information, the distance information between the drone cluster and each dynamic target cluster is calculated.

[0147] Step S612: When the distance information is lower than the preset distance threshold, it is determined that the distance information meets the preset distance condition, and the scene model is updated based on the new drone position distribution information of each drone and the new target position distribution information of the dynamic target cluster to obtain a new scene model.

[0148] Step S613, based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target, a new tracking route for each drone in the drone cluster is generated through a multi-dynamic target tracking algorithm.

[0149] Step S614: Based on the new tracking routes of each drone, the new scene model is updated to obtain the current drone position distribution information of the drone cluster and the current target position distribution information of the dynamic target cluster. If the current position information of the drone does not exist and overlaps with the current target position distribution information of the dynamic target cluster, the current drone position distribution information of the drone cluster is replaced by the new drone position distribution information of the drone cluster, and the current target position distribution information of the dynamic target cluster is replaced by the current target position distribution information of the dynamic target cluster.

[0150] Step S615, return to step S614, until the current position information of the drone exists and overlaps with the current target position distribution information of the dynamic target cluster, the new tracking route obtained in each iteration is summarized in the generation time sequence of each new tracking route to obtain the dynamic tracking control strategy of the drone cluster.

[0151] In step S616, based on the dynamic tracking control strategy, the drone cluster is controlled to track each dynamic target cluster, thereby completing the multi-dynamic target autonomous tracking task of the drone cluster.

[0152] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0153] Based on the same inventive concept, the present application also provides a drone swarm multi-dynamic target autonomous tracking device for implementing the aforementioned drone swarm multi-dynamic target autonomous tracking method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the one or more drone swarm multi-dynamic target autonomous tracking device embodiments provided below can be found in the above-mentioned limitations of the drone swarm multi-dynamic target autonomous tracking method, and will not be repeated here.

[0154] In an exemplary embodiment, Figure 7 As shown, a multi-dynamic target autonomous tracking device for a drone swarm is provided, comprising: an acquisition module 710, a generation module 720, an identification module 730, and a control module 740, wherein:

[0155] An acquisition module 710 is configured to acquire cluster information of a drone cluster, environmental information of the drone cluster, and behavioral data information of the drone cluster, and construct a scenario model of the drone cluster based on the cluster information and environmental information;

[0156] A generation module 720 is configured to construct a cluster behavior model of the drone cluster based on the behavior data information of the drone cluster, and generate path planning information of the cluster behavior model based on the scenario model and a cluster path planning strategy;

[0157] an identification module 730 for identifying distance information between the drone cluster and each dynamic target cluster based on the path planning information and the scenario model, and generating a dynamic tracking control strategy for the drone cluster based on the scenario model and a multi-dynamic target tracking algorithm when the distance information meets a preset distance condition;

[0158] The control module 740 is used to control the UAV cluster based on the dynamic tracking control strategy, track each of the dynamic target clusters, and complete the multi-dynamic target autonomous tracking task of the UAV cluster.

[0159] Optionally, the acquisition module 710 is specifically configured to:

[0160] Based on the environmental information, identifying information about each restriction in the environment in which the drone cluster is located, and initial target position information of a dynamic target cluster in the environment in which the drone cluster is located, and based on the cluster information, identifying initial drone position information of each drone in the drone cluster, and cluster target position information of cluster targets of the dynamic target cluster;

[0161] Based on the information of each of the restricting objects, a scene plane model of the drone cluster is constructed, and based on the initial target position information of the dynamic target cluster, the initial drone position information of each of the drones, and the cluster target position information of the cluster targets of the dynamic target cluster, drone position parameters of the drone cluster, target position parameters of the dynamic target cluster, and cluster target position parameters of the cluster targets are identified in the scene plane model;

[0162] The drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster target are added to the scene plane model to obtain the scene model of the drone cluster.

[0163] Optionally, the generating module 720 is specifically configured to:

[0164] For each drone in the drone cluster, identifying the speed information and the location information of the drone based on the behavior data information of the drone;

[0165] Based on the speed information of the UAV, a motion model of the UAV is constructed by a motion modeling strategy, and based on the position information of the UAV, position state progressive information of the UAV is generated by a position state algorithm;

[0166] The motion model of the drone and the progressive information of the position state of the drone are used as the drone behavior model of the drone, and all the drone behavior models are used as the cluster behavior model of the drone cluster.

[0167] Optionally, the generating module 720 is specifically configured to:

[0168] Identifying drone position distribution information of the drone cluster in the scene model, target position distribution information of the dynamic target cluster in the scene model, and restricted position information of each restricted object in the scene model;

[0169] Based on the UAV position distribution information, the target position distribution information, and each of the restricted position information, a cluster path planning strategy is used to generate path planning information of the cluster behavior model of each UAV.

[0170] Optionally, the identification module 730 is specifically configured to:

[0171] Based on the path planning information, the target tracking process of the cluster behavior model is simulated through the scenario model to obtain new drone position distribution information of the drone cluster and new target position distribution information of the dynamic target cluster;

[0172] Based on the new target position distribution information and the new drone position distribution information, the target center position information of the dynamic target cluster and the drone center position information of the drone cluster are identified through the center position division algorithm, and based on the target center position information and the drone center position information, the distance information between the drone cluster and each dynamic target cluster is calculated.

[0173] Optionally, the identification module 730 is specifically configured to:

[0174] When the distance information is lower than a preset distance threshold, determining that the distance information satisfies a preset distance condition, and updating the scene model based on the new drone position distribution information of each drone and the new target position distribution information of the dynamic target cluster to obtain a new scene model;

[0175] Based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target, a new tracking route for each drone in the drone cluster is generated by a multi-dynamic target tracking algorithm;

[0176] Based on the new tracking routes of the drones, the new scene model is updated to obtain the current drone position distribution information of the drone cluster and the current target position distribution information of the dynamic target cluster. When there is no current drone position information that overlaps with the current target position distribution information of the dynamic target cluster, the new drone position distribution information of the drone cluster is replaced by the current drone position distribution information of the drone cluster, and the current target position distribution information of the dynamic target cluster is replaced by the current target position distribution information of the dynamic target cluster.

[0177] Return to the step of generating a new tracking route for each drone in the drone cluster based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target through a multi-dynamic target tracking algorithm, until the current position information of the drone exists and overlaps with the current target position distribution information of the dynamic target cluster. Then, the new tracking route obtained in each iteration is summarized in the generation time order of each new tracking route to obtain the dynamic tracking control strategy of the drone cluster.

[0178] Each module in the aforementioned UAV swarm multi-dynamic target autonomous tracking device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0179] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be achieved via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for autonomous tracking of multiple dynamic targets in a drone swarm. The display unit of the computer device is used to produce a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0180] Those skilled in the art will understand that Figure 8The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0181] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of any one of the methods in the first aspect when executing the computer program.

[0182] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0183] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of any one of the methods of the first aspect when executed by a processor.

[0184] 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, stored data, displayed data, 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 relevant data must comply with relevant regulations.

[0185] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, 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 various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0186] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0187] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for autonomous tracking of multiple dynamic targets in a swarm of drones, characterized in that: The method comprises: Acquire cluster information of a drone cluster, environmental information of the drone cluster, and behavioral data information of the drone cluster, and construct a scenario model of the drone cluster based on the cluster information and the environmental information; Based on the behavioral data information of the drone cluster, a cluster behavior model of the drone cluster is constructed, and based on the scenario model, path planning information of the cluster behavior model is generated through a cluster path planning strategy; Based on the path planning information, the distance information between the drone cluster and each dynamic target cluster is identified through the scenario model, and when the distance information meets a preset distance condition, a dynamic tracking control strategy for the drone cluster is generated based on the scenario model and through a multi-dynamic target tracking algorithm; Based on the dynamic tracking control strategy, the drone cluster is controlled to track each of the dynamic target clusters to complete the multi-dynamic target autonomous tracking task of the drone cluster.

2. The method according to claim 1, characterized in that The step of constructing a scene model of the drone cluster based on the cluster information and the environmental information includes: Based on the environmental information, identifying information about each restriction in the environment in which the drone cluster is located, and initial target position information of a dynamic target cluster in the environment in which the drone cluster is located, and based on the cluster information, identifying initial drone position information of each drone in the drone cluster, and cluster target position information of cluster targets of the dynamic target cluster; Based on the information of each of the restricting objects, a scene plane model of the drone cluster is constructed, and based on the initial target position information of the dynamic target cluster, the initial drone position information of each of the drones, and the cluster target position information of the cluster targets of the dynamic target cluster, drone position parameters of the drone cluster, target position parameters of the dynamic target cluster, and cluster target position parameters of the cluster targets are identified in the scene plane model; The drone position parameters of the drone cluster, the target position parameters of the dynamic target cluster, and the cluster target position parameters of the cluster target are added to the scene plane model to obtain the scene model of the drone cluster.

3. The method according to claim 1, characterized in that The step of constructing a cluster behavior model of the drone cluster based on the behavior data information of the drone cluster includes: For each drone in the drone cluster, identifying the speed information and the location information of the drone based on the behavior data information of the drone; Based on the speed information of the UAV, a motion model of the UAV is constructed by a motion modeling strategy, and based on the position information of the UAV, position state progressive information of the UAV is generated by a position state algorithm; The motion model of the drone and the progressive information of the position state of the drone are used as the drone behavior model of the drone, and all the drone behavior models are used as the cluster behavior model of the drone cluster.

4. The method according to claim 2, characterized in that Generating path planning information of the cluster behavior model based on the scenario model through a cluster path planning strategy includes: Identifying drone position distribution information of the drone cluster in the scene model, target position distribution information of the dynamic target cluster in the scene model, and restricted position information of each restricted object in the scene model; Based on the UAV position distribution information, the target position distribution information, and each of the restricted position information, a cluster path planning strategy is used to generate path planning information of the cluster behavior model of each UAV.

5. The method according to claim 4, characterized in that The identifying, based on the path planning information and using the scene model, distance information between the drone cluster and each dynamic target cluster includes: Based on the path planning information, the target tracking process of the cluster behavior model is simulated through the scenario model to obtain new drone position distribution information of the drone cluster and new target position distribution information of the dynamic target cluster; Based on the new target position distribution information and the new drone position distribution information, the target center position information of the dynamic target cluster and the drone center position information of the drone cluster are identified through the center position division algorithm, and based on the target center position information and the drone center position information, the distance information between the drone cluster and each dynamic target cluster is calculated.

6. The method according to claim 2, characterized in that When the distance information satisfies a preset distance condition, a dynamic tracking control strategy for the UAV cluster is generated based on the scene model and through a multi-dynamic target tracking algorithm, including: When the distance information is lower than a preset distance threshold, determining that the distance information satisfies a preset distance condition, and updating the scene model based on the new drone position distribution information of each drone and the new target position distribution information of the dynamic target cluster to obtain a new scene model; Based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target, a new tracking route for each drone in the drone cluster is generated by a multi-dynamic target tracking algorithm; Based on the new tracking routes of the drones, the new scene model is updated to obtain the current drone position distribution information of the drone cluster and the current target position distribution information of the dynamic target cluster. When there is no current drone position information that overlaps with the current target position distribution information of the dynamic target cluster, the new drone position distribution information of the drone cluster is replaced by the current drone position distribution information of the drone cluster, and the current target position distribution information of the dynamic target cluster is replaced by the current target position distribution information of the dynamic target cluster. Return to the step of generating a new tracking route for each drone in the drone cluster based on the new drone position distribution information of the drone cluster, the new target position distribution information of the dynamic target cluster, and the cluster target position information of the cluster target through a multi-dynamic target tracking algorithm, until the current position information of the drone exists and overlaps with the current target position distribution information of the dynamic target cluster. Then, the new tracking route obtained in each iteration is summarized in the generation time order of each new tracking route to obtain the dynamic tracking control strategy of the drone cluster.

7. An autonomous tracking device for multiple dynamic targets in a swarm of drones, characterized in that: The device comprises: An acquisition module is used to obtain cluster information of a drone cluster, environmental information of the drone cluster, and behavioral data information of the drone cluster, and to construct a scene model of the drone cluster based on the cluster information and the environmental information; A generation module, configured to construct a cluster behavior model of the drone cluster based on the behavior data information of the drone cluster, and generate path planning information of the cluster behavior model through a cluster path planning strategy based on the scenario model; an identification module for identifying distance information between the UAV cluster and each dynamic target cluster based on the path planning information and the scenario model, and generating a dynamic tracking control strategy for the UAV cluster based on the scenario model and a multi-dynamic target tracking algorithm when the distance information meets a preset distance condition; The control module is used to control the UAV cluster based on the dynamic tracking control strategy, track each dynamic target cluster, and complete the multi-dynamic target autonomous tracking task of the UAV cluster.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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