Heterogeneous agent cooperative control method for water surface of unfamiliar area

Abstract

The invention belongs to the technical field of marine search, and particularly relates to a heterogeneous agent cooperative control method for the water surface of an unfamiliar area. The unfamiliar area pursuit method can realize global coverage and quick response, and comprises the following steps: S1, scientifically dividing areas according to the number of first-class intelligent agents, ensuring that subarea boundaries are clear and non-overlapped and are adaptive to operation radiuses of the subareas, and allocating the first-class intelligent agents to each subarea to execute an idle garrison mechanism, and meanwhile, the second type of intelligent agent is controlled to execute circumferential cruise for wide-area observation. And S2, when a target object is detected in any partition, the second type of intelligent agent determines an adaptive first type of intelligent agent through a screening strategy based on real-time information such as target position coordinates and moving speed, and the state of the first type of intelligent agent is switched to a chasing mechanism until the target is captured or disposed. And S3, after the task is completed, the first type of agent is switched back to the idle garrison mechanism and returns to the corresponding partition. And S4, circularly executing the steps S2-S3 until all targets are processed.

Description

Technical Field

[0001] This application belongs to the field of maritime search and rescue technology, specifically relating to a method for cooperative control of heterogeneous intelligent agents in unfamiliar water areas. Background Technology

[0002] Safety monitoring, emergency search and rescue, and environmental monitoring activities in unfamiliar areas (such as unexplored sea areas, remote shipping routes, and areas affected by sudden disasters) are becoming increasingly frequent. These areas are characterized by unknown environments, lack of pre-set operational benchmarks, and dynamic and ever-changing sea conditions, placing stringent demands on the adaptability and control precision of intelligent agents in collaborative operations. The traditional operational model, which relies primarily on maritime rescue vessels and secondarily on helicopters, faces challenges in unfamiliar areas, including difficulties in refueling, poor environmental adaptability, and the lack of pre-set route references. Furthermore, the reliance on a single platform control method with manual monitoring has limitations such as limited coverage, delayed response, and susceptibility to unknown risks, and can no longer meet the core requirements of "rapid perception, precise coordination, and efficient response" on the surface of unfamiliar waters.

[0003] To overcome the aforementioned challenges, intelligent agents centered on drones and unmanned surface vessels (USVs) have become the preferred carriers for operations on unfamiliar water surfaces, and intelligent agent collaborative control technology is key to maximizing their effectiveness. Drones possess advantages in high-altitude wide-area observation and long-distance communication, enabling them to quickly acquire environmental information in unfamiliar areas; USVs have the ability to maneuver and remain on water for extended periods, allowing them to perform close-range reconnaissance and response tasks. Collaborative control of both can achieve a closed-loop operation of "environmental perception - task allocation - action execution," becoming an effective way to address unknown risks in unfamiliar areas. However, existing intelligent agent collaborative control technologies have significant limitations: most are designed for known environments and do not fully consider the unknown and dynamic characteristics of unfamiliar environments. They focus only on path planning or local action control of a single intelligent agent, lacking a global collaborative control mechanism across intelligent agents. This results in poor information exchange between agents, a lack of dynamic adaptability in task allocation, and low collaborative operation efficiency, making it difficult to meet the complex needs of unfamiliar water surfaces.

[0004] Optimizing intelligent agent collaborative control technology in unfamiliar waters is of great significance for improving maritime emergency response capabilities, reducing operational risks in unfamiliar waters, and minimizing casualties and property losses. As a major maritime nation, my country urgently needs to overcome the technological bottlenecks of intelligent agent collaborative control in unfamiliar environments, addressing core issues such as collaborative perception, dynamic task allocation, and precise control in unknown scenarios. Therefore, developing an intelligent agent collaborative control method adapted to unfamiliar waters can provide reliable technical support for various maritime operations in unfamiliar waters, contributing to the high-quality development of the marine economy and society, and possessing significant practical value and broad application prospects. Summary of the Invention

[0005] The purpose of this application is to provide an intelligent agent collaborative control method that is adapted to unfamiliar water areas, which can provide reliable technical support for various maritime operations in unfamiliar waters.

[0006] The embodiments of this application can be implemented through the following technical solutions: A method for cooperative control of heterogeneous intelligent agents on unfamiliar water surfaces, wherein the heterogeneous intelligent agents include a first type of intelligent agent and a second type of intelligent agent, comprising the following steps: S1: Divide the unfamiliar area into partitions according to the number of the first type of intelligent agents, assign a first type of intelligent agent to each partition and make it execute the idle guarding mechanism, while controlling the second type of intelligent agents to execute the circular patrol mechanism; S2: When a target object is detected in the partition corresponding to any first-type intelligent agent, the second-type intelligent agent, based on the real-time information of the target object, selects the first-type intelligent agent that is suitable for tracking the target object through a filtering strategy, and switches the working state of the first-type intelligent agent from the idle guarding mechanism to the pursuit mechanism until the first-type intelligent agent captures or disposes of the target object. The real-time information of the target object includes, but is not limited to, the target object's position coordinates, movement speed, and heading angle; S3: Switch the working state of the first type of intelligent agent from the pursuit mechanism to the idle guarding mechanism, and return to its corresponding partition; S4: Repeat steps S2 to S3 until all target objects have been captured or disposed of.

[0007] Furthermore, when the first type of intelligent agent is in the idle stationary mechanism, it uses a spiral search mode to conduct area detection; When multiple second-type intelligent agents execute the circular cruise mechanism, they adopt an anti-phase circular cruise cooperative mode.

[0008] Further, in step S2, the screening strategy includes the following steps: S21: Collect the state information of all first-type intelligent agents in the idle guarding mechanism. The state information includes, but is not limited to, the real-time movement speed, remaining energy, payload capacity and straight-line distance relative to the target object of each first-type intelligent agent. S22: Based on the environmental constraints of the unfamiliar area, predict the potential escape route of the target object according to the real-time information of the target object. The environmental constraints of the unfamiliar area include, but are not limited to, the water flow velocity, wave level, and obstacle distribution within the area. S23: Based on the potential escape routes of the target object, select the first type of intelligent agent with the shortest expected arrival time on the potential escape route, and determine it as the intelligent agent to perform the target object tracking task.

[0009] Furthermore, in step S2, the strategy of the first type of agent switching to the pursuit mechanism during the pursuit of the target object is as follows: If the straight-line distance between the first type of intelligent agent and the target object is less than the perception radius of the target object, then the agent pursues the target object according to its potential escape route using a pursuit-escape game strategy; otherwise, the agent pursues the target object directly according to its potential escape route.

[0010] Furthermore, the execution process of the pursuit-escape game strategy is as follows: S24: Establish a two-player zero-sum game model of pursuit and escape, with the first type of intelligent agent switching to the pursuit mechanism as the pursuer and the target object as the escapee. S25: Solve the Nash equilibrium solution for a two-player zero-sum chase-escape game model based on the differential evolution algorithm to obtain the optimal strategy pair for both the chaser and the escapee; S26: Integrate the preset physical constraints into the optimal strategy to optimize the corresponding pursuit strategy. The physical constraints include, but are not limited to, the maximum speed limit, minimum turning radius limit, smooth steering requirements based on heading angular velocity control, and a collision avoidance strategy based on collision radius. S27: Output the optimized tracking strategy of the first type of intelligent agent, and control the first type of intelligent agent to perform a pursuit operation on the target object according to the optimized tracking strategy.

[0011] Furthermore, the execution steps of the advance avoidance strategy are as follows: S261: The straight path from the first type of intelligent agent to the target object is uniformly sampled into several sampling points at a preset sampling interval, and it is determined in turn whether each sampling point is within the safe avoidance range of the obstacle. If so, the first type of intelligent agent continues to pursue the target object according to its potential escape route; otherwise, step S262 is executed. S262: Locate the first detected obstacle on the straight path as the primary obstacle, and generate a preferred waypoint that meets the requirements for safe detour based on the core parameters of the primary obstacle and the current path direction of the first type of agent.

[0012] Furthermore, in step S262, when the target object is not obscured by obstacles and only the straight line of pursuit by the first type of intelligent agent is blocked, the preferred waypoint is determined according to the normal tracking scenario; when the target object is directly obscured by obstacles and the first type of intelligent agent needs to bypass the obstacles while maintaining tracking of the target object, the preferred waypoint is determined according to the dynamic target tracking scenario.

[0013] Further, step S1 includes the following steps: S11: Perform initial partitioning of the unfamiliar region based on the number of the first type of intelligent agents; S12: Verify the feasibility of the initial partition center point relative to the avoidance area. If it is feasible, use the initial partition center point as the patrol origin and execute step S15; otherwise, execute step S13. S13: Extend the boundary of the initial partition outward by a first preset distance, and form uniform grid points in the extended partition area as candidate origins; S14: Eliminate candidate points that are less than the second preset distance from the avoidance area, and select the point that is closest to the initial partition center origin from the remaining candidate points as the patrol origin; S15: Assign a first-class agent to each partition and have it perform an idle guarding mechanism centered on the patrol origin, while controlling a second-class agent to perform a circular patrol mechanism.

[0014] Furthermore, the first type of intelligent agent is an unmanned surface vessel, and the second type of intelligent agent is an unmanned aerial vehicle (UAV).

[0015] The heterogeneous intelligent agent cooperative control method for unfamiliar water surfaces provided in the embodiments of this application has at least the following beneficial effects: To achieve full coverage and rapid response in unfamiliar areas, the area is first scientifically divided into zones based on the number of Type I agents. This ensures clear boundaries, non-overlapping coverage, and suitability for the operational radius of the Type I agents. Then, a Type I agent is assigned to each zone, executing a pre-defined idle monitoring mechanism within its designated zone. This allows the selected agent to initiate a task directly from its monitoring position when a target object is detected in any zone, significantly reducing response distance and time costs. Simultaneously, leveraging the high-altitude wide-area observation, maneuverability, and long-distance information transmission capabilities of Type II agents, the agents are controlled to perform a circular patrol mechanism over the unfamiliar area. Through continuous and comprehensive airspace scanning, the dynamic tracks of target objects are quickly captured, and real-time, comprehensive information about the target and its surrounding environment is collected and transmitted, providing reliable data support for subsequent cross-agent collaborative decision-making and task scheduling. This application designs a collaboratively optimized search mode for the idle stationing and patrol mechanisms of two types of intelligent agents. The detection range of the unmanned surface vessel is circular. When it is in the idle stationing mechanism, it will start a spiral search mode at the center point of the corresponding partition. The detection range of the unmanned aerial vehicle (UAV) is fan-shaped. In order to enhance the integrity of the entire airspace detection, this application adopts a dual-aircraft anti-phase circular patrol collaborative mode to solve the problems of blind spots at partition boundaries, repeated coverage of areas, and long-term missed detection that are easily caused by the detection range characteristics of the first type of intelligent agent (unmanned surface vessel) and the second type of intelligent agent (UAV). To avoid the problem of having no safe origin points to choose from due to the initial partition center point being surrounded by obstacles, the boundaries of risky partitions are expanded outwards. The expansion distance is determined according to preset standards, which ensures that the expanded area is still within the overall control range of the target area, while significantly broadening the selection space of safe candidate patrol origin points and providing sufficient samples for subsequent screening. This application uses different generation logics for safe detour waypoints in the early avoidance strategy based on real-world scenarios to ensure that tracking of the target object is maintained while avoiding obstacles. Attached Figure Description

[0016] Figure 1 This is a flowchart of a heterogeneous intelligent agent cooperative control method for an unfamiliar water surface according to this application; Figure 2 This is a flowchart of step S1 in this application; Figure 3 A flowchart of the screening strategy; Figure 4 A flowchart of a pursuit-escape game strategy; Figure 5 A flowchart for an early avoidance strategy; Figure 6 Example of an advance avoidance strategy Figure 1 ; Figure 7 Example of an advance avoidance strategy Figure 2 ; Figure 8 A schematic diagram showing the second type of intelligent agent executing a circular patrol mechanism after the partition allocation is completed and the first type of intelligent agent executes the idle guarding mechanism; Figure 9 A schematic diagram of the trajectory for collaborative search by the first and second type of intelligent agents; Figure 10 The trajectory diagram of the entire tracking game between USV0 and target 0; Figure 11 The trajectory diagram of the entire tracking game between USV0 and Target 1; Figure 12 The trajectory diagram of the entire tracking game between USV2 and Target 2; Figure 13 The trajectory diagram of the entire tracking game between USV3 and Target 3; Figure 14 A flight path diagram for USV0 pursuing target 0; Figure 15 A flight path diagram for USV0's pursuit of target 1; Figure 16 A flight path diagram for USV2 pursuing target 2; Figure 17A flight path diagram for USV3 pursuing target 3; Figure 18 Distance map for USV0 pursuing target 0; Figure 19 Distance map for USV0 pursuing target 1; Figure 20 Distance map for USV2 pursuing target 2; Figure 21 Distance map for USV3 pursuing target 3; Figure 22 A course deviation diagram for USV0 pursuing target 0; Figure 23 A deviation diagram of the course of USV0 pursuing target 1; Figure 24 A deviation diagram of the course of USV2 pursuing target 2; Figure 25 A heading deviation diagram for USV3 pursuing target 3. Detailed Implementation

[0017] The present application will now be further described based on preferred embodiments and with reference to the accompanying drawings.

[0018] The vocabulary used in this specification is for illustrative purposes and is not intended to limit the scope of this application. Unless otherwise expressly specified and limited, the terms "set," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, a direct connection, or an indirect connection via an intermediate medium; or they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of these terms in this application.

[0019] Furthermore, in the description of the embodiments of this application, various components on the drawings have been enlarged or reduced for ease of understanding, but this is not intended to limit the scope of protection of this application.

[0020] This application provides a heterogeneous intelligent agent collaborative control method for unfamiliar water surfaces (hereinafter referred to as the "method"). This method is designed to meet the collaborative operation needs of heterogeneous intelligent agents, effectively achieving precise scheduling and efficient collaborative control among different types of intelligent agents. The heterogeneous intelligent agents are functionally divided into a first type and a second type. Each type performs its own function and cooperates with the others to complete the entire process of operation on unfamiliar water surfaces, from target detection and trajectory prediction to tracking and capture. In a specific embodiment of this application, to clearly illustrate the execution logic, application scenarios, and technical effects of the collaborative control method, and considering the mainstream application scenarios in the current field of intelligent water surface operations, the first type of intelligent agent is exemplarily set as an unmanned surface vessel (USV), and the second type of intelligent agent is exemplarily set as a drone. The USV possesses the core advantages of prolonged water surface stay and close-range response, while the drone has the technical characteristics of high-altitude wide-area observation and long-distance information transmission. The two complement each other through the collaborative control strategy of this application, fully adapting to the operational characteristics of unfamiliar water surfaces—unknown environments, no preset operational benchmarks, and dynamically changing sea conditions—maximizing the collaborative operational efficiency of heterogeneous intelligent agents.

[0021] Figure 1 A flowchart of the method is shown, as follows: Figure 1 As shown, the method includes the following steps: S1: Divide the unfamiliar area into partitions based on the number of first-class agents, assign a first-class agent to each partition and make it execute the idle guarding mechanism, while controlling the second-class agents to execute the circular patrol mechanism.

[0022] To achieve full coverage and rapid response in unfamiliar areas, the area is first scientifically divided into zones based on the number of Type I agents. This ensures clear boundaries, non-overlapping coverage, and suitability for the operational radius of the Type I agents. Then, a Type I agent is assigned to each zone, executing a pre-defined idle monitoring mechanism within its designated zone. This allows the selected agent to initiate a task directly from its monitoring position when a target object is detected in any zone, significantly reducing response distance and time costs. Simultaneously, leveraging the high-altitude wide-area observation, maneuverability, and long-distance information transmission capabilities of Type II agents, the agents are controlled to perform circular patrols over the unfamiliar area. Through continuous and comprehensive airspace scanning, they quickly capture the dynamic tracks of target objects, simultaneously collecting and transmitting real-time, comprehensive information about the target and its surrounding environment. This provides reliable data support for subsequent cross-agent collaborative decision-making and task scheduling.

[0023] In some preferred embodiments of this application, in order to solve the problems of blind spots at the intersection of partition boundaries, repeated coverage of areas, and long-term missed detection that are easily caused by the detection range characteristics of the first type of intelligent agent (unmanned surface vessel) and the second type of intelligent agent (unmanned aerial vehicle), a collaboratively optimized search mode is designed for the idle stationing and patrol mechanism of the two types of intelligent agents. The detection range of the unmanned surface vessel (USV) is circular. When it is in an idle stationary mode, it will initiate a spiral search mode at the center point of the corresponding zone. This mode moves in a circle with the center point of the zone as the origin and a fixed length as the initial radius. The radius of the circle fluctuates periodically with time according to a sinusoidal law. At the same time, a fixed orthogonal phase offset is added to the search path of the USV in adjacent zones. The path phase difference avoids excessive overlap of the search areas of adjacent USVs, which greatly improves the coverage uniformity and search efficiency within the zone. The detection range of the unmanned aerial vehicle (UAV) is fan-shaped. In order to enhance the integrity of the entire airspace detection, this application adopts a dual-aircraft anti-phase circular cruise cooperative mode. That is, both UAVs use the geometric center point of the unfamiliar area as the center and a preset fixed length as the cruise radius to perform circular cruise flight at the maximum angular velocity. The cruise phase of the two UAVs always maintains a 180° difference. Through phase complementarity, the coverage gap of the fan-shaped detection range of a single UAV is filled. Together with the spiral search of the USV, a sea-air cooperative full-coverage detection network is formed, which effectively avoids the problems of missed detection and duplicate coverage.

[0024] In some preferred embodiments of this application, unfamiliar areas often contain fixed or temporary obstacles such as reefs, shipwrecks, and no-navigation zones. These areas directly affect the patrol safety and zoning coverage integrity of the first type of intelligent agent (unmanned surface vessel). To ensure that the unmanned surface vessel can accurately cover the corresponding zone while strictly avoiding the risk of obstacle collision when implementing the idle stationing mechanism, step S1 (regional zoning and intelligent agent deployment) needs to determine the optimal patrol origin point through multi-stage screening, such as... Figure 2 As shown, step S1 is further broken down into the following steps: S11: Perform initial partitioning of the unfamiliar region based on the number of first-class intelligent agents; First, considering the number of the first type of intelligent agents (unmanned surface vessels), the operating radius of a single vessel, and the geometry of the unfamiliar area, a balanced partitioning algorithm is used to initially divide the target area. During the partitioning process, it is necessary to ensure that the area of ​​each partition matches the coverage capability of the unmanned vessel, and that the partition boundaries are clear with no obvious overlap or omissions, laying the foundation for subsequent dedicated deployment by a single vessel.

[0025] S12: Verify the feasibility of the initial partition center point relative to the avoidance area. If it is feasible, use the initial partition center point as the patrol origin; otherwise, proceed to step S13. Using the geometric center point of each partition as a candidate initial patrol origin, the motion trajectory of the unmanned surface vessel (USV) initiating a spiral search mode with that point as the center is simulated using environmental modeling tools. The focus is on verifying whether the trajectory touches the avoidance areas (obstacles) within and around the partition: if the simulation results show that the USV maintains a safe distance from the avoidance area throughout the complete spiral search (including the radius sinusoidal fluctuation process), then the center point of that partition is directly determined as the final patrol origin without further adjustment; if the trajectory poses a risk of touching the avoidance area, then the origin optimization and screening process begins, proceeding to step S13.

[0026] S13: Extend the boundary of the initial partition outward by a first preset distance, and form uniform grid points within the extended partition area as candidate origins; To avoid the problem of having no safe origin point to choose from due to the initial partition center point being surrounded by obstacles, the boundaries of the partitions with potential risks are expanded outwards. The expansion distance is determined according to a preset standard (this preset distance is not less than the sum of the minimum turning radius of the unmanned surface vessel and the safe buffer distance), which ensures that the expanded area is still within the overall control range of the target area, while significantly broadening the selection space of safe candidate patrol origin points and providing sufficient samples for subsequent screening.

[0027] Within the expanded partitioned area, a grid-based point placement algorithm is used to generate uniformly distributed candidate origins. The grid density needs to be set in conjunction with the partition area and the detection accuracy of the unmanned surface vessel (USV) to ensure that the distance between adjacent candidate origins is no greater than half of the USV's detection range, avoiding the omission of optimal origins due to overly sparse candidate point distribution; at the same time, the number of grids is controlled within the computational capacity to improve the efficiency of subsequent screening. Each generated grid point serves as a potential patrol origin candidate.

[0028] S14: Eliminate candidate points that are less than the second preset range from the avoidance area, and select the point that is closest to the initial partition center origin from the remaining candidate points as the patrol origin.

[0029] A dual screening process is performed on all candidate origin points: the first step is safety filtering, eliminating candidate points whose distance from the avoidance zone is smaller than a second preset range (the sum of the UAV's emergency braking distance and obstacle buffer distance), ensuring that the remaining candidate origin points are all within the safe zone; the second step is optimal selection, calculating the straight-line distance between each safe candidate origin point and the original zone center point, and selecting the point closest to the original center point as the final patrol origin point. This selection criterion maximizes the match between the UAV's patrol range and the initial zone, reduces coverage deviation, and balances operational efficiency with safety redundancy.

[0030] S15: Assign a first-class agent to each partition to perform an idle guarding mechanism centered on the patrol origin, while controlling a second-class agent to perform a circular patrol mechanism.

[0031] After determining the patrol origin point through the above steps, the unmanned surface vessel can start a spiral search mode based on the origin point, and cooperate with the second type of intelligent agent (unmanned aerial vehicle) for the opposite circular cruise to form an initial deployment pattern of "sea-air collaboration, safety without blind spots".

[0032] S2: When a target object is detected in the partition corresponding to any first-type intelligent agent, the second-type intelligent agent, based on the real-time information of the target object, selects the first-type intelligent agent that is suitable for tracking the target object through a filtering strategy, and switches the working state of the first-type intelligent agent from the idle guarding mechanism to the pursuit mechanism until the first-type intelligent agent captures or disposes of the target object.

[0033] This step outlines the agent-based collaborative response mechanism after target detection. Its core lies in building an efficient "any detection - unified scheduling" response link. When a first-type agent (unmanned surface vessel) detects a target object within its corresponding zone, or a second-type agent (unmanned aerial vehicle) discovers a target object within any zone during its patrol, the second-type agent acts as the scheduling core. Based on the target object's real-time information (including position coordinates, speed, heading angle, and surrounding environmental data), it uses a preset filtering strategy to select the first-type agent suitable for the tracking task. Specifically, if the target is detected by a first-type agent, it immediately synchronizes the target information to the second-type agent via a communication link. The second-type agent then assesses the information's real-time performance and completeness, determines the optimal first-type agent, sends a state switching command, and establishes a real-time information synchronization channel. If the target is detected by a second-type agent, it can directly complete the selection and scheduling of first-type agents based on its own accurately collected target information. The selected first-class intelligent agent will immediately switch from the idle stationary mechanism to the pursuit mechanism, and perform the tracking task with the support of the target dynamic information continuously provided by the second-class intelligent agent, until the target object is captured or disposed of.

[0034] Specifically, such as Figure 3 As shown, the screening strategy in step S2 includes the following steps: S21: Collect the state information of all Type I agents in the idle standby mechanism. The state information includes, but is not limited to, the real-time movement speed, remaining energy, payload capacity and straight-line distance relative to the target object of each Type I agent. The core logic of this step is as follows: On the one hand, the pursuit capability of unmanned surface vessels (USVs) is a comprehensive reflection of multiple factors such as speed, energy, and payload. Focusing only on USVs that are close at hand may result in them having to turn back midway due to insufficient energy, and considering only USVs that are fast may result in them being unable to complete the task due to the lack of capture devices. Therefore, it is necessary to collect status information in all dimensions. On the other hand, collecting information on USVs with idle stationary mechanisms can avoid including other intelligent agents that are performing other tasks in the screening process, reduce invalid calculations, and improve screening efficiency.

[0035] S22: Based on the environmental constraints of the unfamiliar area, predict the potential escape route of the target object according to the real-time information of the target object. Among them, the environmental constraints of unfamiliar areas include, but are not limited to, the water flow velocity, wave level and the distribution of obstacles in the area; The core logic of this step is that environmental factors in unfamiliar waters have a significant impact on the trajectory of a target object. When moving downstream, the target's escape velocity is superimposed on the water's velocity, and obstacles force the target object to change course. If the route is predicted solely based on the target object's current state, it is prone to deviation from the actual escape trajectory, leading to inefficient tracking by the unmanned surface vessel. Therefore, environmental constraints need to be incorporated as important parameters into the prediction model to make the escape route more closely resemble the actual scenario.

[0036] S23: Based on the potential escape routes of the target object, select the first type of intelligent agent with the shortest expected arrival time on the potential escape route, and identify it as the intelligent agent to perform the target object tracking task.

[0037] This step selects an agent capable of quickly intercepting the target object as the agent to perform the pursuit and tracking task. The determination of this agent is not simply based on "speed / time", but rather incorporates environmental influences and detour costs to ensure the authenticity and accuracy of the results.

[0038] Furthermore, once the first type of agent is identified to carry out the pursuit task, its operating state switches from the idle standby mechanism to the pursuit mechanism. The corresponding pursuit strategy is as follows: If the straight-line distance between the first type of agent and the target object is greater than the target object's perception radius, the agent will directly pursue along the target object's potential escape route. If the initial distance between the first type of agent and the target object is relatively close when switching to the pursuit mechanism, or if the target object enters the range that the first type of agent can perceive (i.e., the target object can perceive the distance between itself and the first type of agent carrying out the pursuit task in real time) as the distance between the two gradually decreases during the pursuit, the target object will enter the range that the first type of agent can perceive (i.e., the target object can perceive the distance between itself and the first type of agent carrying out the pursuit task in real time). In this case, direct pursuit based solely on the predicted escape route can no longer meet the accuracy and timeliness requirements of the pursuit task. Therefore, a pursuit-escape game strategy is required to carry out the pursuit operation based on the target object's potential escape route in order to ensure the pursuit effect.

[0039] Furthermore, such as Figure 4 As shown, the execution process of the pursuit-escape game strategy is as follows: S24: Establish a two-player zero-sum game model in which the first type of intelligent agent, which switches to the pursuit mechanism, is the pursuer and the target object is the escapee.

[0040] Step S24 visualizes the pursuit-escape adversarial relationship between "Type I intelligent agents—dynamic target objects" through a game theory model, using the adversarial characteristics of zero-sum games to characterize the essential conflict of interest between the two parties. Unlike the classic one-sided optimal control problem, in this two-person zero-sum game model, both the pursuer and the escapee have their own dedicated performance index functions (i.e., payoff functions).

[0041] Specifically, the payment function for the first type of intelligent agent performing the pursuit mission is: The payoff function for the target object is Its expression is as follows: ; Where distance represents the real-time straight-line distance between the first type of intelligent agent and the target object. i For a first-class intelligent agent, the specific policy is one of the available policy options. j The specific strategy in the set of optional strategies for the target object.

[0042] From this definition, it is easy to see that for a two-player zero-sum game, the payoff functions of both parties satisfy... That is, an increase in the gains of one party will necessarily correspond to a decrease in the gains of the other party, which is consistent with the conflicting interests characteristic of the pursuit and confrontation.

[0043] S25: Solve the Nash equilibrium solution for a two-player zero-sum game model based on the differential evolution algorithm to obtain the optimal strategy pair for both the pursuer and the pursuer; The Nash equilibrium here refers to a stable combination of strategies in which neither the pursuer nor the fleeing player can improve their own payoff by unilaterally changing their own strategies, given the opponent's strategy. Its expression is as follows: ; In this study, the game payoff matrix is ​​constructed using the distance parameter between the first type of agent and the target object. V The final payoffs for both sides after the game reaches equilibrium. J This represents the payoff calculation function in the game process. The set of all possible policies for the first type of agent. n The number of policies for the first type of agent. The set of all possible strategies for the target object. m The number of strategies for the target object.

[0044] In the specific solution, the differential evolution algorithm performs differential mutation operations (generating mutation vectors based on the differences between individuals in the population) and crossover operations (recombining the mutation vectors with the original individuals) on the policy variables of the first type of agent and the target object. Combined with selection operations, it retains the best individual and converges to the optimal policy pair corresponding to the Nash equilibrium solution after iterative optimization.

[0045] S26: Integrate the preset physical constraints into the optimal strategy to optimize the corresponding pursuit strategy. The physical constraints include, but are not limited to, the maximum speed limit, minimum turning radius limit, smooth steering requirements based on heading angular velocity control, and a collision avoidance strategy based on collision radius deflection heading collision advance avoidance strategy for the first type of intelligent agent.

[0046] In some specific embodiments of this application, such as Figure 5 As shown, the specific steps for implementing the advance avoidance strategy are as follows: S261: The straight path from the first type of intelligent agent to the target object is uniformly sampled into several sampling points at a preset sampling interval, and each sampling point is determined to be within the safe avoidance range of the obstacle. If so, the first type of intelligent agent continues to pursue the target object according to its potential escape route; otherwise, step S262 is executed.

[0047] In some specific embodiments of this application, in step S261, the sampling interval can be set according to the movement speed of the first type of intelligent agent and the distribution density of obstacles, and can be dynamically adjusted based on actual needs, and each sampling point records corresponding three-dimensional coordinate information.

[0048] In some specific embodiments of this application, in step S261, the safe avoidance range of the obstacle is a spatial area preset based on the size and type of the obstacle (such as static obstacles and dynamic obstacles) and safety redundancy requirements (for example, for fixed obstacles, the safe avoidance range is a circular area with the center of the obstacle as the center, the radius of the obstacle plus the radius of the first type of intelligent agent itself plus a safety distance of 2-3 meters).

[0049] S262: Locate the first detected obstacle on the straight path as the primary obstacle, and generate a preferred waypoint that meets the requirements for safe detour based on the core parameters of the primary obstacle and the current path direction of the first type of agent.

[0050] In some specific embodiments of this application, the reason for prioritizing the handling of the first obstacle in step S262 is that it directly obstructs the current pursuit path, and timely avoidance can prevent subsequent path planning from becoming passive.

[0051] In some specific embodiments of this application, in step S262, the core parameters of the main obstacle include the three-dimensional coordinates of the center point, physical radius information, and real-time location information.

[0052] It should be noted that the generation of safe detour waypoints in step S262 is divided into two scenarios based on the actual situation. One is a normal tracking scenario where the target object is not obscured by obstacles and only the agent's straight-line tracking path is blocked. The other is a dynamic target tracking scenario where the target object is directly blocked by obstacles and the agent needs to detour around the obstacles while maintaining tracking of the target object. The two scenarios correspond to different waypoint generation logic: Let's look at the first scenario: when the target object is not blocked by obstacles, the specific generation method is as follows: Using the center point of the main obstacle as the center, and the obstacle's safe radius (i.e., the sum of the obstacle's own radius and the preset safe redundancy distance) as the reference, an circumscribed square is drawn. The four vertices of this circumscribed square are the initial candidate detour points. These four points are chosen because they are furthest away from the core area of ​​the obstacle while covering the main detour directions around the obstacle, ensuring the safety and diversity of the detour path. Then, based on the preset first filtering condition, these four candidate points are screened to finally determine the optimal detour point.

[0053] Furthermore, the first filtering conditions include: 1. The straight-line distance between the candidate point and the current position of the first type of intelligent agent is not less than 100 meters. This distance threshold is set in combination with the control response speed of the intelligent agent and the accuracy of the detour path planning. The purpose is to avoid generating invalid waypoints that are too close to the current position, which would cause the detour actions to be too hasty or frequent; 2. The planned path from the current position of the first type of intelligent agent to the candidate point must be collision-free, that is, all sampling points on the path must not fall within the safe avoidance range of any obstacle; 3. The planned path from the candidate point to the current position of the target object must be collision-free, ensuring that the subsequent pursuit path can be quickly connected after the detour without affecting the pursuit efficiency. From the candidate points that simultaneously meet these three conditions, the one closest to the target object is selected as the optimal detour point. This can minimize the length of the pursuit path after the detour while ensuring safety, thereby improving the pursuit efficiency.

[0054] In the second scenario, when the path to the target object is blocked by an obstacle, it's necessary to plan a detour route based on the target object's dynamic motion state to avoid losing the target simply by bypassing the obstacle. The specific steps are: First, the agent's perception module collects historical motion data of the target object (including historical speed, heading angle, etc.). After filtering and denoising, a forward predicted path trajectory for the target object over a future period (e.g., 5-10 seconds, dynamically adjusted based on the target's speed) is generated based on a preset motion prediction model (e.g., Kalman filter model, particle filter model, etc.). Simultaneously, to handle the extreme case where the entire forward predicted path is obscured by obstacles, a reverse motion path trajectory (i.e., a reverse extension trajectory based on historical motion trends) is also generated as an alternative detour direction. Next, points on both the forward predicted path trajectory and the reverse movement path trajectory are sampled and then filtered according to preset effective waypoint selection criteria. These criteria include: 1. The sampled point itself is not within the safe avoidance range of any obstacle, ensuring the safety of the waypoint itself; 2. The sampled point must be located within a preset target area—an area defined by the target object's current position and a preset pursuit operation radius (e.g., 500 meters), ensuring that the target object can still be tracked after detour; 3. The planned path from the current position of the first-type agent to the sampled point must be collision-free, meaning the path will not conflict with any obstacles throughout. After filtering, the optimal detour point is selected from the effective waypoints of the forward predicted path trajectory that are closest to the current position of the first-type agent, prioritizing the continuity and efficiency of the pursuit; if no effective waypoint meets the criteria in the forward predicted path trajectory, the optimal detour point is selected from the effective waypoints of the reverse movement path trajectory that are closest to the current position of the first-type agent, ensuring safe avoidance and maintaining tracking of the target object even in extreme cases.

[0055] The following will combine Figure 6 and Figure 7 The specific implementation methods of the advance avoidance strategy of this application are described in detail.

[0056] like Figure 6As shown, this corresponds to step S261 in the advance avoidance strategy of this application, specifically the path collision prediction scenario for the first type of intelligent agent (i.e., the pursuing subject performing the pursuit task). In this embodiment, there is a maximum-sized obstacle (i.e., the main obstacle) on the initial straight-line pursuit path between the first type of intelligent agent and the target object (i.e., the escaping subject). By uniformly sampling and verifying the initial straight-line pursuit path, it can be seen that some sampling points on the straight-line path fall within the safe avoidance range of the core obstacle. The aforementioned safe avoidance range is a spatial area preset based on the physical contour size of the core obstacle and environmental safety requirements. Sampling points falling into this area indicates that there is a collision risk on the initial straight-line path, and subsequent detour waypoint planning steps need to be executed. Further combined with scene characteristics, it is determined that the core obstacle only blocks the initial straight-line pursuit path of the first type of intelligent agent, and the target object is not obscured by the obstacle. Therefore, the application scenario corresponding to this embodiment is a normal tracking scenario where "the target object is not blocked by the obstacle", and the following safe detour waypoint generation logic is applicable.

[0057] like Figure 7 The diagram illustrates the process of generating the optimal waypoint in a typical tracking scenario. The specific technical implementation is as follows: First, based on the inherent parameters of the core obstacle (including the three-dimensional coordinates of its center point and its own radius), a preset safety redundancy distance is superimposed (this redundancy distance is determined based on the motion mobility parameters of the first type of intelligent agent and the collision warning response threshold, used to ensure a buffer space for avoidance operations), thus constructing a safe avoidance zone for the core obstacle. Figure 7 The ring-shaped space area formed by the "obstacle body + green ring" is defined in the middle; secondly, based on the center point of the core obstacle, a circumscribed square of the aforementioned safety avoidance area is drawn. Figure 7 The green-bordered square), the four vertices of the circumscribed square ( Figure 7 The points marked with green pentagrams are the initial candidate detour points. Selecting the vertices of the circumscribed square as candidate points ensures that all candidate points are outside the safe avoidance zone and cover the main detour directions around the core obstacle, providing sufficient samples for subsequent optimal path selection. Next, based on the preset first filtering condition and the distance to the target object, the candidate point with the shortest straight-line distance to the target object (i.e., the point marked with a green pentagram) is selected from the candidate points that have passed the compliance verification. Figure 7 The green pentagram in the upper left corner is selected as the optimal detour waypoint. This selection logic can minimize the length of the pursuit path after the detour while ensuring the safety of the detour, thus ensuring the efficiency of the overall pursuit mission.

[0058] S27: Output the optimized tracking strategy of the first type of intelligent agent, and control the first type of intelligent agent to perform a pursuit operation on the target object according to the optimized tracking strategy.

[0059] S3: Switch the working state of the first type of intelligent agent from the pursuit mechanism to the idle guarding mechanism, and return to its corresponding partition; S4: Repeat steps S2 to S3 until all target objects have been captured or disposed of.

[0060] When the first type of agent completes its pursuit of the target object (e.g., successfully locks onto the target, or the target is handed over to the subsequent processing unit), or when the pursuit task termination trigger condition occurs (e.g., the target leaves the tracking range and exceeds the preset search time limit, or the system issues a task abort command), the system will switch the first type of agent's mechanism back to the idle standby mechanism. Simultaneously, the system will access the agent's information and locate its corresponding dedicated partition. Then, based on the relative position of the first type of agent's current location and its corresponding partition, combined with real-time environmental data, the system will generate the optimal return path using a dynamic path planning algorithm. During the first type of agent's return to its corresponding partition, it enters the set of selectable agents for the target object's reappearance. That is, the first type of agent may be selected to pursue other target objects during its return to its corresponding partition, or it may be selected to pursue other target objects after returning to its corresponding partition.

[0061] In some preferred embodiments of this application, the pursuit mechanism of the first type of intelligent agent corresponds to two types of controllers: a PID controller and an ADRC controller. When the wind and waves are small, the sea state is stable, and the model parameters do not change much, the PID controller can achieve good control results. When the wind and waves are large, the sea state is complex, the model parameters increase significantly, or there are unknown disturbances, the ADRC controller can estimate the system state and disturbances in real time through its extended state observer and perform compensation, thereby maintaining the system performance.

[0062]

Example

[0063] like Figure 9 The diagram shows the preset trajectories and initial motion logic design for the UAVs and USVs: The UAV's preset trajectory is a circular trajectory with a fixed radius centered on the center of the work area. This design allows the UAV to perform a uniform coverage scan of the work area, ensuring timely detection of target objects at different locations within the area. The USV's preset trajectory is a double-helix trajectory with the centers of the four quadrants of the work area as references. This trajectory design enables the USV to perform detailed searches of each quadrant area in a flexible and comprehensive manner, improving the detection capability of dispersed targets. Initially, all UAVs and USVs are evenly distributed along the left boundary of the work area, and the system controls each device based on A... The algorithm moves to its respective preset trajectory. After reaching the preset trajectory, the UAV and USV continue to move along their respective preset trajectories to maintain the area detection state.

[0064] like Figures 10-13 The diagram illustrates the entire pursuit and maneuver process between a USV and a target object, from detection to disposal. The core control logic is as follows: During the pursuit, the system calculates the straight-line distance between the USV and the target object in real time. If this distance exceeds the target object's detection radius (400m), the target object cannot perceive the USV's pursuit. In this case, the USV does not need to consider the target's countermeasures and directly determines the target object's real-time status (if the target is within the UAV / USV detection area, its position within a preset time frame is predicted based on its current position and velocity; if the target leaves the detection area, its current position is predicted based on the last detected position and velocity information). The system then uses A... The algorithm plans a path and moves towards the target object. If the distance is less than or equal to 400m, the USV enters the target object's perception area, and a two-player zero-sum game mechanism is initiated. The target object's payoff is the distance d between the two, and the USV's payoff is -d. The system uses a differential evolution algorithm to solve the Nash equilibrium solution of this game model, obtains the optimal pursuit strategy for the USV, and then plans the target waypoint to achieve accurate capture of the target object.

[0065] like Figures 14-17 The diagram illustrates the application process of the control algorithm for multi-USV collaborative pursuit of a target object: Upon the appearance of the target object, the system employs a PID-ADRC adaptive switching control algorithm to achieve collaborative control of four USVs. This algorithm dynamically switches controller types through a built-in disturbance perception mechanism: when external disturbances (such as waves, wind loads, etc.) are strong, the system automatically activates the more robust ADRC controller to enhance the ability to suppress disturbances; when external disturbances are weak and the environment is stable, the system switches to the faster-responding and more energy-efficient PID controller to improve the USV's navigation efficiency and control accuracy. During the actual pursuit process, the system fully considers the impact of external disturbances such as waves on USV navigation. The trajectory results show that the USVs can stably navigate along the reference desired trajectory, with only slight deviations occurring in a few short periods, and can quickly recover stability, fully demonstrating the control system's good adaptability and real-time response capability to complex disturbance environments.

[0066] like Figures 18-21 As shown, this is the dynamic change curve of the distance between the USV and the target object during the pursuit process; Figures 22-25 As shown, the curves represent the evolution of the deviation of the USV from the target object's desired course. These two sets of error curves intuitively reflect the control effect of the proposed control system on path tracking accuracy and course stability in actual pursuit missions: throughout the pursuit process, the course error remains stable, and the distance between the USV and the target object continues to decrease; even under extreme environmental disturbances such as strong winds and waves, the system significantly enhances the suppression effect on sudden disturbances through the PID-ADRC adaptive switching control algorithm, enabling the USV to maintain good path tracking performance and course maintenance capability under complex sea conditions, fully verifying the comprehensive technical advantages of the control algorithm in terms of anti-interference and stability.

[0067] The specific embodiments of this application have been described in detail above. For those skilled in the art, several improvements and modifications can be made to this application without departing from the principle of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for cooperative control of heterogeneous intelligent agents on an unfamiliar water surface, wherein the heterogeneous intelligent agents include a first type of intelligent agent and a second type of intelligent agent, characterized in that, Includes the following steps: S1: Divide the unfamiliar area into partitions according to the number of the first type of intelligent agents, assign a first type of intelligent agent to each partition and make it execute the idle guarding mechanism, while controlling the second type of intelligent agents to execute the circular patrol mechanism; S2: When a target object is detected in the partition corresponding to any first-type intelligent agent, the second-type intelligent agent, based on the real-time information of the target object, selects the first-type intelligent agent that is suitable for tracking the target object through a filtering strategy, and switches the working state of the first-type intelligent agent from the idle guarding mechanism to the pursuit mechanism until the first-type intelligent agent captures or disposes of the target object. The real-time information of the target object includes, but is not limited to, the target object's position coordinates, movement speed, and heading angle; S3: Switch the working state of the first type of intelligent agent from the pursuit mechanism to the idle guarding mechanism, and return to its corresponding partition; S4: Repeat steps S2 to S3 until all target objects have been captured or disposed of.

2. The heterogeneous intelligent agent cooperative control method for unfamiliar water surfaces according to claim 1, characterized in that: When the first type of intelligent agent is in the idle stationing mechanism, it uses a spiral search mode to conduct area detection; When multiple second-type intelligent agents execute the circular cruise mechanism, they adopt an anti-phase circular cruise cooperative mode.

3. The heterogeneous intelligent agent cooperative control method for unfamiliar water surfaces according to claim 1, characterized in that, In step S2, the screening strategy includes the following steps: S21: Collect the state information of all first-type intelligent agents in the idle guarding mechanism. The state information includes, but is not limited to, the real-time movement speed, remaining energy, payload capacity and straight-line distance relative to the target object of each first-type intelligent agent. S22: Based on the environmental constraints of the unfamiliar area, predict the potential escape route of the target object according to the real-time information of the target object. The environmental constraints of the unfamiliar area include, but are not limited to, the water flow velocity, wave level, and obstacle distribution within the area. S23: Based on the potential escape routes of the target object, select the first type of intelligent agent with the shortest expected arrival time on the potential escape route, and determine it as the intelligent agent to perform the target object tracking task.

4. The heterogeneous intelligent agent cooperative control method for unfamiliar water surfaces according to claim 1, characterized in that, In step S2, the strategy of the first type of agent, which switches to the pursuit mechanism, in the process of pursuing the target object is as follows: If the straight-line distance between the first type of intelligent agent and the target object is less than the perception radius of the target object, then the agent pursues the target object according to its potential escape route using a pursuit-escape game strategy; otherwise, the agent pursues the target object directly according to its potential escape route.

5. The heterogeneous intelligent agent cooperative control method for unfamiliar water surfaces according to claim 4, characterized in that, The execution process of the pursuit and escape game strategy is as follows: S24: Establish a two-player zero-sum game model of pursuit and escape, with the first type of intelligent agent switching to the pursuit mechanism as the pursuer and the target object as the escapee. S25: Solve the Nash equilibrium solution for a two-player zero-sum chase-escape game model based on the differential evolution algorithm to obtain the optimal strategy pair for both the chaser and the escapee; S26: Integrate the preset physical constraints into the optimal strategy to optimize the corresponding pursuit strategy. The physical constraints include, but are not limited to, the maximum speed limit, minimum turning radius limit, smooth steering requirements based on heading angular velocity control, and a collision avoidance strategy based on collision radius. S27: Output the optimized tracking strategy of the first type of intelligent agent, and control the first type of intelligent agent to perform a pursuit operation on the target object according to the optimized tracking strategy.

6. The heterogeneous intelligent agent cooperative control method for unfamiliar water surfaces according to claim 5, characterized in that, The execution steps of the advance avoidance strategy are as follows: S261: The straight path from the first type of intelligent agent to the target object is sampled evenly into several sampling points according to a preset sampling interval, and it is determined in turn whether each sampling point is within the safe avoidance range of the obstacle. If so, the first type of intelligent agent continues to pursue the target object according to its potential escape route. Otherwise, proceed to step S262; S262: Locate the first detected obstacle on the straight path as the primary obstacle, and generate a preferred waypoint that meets the requirements for safe detour based on the core parameters of the primary obstacle and the current path direction of the first type of agent.

7. The heterogeneous intelligent agent cooperative control method for unfamiliar water surfaces according to claim 6, characterized in that: In step S262, when the target object is not obscured by obstacles and only the straight line of pursuit by the first type of intelligent agent is blocked, the preferred waypoint is determined according to the normal tracking scenario; when the target object is directly obscured by obstacles and the first type of intelligent agent needs to bypass the obstacles while maintaining tracking of the target object, the preferred waypoint is determined according to the dynamic target tracking scenario.

8. The heterogeneous intelligent agent cooperative control method for unfamiliar water surfaces according to claim 1, characterized in that, Step S1 includes the following steps: S11: Perform initial partitioning of the unfamiliar region based on the number of the first type of intelligent agents; S12: Verify the feasibility of the initial partition center point relative to the avoidance area. If it is feasible, use the initial partition center point as the patrol origin and execute step S15; otherwise, execute step S13. S13: Extend the boundary of the initial partition outward by a first preset distance, and form uniform grid points in the extended partition area as candidate origins; S14: Eliminate candidate points that are less than the second preset distance from the avoidance area, and select the point that is closest to the initial partition center origin from the remaining candidate points as the patrol origin; S15: Assign a first-class agent to each partition and have it perform an idle guarding mechanism centered on the patrol origin, while controlling a second-class agent to perform a circular patrol mechanism.

9. The heterogeneous intelligent agent cooperative control method for unfamiliar water surfaces according to claim 1, characterized in that: The first type of intelligent agent is an unmanned surface vessel, and the second type of intelligent agent is an unmanned aerial vehicle (UAV).

Images