UAV swarm collaborative system based on ad hoc network

Abstract

This invention relates to a drone swarm collaborative system based on a self-organizing network, employing a star topology. In this star topology, a leader drone acts as the central node, directly communicating with subordinate drones to issue commands and collect data. All subordinate drones revolve around the leader drone to execute tasks, ensuring centralized management and efficient coordination. The leader drone, as the core node for communication and task command, is responsible for task allocation, path planning, and monitoring task progress. In this star topology, some subordinate drones use other nodes as relays to ensure that remote drones can receive task commands and feed data back to the leader drone. Multi-hop communication maintains connectivity within the swarm, ensuring communication connections and command transmission even in large task areas or when drones fly long distances. This ensures efficient collaborative operation of the drone swarm in complex environments.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) swarm control technology, and in particular to a UAV swarm collaborative system based on ad hoc networks. Background Technology

[0002] With the rapid development of drone technology, drone swarms have been widely used in logistics delivery, urban monitoring, and emergency rescue. However, multi-drone cooperative flight faces several key challenges in practical applications. First, the real-time nature of communication is a significant issue. Traditional communication methods suffer from latency in large-scale or complex environments, potentially leading to delays and inconsistencies in mission execution. Second, the efficiency of path planning faces challenges. Drone swarms need to dynamically adjust their paths based on mission requirements and environmental changes, and traditional distributed control models are ill-suited to complex path planning needs. Finally, obstacle avoidance in dynamic environments is also a major challenge. Drones may encounter dynamic obstacles or sudden environmental changes during flight, and existing systems often cannot respond effectively and quickly, affecting the successful execution of drone missions.

[0003] In existing technologies, distributed control modes, due to the lack of unified global management, are prone to delays in task command issuance, increased complexity in path planning, and incoordination in task execution. To address this, a centralized control-based UAV swarm management system is proposed, combining ad hoc networking technology to achieve efficient and reliable collaborative work of UAVs in dynamic and complex environments, possessing significant research value and practical application potential. Summary of the Invention

[0004] The purpose of this invention is to provide a drone swarm collaboration system based on a self-organizing network. This system enables a leader drone to centrally control subordinate drones, ensuring that the drone swarm can work together efficiently and reliably when performing tasks, and has dynamic obstacle avoidance, real-time task adjustment and multi-hop communication capabilities.

[0005] To achieve the above objectives, the present invention proposes the following technical solution:

[0006] A drone swarm collaboration system based on a self-organizing network is disclosed. The system adopts a star topology, in which a leader drone acts as the central node, issuing commands and collecting data from subordinate drones through direct communication. All subordinate drones revolve around the leader drone to execute tasks, ensuring centralized management and efficient coordination of tasks. The leader drone, as the core node for communication and task command, is responsible for allocating tasks, planning routes, and monitoring task progress.

[0007] In a star topology, some subordinate drones use other nodes as relays to ensure that remote drones can receive mission instructions and feed data back to the leader drone. Multi-hop communication is used to maintain connectivity within the cluster. The multi-hop communication mechanism ensures that communication connections and mission instructions can still be transmitted even when the mission area is large or the drones are flying far away.

[0008] Furthermore, when planning the path, CBS combined with an improved RRT* algorithm is used to generate conflict-free paths: First, the improved RRT* algorithm randomly samples on a plane at a specific altitude layer based on the UAV's starting point, ending point, and flight altitude mission requirements to generate a preliminary path, ensuring that the path planning complies with flight altitude restrictions; on this basis, the CBS algorithm performs dynamic conflict detection, by detecting the overlap points of each path's nodes with other UAV paths in real time. If a potential conflict is detected, the system automatically calls the improved RRT* algorithm to replan the conflicting path segments, ensuring conflict-free path execution.

[0009] Furthermore, the process of generating conflict-free paths by combining CBS with the improved RRT* algorithm is as follows:

[0010] S1: After setting the mission requirements (including parameters such as start point, destination, and flight altitude), the leader drone first uses the improved RRT* algorithm to generate a preliminary path.

[0011] The improved RRT* algorithm performs layered sampling in three-dimensional space, independently generating path points on the plane of each height layer and combining them to generate a complete path. The specific process is as follows:

[0012] 1) Define the sampling space and layered heights. The altitude range of the flight area is divided into several independent altitude layers Z1, Z2, ..., Zk. Path generation for each altitude layer is performed independently. For the k-th altitude layer, path points are sampled only in the X and Y planes, while Zk remains constant. The path generation formula is expressed as:

[0013]

[0014] Where Pathk represents the set of path points generated at the k-th height layer. This represents the waypoints generated on the altitude layer Zk, where Zk is the flight altitude of that layer, and n is the number of waypoints.

[0015] 2) After the sampling point sets in each height layer are generated, a complete 3D path is formed by merging path points from multiple height layers. The final path is defined as follows:

[0016]

[0017] Where K is the total number of height levels, Path finalIndicates the final path;

[0018] S2: Dynamic conflict detection of paths generated by the improved RRT* algorithm using the CBS algorithm: The CBS algorithm decomposes the multi-UAV path planning task into a constraint tree structure (conflicts during path finding). In the conflict detection phase, the system detects the overlap between the path points of each UAV and the path points of other UAVs in real time. When the node positions of two paths overlap at time t, a path conflict occurs, satisfying the following conditions:

[0019]

[0020] Where, Path p (t) and Path q (t) represent the sets of path points for the two drones, respectively.

[0021] If a conflict exists, the CBS algorithm will create a new node in the constraint tree structure and apply constraints to ensure that the conflicting paths do not overlap.

[0022] S3: When CBS detects a path conflict, the system automatically invokes the improved RRT* algorithm to regenerate the conflicting path segment in the constrained area. The improved RRT* algorithm prioritizes avoiding areas where conflicts have already occurred during sampling and searches for feasible paths under constraints. The path adjusted by the improved RRT* algorithm is defined as follows:

[0023]

[0024] Where, Path adjusted This represents the set of regenerated paths. This represents the adjusted path points, and n′ is the number of adjusted path points;

[0025] S4: The conflict-free path generated by combining the CBS algorithm with the improved RRT* algorithm will be used as the final path and will be assigned to each subordinate drone by the leader drone. Each drone will execute the flight according to the mission requirements and the assigned path, ensuring that there are no path conflicts and the flight altitude is controlled within the cluster.

[0026] Furthermore, the aforementioned UAV swarm collaborative system achieves efficient task planning and execution through a self-organizing network. The specific process is as follows:

[0027] S1: During mission initialization, the leader drone receives external mission requirements, performs mission analysis and path planning, and constructs a 3D map required for the mission based on environmental information of the mission area (such as terrain, obstacles, weather conditions, etc.) and plans the optimal flight route.

[0028] S2: After the path planning is completed, the leader drone distributes mission instructions to each subordinate drone through an ad hoc network. The mission instructions include flight routes, mission areas and mission execution order information. The leader drone ensures that the missions of all subordinate drones are reasonably allocated to avoid mission overlap or conflict.

[0029] S3: After the mission instructions are distributed, each subordinate UAV begins to execute the mission according to the received mission instructions. The subordinate UAV uses built-in sensors (such as lidar, infrared sensors, etc.) to detect the environment in real time and fly according to the flight path planned for the mission, while collecting data at the same time.

[0030] S4: During mission execution, each subordinate drone maintains real-time communication with the leader drone through an ad hoc network. The leader drone performs comprehensive analysis based on the received mission progress data. If the environment changes dynamically (such as the appearance of new obstacles or path obstruction), the leader drone will dynamically adjust the mission plan based on the new environmental information and issue new mission instructions in real time.

[0031] S5: During mission execution, if a subordinate drone is unable to continue performing the mission (e.g., due to communication failure, obstructed flight path, etc.), the system will automatically trigger the backup drone election mechanism, and another drone will take over the mission to ensure mission continuity. The backup election mechanism is completed automatically through the self-organizing network and does not affect the overall mission execution.

[0032] S6: After the mission is completed, all subordinate drones will transmit the collected data back to the leader drone through the self-organizing network. The leader drone will integrate and process the data, generate a mission report, and confirm the mission completion status. All drones will return to their initial positions or preset recovery points as instructed, and the mission will be successfully completed.

[0033] Furthermore, a real-time Dijkstra variant algorithm is used to enhance the dynamic optimization capability of the communication path and the adaptability of the system. This real-time Dijkstra variant algorithm dynamically adjusts the communication path weights by monitoring the status information of the UAV nodes in real time, including distance, battery level, and signal strength, to ensure the efficiency and stability of communication between UAV nodes in complex environments. The specific process is as follows:

[0034] In each communication cycle, the leader drone dynamically calculates the communication path weight W based on the status information of each subordinate drone. ij The communication cost from node i to node j is represented by the following formula:

[0035]

[0036] Where: d ij S represents the distance from node i to node j. The shorter the distance, the lower the path cost.j E represents the signal strength of node j; the higher the signal strength, the lower the path cost. j Let be the battery level of node j. The higher the battery level, the lower the path cost. α, β, and γ are dynamic weighting coefficients that reflect the relative importance of distance, signal strength, and battery level, respectively.

[0037] The node with the lowest communication cost is selected as the relay node. During the flight of the drone swarm, a communication path cost threshold T and a state threshold E are set. If the state of a relay node falls below the preset state threshold E, the system will automatically trigger a communication path update; or when W... ij When the communication path cost threshold T is reached, the real-time Dijkstra variant algorithm will recalculate the optimal communication path; after the communication path is updated, a new relay node will be selected so that the drone swarm can still maintain a stable communication link when the node status changes frequently.

[0038] To avoid overload caused by frequent use of certain relay nodes, a load balancing mechanism is integrated into the real-time Dijkstra variant algorithm, with each relay node having a load factor L. j The calculation is as follows:

[0039]

[0040] Wherein: F j C represents the current data transmission frequency of node j. j The communication capability of node j is proportional to the signal strength and power.

[0041] When the node's load factor L j When the load exceeds the preset load threshold Lth, the system will exclude the node from the relay node candidate list and prioritize the node with the lower load for data transmission.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The system of this invention adopts a star topology structure, which enables centralized control of subordinate drones by a leader drone, ensuring efficient collaborative operation of the drone swarm in complex environments, and has self-organizing network function, dynamic obstacle avoidance and real-time communication capabilities.

[0044] This invention's system can dynamically adjust the communication path based on the UAV's position, communication link quality, and mission requirements during flight. When the UAV is flying in complex environments or long-distance mission areas, adaptive routing can intelligently select the optimal communication path, ensuring that data and commands are transmitted with minimal latency. This function is particularly suitable for large-scale mission scenarios with complex terrain. By dynamically optimizing the communication path, it ensures that mission commands from the UAV swarm can be transmitted in real time, improving mission execution stability and overall communication efficiency.

[0045] The system of this invention supports multi-hop communication and relay functions. Through self-organizing network technology, subordinate drones in the cluster can act as communication relay nodes to help transmit data and instructions from other remote drones. This ensures that even if the drones are outside the direct communication range of the leader drone, the drones in the cluster can still maintain a stable communication connection. It can ensure that mission instructions are efficiently transmitted in a large mission area, while adapting to various complex terrains and environments, and expanding the communication coverage of the drone cluster. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the functional structure of each module of the leader drone in the drone swarm collaboration system of this invention.

[0047] Figure 2 This is a schematic diagram of the functional structure of each module of the subordinate drone in the drone swarm collaboration system of the present invention.

[0048] Figure 3 This is a schematic diagram of the star topology of the UAV swarm collaborative system of the present invention.

[0049] Figure 4 This is a schematic diagram of the process for planning and executing drone swarm tasks based on ad hoc networks, as described in this invention. Detailed Implementation

[0050] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, but this is not intended to limit the scope of protection of this application.

[0051] This invention relates to a self-organizing network-based UAV swarm collaborative system, employing a star topology. Subordinate UAVs act as nodes executing specific tasks, while the leader UAV serves as the core node of the swarm control system, undertaking functions such as global task planning, environmental perception, obstacle avoidance, and communication coordination. Within the swarm, the leader UAV remains relatively fixed, directly controlling the subordinate UAVs without requiring communication with the ground. In the event of a leader UAV failure, a new leader UAV can be elected.

[0052] In this invention, the path planning section of the task planning and management module provides 3D map support for path planning through the environmental perception and mapping module. The lead UAV first uses multi-source sensor data (such as LiDAR, visual sensors, etc.) to perceive the environmental information of the task area in real time, constructing a 3D map including terrain, buildings, and obstacles, and then transmits the map data to the task planning and management module. Based on the map constructed from environmental perception, the path planning section uses CBS (Conflict Basis Search) combined with an improved RRT* algorithm to generate conflict-free paths. First, the improved RRT* algorithm randomly samples on a plane at a specific altitude layer based on the UAV's start point, end point, and flight altitude mission requirements to generate preliminary paths, ensuring that the path planning complies with flight altitude restrictions. On this basis, the CBS algorithm performs dynamic conflict detection, by detecting the overlap points of each path's nodes with other UAV paths in real time. If a potential conflict is detected, the system automatically calls the improved RRT* algorithm to replan the conflicting path segments, ensuring conflict-free path execution.

[0053] The star topology uses a leader drone as the central node, communicating directly with subordinate drones for command issuance and data collection. All subordinate drones revolve around the leader drone to execute tasks, ensuring centralized management and efficient coordination. The leader drone, as the core node for communication and mission command, is responsible for task allocation, path planning, and monitoring mission progress. Through the star topology, the leader drone can maintain direct communication with each subordinate drone, ensuring accurate transmission of mission commands. The multi-hop communication and relay mechanism, within the star topology, allows some subordinate drones to use other nodes as relays, ensuring that remote drones can receive mission commands and feed data back to the leader drone. This multi-hop communication mechanism effectively extends the system's communication coverage, ensuring that communication connections and mission command transmission are maintained even in large mission areas or when drones fly long distances.

[0054] Specifically,

[0055] Leader drones, such as Figure 1 As shown, the leader drone is divided into a mission planning and management module, an environmental perception and map building module, a dynamic obstacle avoidance and flight control module, and a communication and coordination module.

[0056] The mission planning and management module performs path planning based on mission requirements, sets mission objectives, flight routes and execution sequences for subordinate drones, and monitors the mission execution progress and dynamic changes of subordinate drones in real time. Based on dynamic changes (such as environmental changes or subordinate drone malfunctions), it reallocates tasks or optimizes paths, and stores mission-related data for subsequent analysis and optimization of mission execution.

[0057] The environmental perception and map building module uses multiple sensors such as LiDAR, visual cameras, and infrared sensors to perceive environmental information such as obstacles and terrain changes in the task area, collect and integrate environmental data to build a three-dimensional dynamic map, and update the map information in real time to adapt to complex task environments.

[0058] The dynamic obstacle avoidance and flight control module detects dynamic obstacles in the mission area in real time, adjusts the UAV's flight path based on environmental perception data, and dynamically adjusts the UAV's flight altitude according to mission requirements and environmental changes to ensure the UAV's flight safety and mission execution stability.

[0059] The communication and coordination module manages the communication routes of the drone swarm, employs multi-hop communication to ensure smooth communication among all drones in the swarm, and ensures that mission commands and data can be smoothly transmitted between drones through the self-organizing network function. Especially when the direct communication range is exceeded, other subordinate drones can act as relay nodes to maintain the connectivity of the communication link.

[0060] Subordinate drones, such as Figure 2 As shown, the subordinate UAV is divided into a mission execution module, a dynamic obstacle avoidance and flight control module, and a communication and relay module.

[0061] The mission execution module receives and parses the mission instructions sent by the leader drone, executes the mission according to the planned path, including the flight path, target point, mission parameters, etc., flies along the set route according to the mission instructions, and provides status feedback during the mission.

[0062] The dynamic obstacle avoidance and flight control module detects local obstacles during flight and performs obstacle avoidance operations to ensure the safe flight of the drone in complex environments. It also adjusts the flight altitude, speed, and direction in a timely manner to ensure stable flight and to follow the path planning of the leader drone.

[0063] The communication and relay module is responsible for receiving mission instructions from the leader drone, and providing feedback on flight status, mission progress, and collected environmental data. It also acts as a communication relay between the leader drone and other subordinate drones to ensure smooth communication links within the system.

[0064] CBS combines an improved RRT* algorithm to generate conflict-free multi-UAV paths, ensuring efficient and safe flight of UAV swarms in complex environments. The specific process is as follows:

[0065] S1: After setting the mission requirements (including parameters such as start point, destination, and flight altitude), the leader drone first uses the improved RRT* algorithm to generate a preliminary path.

[0066] The improved RRT* algorithm performs layered sampling in three-dimensional space, independently generating path points on the plane of each height layer and combining them to generate a complete path. The specific process is as follows:

[0067] 1) Define the sampling space and layered heights. The altitude range of the flight area is divided into several independent altitude layers Z1, Z2, ..., Zk. Path generation for each altitude layer is performed independently. For the k-th altitude layer, path points are sampled only in the X and Y planes, while Zk remains constant. The path generation formula is expressed as:

[0068]

[0069] Where Pathk represents the set of path points generated at the k-th height layer. This represents the waypoints generated on the altitude layer Zk, where Zk is the flight altitude of that layer, and n is the number of waypoints.

[0070] 2) After the sampling point sets in each height layer are generated, a complete 3D path is formed by merging path points from multiple height layers. The final path is defined as follows:

[0071]

[0072] Where K is the total number of height levels, Path final Indicates the final path;

[0073] S2: Dynamic conflict detection of paths generated by the improved RRT* algorithm using the CBS algorithm: The CBS algorithm decomposes the multi-UAV path planning task into a constraint tree structure (conflicts during path finding). In the conflict detection phase, the system detects the overlap between the path points of each UAV and the path points of other UAVs in real time. When the node positions of two paths overlap at time t, a path conflict occurs, satisfying the following conditions:

[0074]

[0075] Where, Path p (t) and Path q (t) represent the sets of path points for the two drones, respectively.

[0076] If a conflict exists, the CBS algorithm will create a new node in the constraint tree structure and apply constraints to ensure that the conflicting paths do not overlap.

[0077] S3: When CBS detects a path conflict, the system automatically invokes the improved RRT* algorithm to regenerate the conflicting path segment in the constrained area. The improved RRT* algorithm prioritizes avoiding areas where conflicts have already occurred during sampling and searches for feasible paths under constraints. The path adjusted by the improved RRT* algorithm is defined as follows:

[0078]

[0079] Where, Path adjusted This represents the set of regenerated paths. This represents the adjusted path points, and n′ is the number of adjusted path points;

[0080] S4: The conflict-free path generated by combining the CBS algorithm with the improved RRT* algorithm will be used as the final path and will be assigned to each subordinate drone by the leader drone. Each drone will execute the flight according to the mission requirements and the assigned path, ensuring that there are no path conflicts and the flight altitude is controlled within the cluster.

[0081] This invention provides a method for generating conflict-free paths by combining CBS with an improved RRT* algorithm. This method can dynamically adjust and ensure the real-time resolution of path conflicts, thereby improving the flexibility of path planning and the safety of UAVs. Each UAV executes flight according to mission requirements and assigned paths, ensuring conflict-free paths and controlled flight altitudes within the cluster.

[0082] The improved RRT* algorithm in this invention ensures efficient path planning for UAVs in complex environments. It optimizes path generation and obstacle avoidance capabilities by performing random sampling at different altitude levels. This method performs layered sampling in three-dimensional space, independently generating path points on the plane of each altitude level and combining them to generate a complete path. The final path contains path points from different altitude levels, forming a conflict-free and smooth three-dimensional path to meet the path requirements of UAVs in complex environments.

[0083] Star topology, such as Figure 3As shown, the leader drone acts as the central node, maintaining direct communication with all subordinate drones to ensure centralized task management and efficient coordination. In the star topology, all subordinate drones revolve around the leader drone to execute tasks, ensuring accurate transmission of task instructions and real-time data feedback. When some subordinate drones exceed the direct communication range of the leader drone, the system expands the communication coverage through a multi-hop communication mechanism to ensure that remote drones maintain communication connections. To further improve the system's communication efficiency and adaptability in complex dynamic environments, this system preferably introduces a real-time Dijkstra variant algorithm. Based on the star topology and multi-hop communication, it achieves more efficient communication management through dynamic path optimization and load balancing. The real-time Dijkstra variant algorithm optimizes the communication path in real time based on the status information of each node (such as distance, signal strength, and battery level), prioritizing nodes with high signal strength and sufficient battery power as relays. When the status of a relay node changes (such as insufficient battery power or weakened signal), the system automatically selects a new relay node to ensure the continuity and stability of data transmission. Meanwhile, to prevent individual relay nodes from overloading, the system dynamically allocates data traffic through a load balancing mechanism, prioritizing nodes with lower loads as relays. This effectively balances the communication load, extends the drone's flight time, and improves the overall efficiency of cluster communication.

[0084] Preferably, a real-time Dijkstra variant algorithm is used to enhance the dynamic optimization capability of the communication path and the adaptability of the system. This real-time Dijkstra variant algorithm dynamically adjusts the communication path weights by monitoring the status of UAV nodes in real time (including distance, battery level, signal strength, etc.) to ensure the efficiency and stability of communication between UAV nodes in complex environments. The specific process is as follows:

[0085] In each communication cycle, the leader drone dynamically calculates the communication path weight W based on the status information of each subordinate drone (including battery level, signal strength, and distance). ij The communication cost from node i to node j is represented by the following formula:

[0086]

[0087] Where: d ij S represents the distance from node i to node j. The shorter the distance, the lower the path cost. j E represents the signal strength of node j; the higher the signal strength, the lower the path cost. j Let be the battery level of node j. The higher the battery level, the lower the path cost. α, β, and γ are dynamic weighting coefficients that reflect the relative importance of distance, signal strength, and battery level, respectively.

[0088] The node with the lowest communication cost is selected as the relay node. During the flight of the drone swarm, a communication path cost threshold T and a state threshold E are set. If the state (such as battery level or signal strength) of a relay node falls below the preset state threshold E, the system will automatically trigger a communication path update and recalculate the communication cost; or when W... ij When the communication path cost threshold T is reached, the real-time Dijkstra variant algorithm will recalculate the optimal communication path and select a new relay node, so that the drone swarm can still maintain a stable communication link when the node status changes frequently.

[0089] To avoid overload caused by frequent use of certain relay nodes, a load balancing mechanism is integrated into the real-time Dijkstra variant algorithm, with each relay node having a load factor L. j The calculation is as follows:

[0090]

[0091] Wherein: F j C represents the current data transmission frequency of node j. j The communication capability of node j is proportional to the signal strength and power.

[0092] When the node's load factor L j When the load exceeds the preset load threshold Lth, the system will exclude the node from the relay node candidate list and prioritize the node with the lower load for data transmission.

[0093] The communication method of this invention helps to balance the communication load, extend the flight time of the UAV, and improve the overall operating efficiency of the system.

[0094] Preferably, during the execution of a mission by a drone swarm, if the leader drone is unable to continue operating due to power depletion, equipment failure, or other unforeseen reasons, this system has the function of automatically electing a backup leader. When the leader drone fails, the system will automatically elect a capable slave drone as the new leader based on the status feedback from slave drones (including parameters such as power level, communication quality, and mission progress) to take over command of the mission. The backup leader election mechanism ensures mission continuity, prevents the drone swarm from stalling when the leader fails, and effectively improves the system's fault tolerance and the reliability of mission completion.

[0095] The UAV swarm system of this invention achieves efficient task planning and execution through a self-organizing network. The specific process is as follows:

[0096] S1: During mission initialization, the leader drone receives external mission requirements and performs mission analysis and path planning through its mission planning and management module. Based on environmental information of the mission area (such as terrain, obstacles, weather conditions, etc.), the leader drone constructs the required 3D map for the mission and plans the optimal flight route.

[0097] S2: After path planning is complete, the leader drone distributes mission instructions to all subordinate drones via an ad hoc network. Mission instructions include information such as flight routes, mission areas, and mission execution order. The leader drone ensures that all subordinate drones are assigned tasks appropriately, avoiding mission overlap or conflict.

[0098] S3: After the mission instructions are distributed, each subordinate UAV begins to execute the mission according to the received instructions. They use built-in sensors (such as lidar, infrared sensors, etc.) to detect the environment in real time, fly according to the flight path planned for the mission, and collect data at the same time.

[0099] S4: During mission execution, each subordinate drone maintains real-time communication with the leader drone through an ad hoc network. The leader drone performs comprehensive analysis based on the received mission progress data. If the environment changes dynamically (such as the appearance of new obstacles or path obstruction), the leader drone will dynamically adjust the mission plan based on the new environmental information and issue new mission instructions in real time.

[0100] S5: During mission execution, if a subordinate UAV is unable to continue performing the mission (e.g., due to communication failure, obstructed flight path), the system will automatically trigger a backup UAV election mechanism, allowing another UAV to take over the mission and ensure mission continuity. The backup election mechanism is completed automatically through the self-organizing network and does not affect the overall mission execution.

[0101] S6: Upon completion of the mission, all subordinate drones will transmit the collected data back to the leader drone via an ad-hoc network. The leader drone will integrate and process the data, generate a mission report, and confirm the mission completion status. All drones will then return to their initial positions or preset recovery points as instructed, and the mission will be successfully completed.

[0102] Any aspects not covered in this invention are applicable to existing technologies.

Claims

1. A drone swarm collaborative system based on ad hoc networks, characterized in that, The system adopts a star topology, in which the leader drone serves as the central node, and commands and data are issued to subordinate drones through direct communication. All subordinate drones revolve around the leader drone to execute missions, ensuring centralized management and efficient coordination of missions; The leader drone serves as the core node for communication and mission command, responsible for allocating tasks, planning routes, and monitoring mission progress. In a star topology, some subordinate drones use other nodes as relays to ensure that remote drones can receive mission instructions and feed data back to the leader drone, maintaining connectivity within the cluster through multi-hop communication; When planning routes, CBS combined with an improved RRT* algorithm is used to generate conflict-free paths. The process of generating conflict-free paths using CBS combined with the improved RRT* algorithm is as follows: S1: After setting the mission requirements, the leader drone first uses the improved RRT* algorithm to generate a preliminary path. The improved RRT* algorithm performs layered sampling in three-dimensional space, independently generating path points on the plane of each height layer and combining them to generate a complete path. The specific process is as follows: 1) Define the sampling space and layered heights. The altitude range of the flight area is divided into several independent altitude layers Z1, Z2, ..., Zk. Path generation for each altitude layer is performed independently. For the k-th altitude layer, path points are sampled only in the X and Y planes, while Zk remains constant. The path generation formula is expressed as: Path k ={(x i ,y i ,Z k )|i=1,2,...,n} Where Pathk represents the set of path points generated at the k-th height layer, (x i y i ) represents a waypoint generated on altitude layer Zk, where Zk is the flight altitude of that layer, and n is the number of waypoints; 2) After the sampling point sets in each height layer are generated, a complete 3D path is formed by merging path points from multiple height layers. The final path is defined as follows: Where K is the total number of height levels, Path final Indicates the final path; S2: Dynamic conflict detection of paths generated by the improved RRT* algorithm using the CBS algorithm: The CBS algorithm decomposes the multi-UAV path planning task into a constraint tree structure. In the conflict detection phase, the system detects the overlap between the path points of each UAV and the path points of other UAVs in real time. When the node positions of two paths overlap at time t, a path conflict occurs, satisfying the following conditions: Where, Path p (t) and Path q (t) represent the sets of path points for the two drones, respectively. If a conflict exists, the CBS algorithm will create a new node in the constraint tree structure and apply constraints to ensure that the conflicting paths do not overlap. S3: When CBS detects a path conflict, the system automatically invokes the improved RRT* algorithm to regenerate the conflicting path segment in the constrained area. The improved RRT* algorithm prioritizes avoiding areas where conflicts have already occurred during sampling and searches for feasible paths under constraints. The path adjusted by the improved RRT* algorithm is defined as follows: Path adjusted ={(x′ i ,y′ i ,Z k )|i=1,2,...,n′} Where, Path adjusted Represents the regenerated set of paths, (x′ i y′ i ) represents the adjusted path points, and n′ is the number of adjusted path points; S4: The conflict-free path generated by combining the CBS algorithm with the improved RRT* algorithm will be used as the final path and will be assigned to each subordinate drone by the leader drone. Each drone will execute the flight according to the mission requirements and the assigned path, ensuring that there are no path conflicts and the flight altitude is controlled within the cluster.

2. The UAV swarm collaborative system based on ad hoc networks according to claim 1, characterized in that, The aforementioned drone swarm collaborative system achieves efficient task planning and execution through a self-organizing network. The specific process is as follows: S1: During mission initialization, the leader drone receives external mission requirements, performs mission analysis and path planning, and constructs a 3D map required for the mission based on the environmental information of the mission area, and plans the optimal flight route. S2: After the path planning is completed, the leader drone distributes mission instructions to each subordinate drone through an ad hoc network. The mission instructions include flight routes, mission areas and mission execution order information. The leader drone ensures that the missions of all subordinate drones are reasonably allocated to avoid mission overlap or conflict. S3: After the mission instructions are distributed, each subordinate drone begins to execute the mission according to the received mission instructions. The subordinate drones use built-in sensors to detect the environment in real time and fly according to the flight path planned for the mission, while collecting data at the same time. S4: During mission execution, each subordinate drone maintains real-time communication with the leader drone through an ad hoc network. The leader drone performs comprehensive analysis based on the received mission progress data. If the environment changes dynamically, the leader drone will dynamically adjust the mission plan based on the new environmental information and issue new mission instructions in real time. S5: During mission execution, if a subordinate drone is unable to continue performing the mission, the system will automatically trigger the backup drone election mechanism, and another drone will take over the mission to ensure mission continuity. The backup election mechanism is completed automatically through the self-organizing network and does not affect the overall mission execution. S6: After the mission is completed, all subordinate drones will transmit the collected data back to the leader drone through the self-organizing network. The leader drone will integrate and process the data, generate a mission report, and confirm the mission completion status. All drones will return to their initial positions or preset recovery points as instructed, and the mission will be successfully completed.

3. The UAV swarm collaborative system based on ad hoc networks according to claim 1, characterized in that, The real-time Dijkstra variant algorithm enhances the dynamic optimization capability of communication paths and the adaptability of the system. By monitoring the status information of UAV nodes in real time, including distance, battery level, and signal strength, the real-time Dijkstra variant algorithm dynamically adjusts the communication path weights to ensure the efficiency and stability of communication between UAV nodes in complex environments. The specific process is as follows: In each communication cycle, the leader drone dynamically calculates the communication path weight W based on the status information of each subordinate drone. ij The communication cost from node i to node j is represented by the following formula: Where: d ij S represents the distance from node i to node j. The shorter the distance, the lower the path cost. j E represents the signal strength of node j; the higher the signal strength, the lower the path cost. j Let be the battery level of node j. The higher the battery level, the lower the path cost. α, β, and γ are dynamic weighting coefficients that reflect the relative importance of distance, signal strength, and battery level, respectively. The node with the lowest communication cost is selected as the relay node. During the flight of the drone swarm, a communication path cost threshold T and a state threshold E are set. If the state of a relay node falls below the preset state threshold E, the system will automatically trigger a communication path update; or when W... ij When the communication path cost threshold T is reached, the real-time Dijkstra variant algorithm will recalculate the optimal communication path; after the communication path is updated, a new relay node will be selected so that the drone swarm can still maintain a stable communication link when the node status changes frequently. To avoid overload caused by frequent use of certain relay nodes, a load balancing mechanism is integrated into the real-time Dijkstra variant algorithm, with each relay node having a load factor L. j The calculation is as follows: Wherein: F j C represents the current data transmission frequency of node j. j The communication capability of node j is proportional to the signal strength and power. When the node's load factor L j When the load exceeds the preset load threshold Lth, the system will exclude the node from the relay node candidate list and prioritize the node with a lower load for data transmission.

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