Multi-uav rope-driven parallel cooperation system

Abstract

The embodiment of the application discloses a multi-unmanned aerial vehicle rope driving parallel cooperation system. A magnetic attraction claw anchoring module is designed based on the principle of bionics, so that the claws of the maintenance robot or unmanned aerial vehicle can be stably adhered to the surface of the steel structure. Through the research on the interactive mechanism of controllable adhesion magnetic attraction force and the surface of the steel structure, the ability of the operation carrier is improved by combining the multi-machine cooperation pulling technology. The climbing gait and anchor point configuration strategy are optimized, and the system stability and operation efficiency are significantly enhanced. Finally, an integrated system is constructed by integrating the climbing maneuvering body, the magnetic attraction claw and the lightweight maintenance tool, and the efficient detection and heavy load maintenance functions of the complex steel structure surface are realized. The rope is replaced by a net-shaped rope and other deformed pulling ropes, and the system is also applicable to tracking and trapping "black flight" unmanned aerial vehicles, small aircrafts or ground moving targets.

Description

[0001] Statement: This patent application belongs to the intellectual property rights of the Guangdong Provincial Department of Science and Technology, Guangdong Provincial Key Research and Development Program Project, Project No.: 2025B0909040003, Project Name: Research and Application of Magnetic Adsorption Climbing and Intelligent Detection Robot for Complex Spatial Steel Structures. Technical Field

[0002] This application relates to the field of equipment control, specifically to a multi-UAV rope-driven parallel cooperative system. Background Technology

[0003] The multi-UAV rope-driven parallel collaborative system and control method are particularly suitable for the maintenance of large steel structures. In traditional steel structure maintenance, it is usually necessary to manually climb to high places to carry out the work, which not only poses safety risks but is also inefficient.

[0004] To overcome these problems, researchers began exploring the possibility of using drone technology for steel structure maintenance. However, single drones or maintenance equipment have limitations in terms of operational capacity, stability, and operating range, making it difficult to meet the complex and ever-changing maintenance needs of large steel structures. Therefore, existing maintenance methods still suffer from bottlenecks such as poor adaptability to multiple scenarios, insufficient dynamic trajectory optimization, and weak multimodal detection fusion. Utility Model Content

[0005] This application provides a multi-UAV rope-driven parallel cooperative system, which realizes multi-modal maintenance tasks of the robot system through multi-UAV cooperative rope-driven parallel control technology and variable configuration multi-UAV formation control technology.

[0006] The first aspect of this application provides a multi-UAV rope-driven parallel cooperative system, including: a maintenance robot, multiple anchored traction UAVs, and a traction and guidance system connecting the maintenance robot and each of the anchored traction UAVs;

[0007] The maintenance robot is equipped with maintenance equipment, a winch, and a first magnetic claw anchoring module.

[0008] Each of the aforementioned anchoring and traction UAVs is equipped with a second magnetic grappling hook anchoring module;

[0009] One end of the traction and guiding system is wound around the winch of the maintenance robot, and the other end is connected to the anchoring and traction drone.

[0010] The system also includes a control unit connected to the anchoring and towing drone to control the flight status of the anchoring and towing drone;

[0011] The control unit is also connected to the winch to control the winch to tighten or release the traction rope of the traction guide system.

[0012] The second aspect of this application provides a multi-UAV rope-driven parallel cooperative control method, which is applied to a multi-UAV rope-driven parallel cooperative system, the system including a maintenance robot and multiple UAVs connected to the maintenance robot.

[0013] The method includes:

[0014] When a mission instruction is received, if the mission instruction indicates that a flight operation should be performed, then the system is determined to enter the mobile base cable driven parallel robot mode. The mobile base cable driven parallel robot mode is used to indicate that the maintenance robot lands at the target position under the flight traction of the multiple UAVs.

[0015] In the mobile base-rope driven parallel robot mode, based on the communication topology and mass dynamics model among the multiple UAVs, a distributed cooperative control algorithm is used to control the multiple UAVs to establish and maintain a preset geometric formation for flight.

[0016] The multiple drones positioned in the preset geometric formation are controlled to jointly pull the maintenance robot so that the maintenance robot is positioned at the geometric center of the preset geometric formation;

[0017] If the task instruction indicates to perform climbing maintenance work, then the system is determined to enter the fixed base rope-driven parallel robot mode. The fixed base rope-driven parallel robot mode is used to indicate that when the multiple drones are anchored at the target position, the maintenance robot climbs to the maintenance position based on the pull of the multiple drones to perform maintenance.

[0018] In the fixed-base rope-driven parallel robot mode, a quadratic programming problem is constructed based on the nonlinear dynamic model of the maintenance robot, with tracking error and control input increment as optimization objectives;

[0019] The quadratic programming problem is solved to determine the optimal rope tension, and the rope drive mechanism is controlled based on the optimal rope tension to perform trajectory tracking control on the maintenance robot.

[0020] A third aspect of this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method of the first aspect described above.

[0021] A fourth aspect of this application provides a computer storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described in the first aspect.

[0022] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0023] Based on biomimetic principles, a magnetic grappling hook anchoring module was designed to enable the grappling hooks of maintenance robots or drones to stably adhere to steel structure surfaces. Through research on the interaction mechanism between controllable magnetic adhesion and the steel structure surface, and combined with multi-machine collaborative traction technology, load-bearing capacity was improved. Optimization of climbing gait and anchor point configuration strategies significantly enhanced system stability and operational efficiency. Ultimately, an integrated system was constructed, combining a climbing motor, magnetic grappling hooks, and lightweight maintenance tools, enabling high-efficiency inspection and heavy-duty maintenance of complex steel structure surfaces. Attached Figure Description

[0024] Figure 1 This is an exemplary structural diagram of a multi-UAV rope-driven parallel cooperative system in the embodiments of this application;

[0025] Figure 2 This is an exemplary structural diagram of a multi-UAV rope-driven parallel cooperative system in the single-UAV climbing operation mode in this application embodiment;

[0026] Figure 3 This is a schematic diagram illustrating an exemplary scenario of single-machine mode and multi-machine cooperative mode climbing maintenance in the embodiments of this application;

[0027] Figure 4 This is a flowchart illustrating the multi-UAV tethered parallel cooperative control method in the embodiments of this application;

[0028] Figure 5 This is an exemplary coordinate diagram of an aircraft coordinate system in an embodiment of this application;

[0029] Figure 6 This is an exemplary schematic diagram illustrating the flight trajectory of a formation of multiple drones in an embodiment of this application.

[0030] Figure 7 This is a schematic diagram of a computer device in an embodiment of this application. Detailed Implementation

[0031] This application provides a multi-UAV rope-driven parallel cooperative system, which realizes multi-modal maintenance tasks of the robot system through multi-UAV cooperative rope-driven parallel control technology and variable configuration multi-UAV formation control technology.

[0032] Complex spatial steel structures, such as large power grid towers, wind turbines, bridges, railway stations, and stadiums, are characterized by their enormous scale and complex structure. Their physical dimensions are enormous (e.g., bridge spans can reach several kilometers, and tower heights can be hundreds of meters), and their internal structures often include dense steel trusses, suspension systems, irregular curved surfaces (such as stadium roofs), and dense pipelines (stations), forming intricate spatial layouts. Furthermore, these complex spatial structures often contain multi-scale and multi-type surface features and potential defects, such as corrosion, cracks, coating peeling, loose bolts, ice and snow accumulation, vegetation encroachment (towers), and localized deformation, necessitating inspection and maintenance.

[0033] Existing inspection methods mainly include traditional manual inspection, drone-based rough inspection, and climbing robot-based detailed inspection. Traditional manual inspection typically uses crane assistance or a zipline-borne manned basket, which can carry a magnetic flux meter or X-ray imaging instrument for internal cable inspection and repair. However, the basket is prone to damaging the protective layer and suffers from drawbacks such as low efficiency, numerous blind spots, poor accessibility, and high inspection risks. Drone rough inspection, through remote wireless control, acquires video images of complex spatial structures, offering advantages such as high efficiency, low cost, and non-destructive operation. However, its limited payload restricts it to rough inspection only, not repair. While a single climbing robot can complete the inspection task, it is prone to damaging the structural surface and has limited heavy-duty capacity and obstacle-crossing ability. Therefore, existing methods still suffer from bottlenecks such as poor adaptability to multiple scenarios, insufficient dynamic trajectory optimization, and weak multimodal detection fusion.

[0034] With the continuous advancement of intelligent sensing and autonomous control technologies, unmanned aerial vehicles (UAVs) have demonstrated broad application potential in industrial inspection and maintenance operations due to their flexible deployment, high mobility, and excellent scalability. However, single UAVs are limited by their load capacity, endurance, spatial coverage, and anti-interference stability, making it difficult to meet the comprehensive demands of high-precision, long-duration, and multi-task operations in complex structural environments. To address this, multi-UAV collaborative swarm technology has emerged. Through task division and collaborative control, it exhibits significant advantages in system efficiency, spatial coverage, and task robustness, not only improving operational capabilities in complex environments but also effectively overcoming the limitations of single-UAV operations. Especially in scenarios such as bridges and towers where access is difficult or operation time is limited, aerial mobile platforms built based on multi-UAV swarms can replace traditional hoisting or scaffolding operations, reducing construction interference and significantly improving operational efficiency. Therefore, research on collaborative control strategies for UAV swarms, providing technical support for the construction and autonomous operation of mobile operation platforms, has significant engineering value and application prospects.

[0035] Rope-driven parallel robot systems are characterized by using flexible ropes to control a rigid moving platform to achieve the control objectives of the entire work group. Compared with traditional parallel systems, they offer advantages such as a larger workspace, lower inertia, higher payload capacity, and high-speed movement. An inherent property of ropes is that they can only provide tension, not thrust, thus requiring constraints on the controller output, which presents a challenge for controller design in such systems. The reconfiguration of rope-driven parallel robot systems can be achieved through geometric reconfiguration of the ropes and kinematic reconfiguration of the mobile base. Geometric reconfiguration of the ropes can expand the workspace; this type of reconfigurable rope-driven parallel robot is called a classic rope-driven parallel robot. Another reconfigurable method is through a mobile base. Installing the rope-driven parallel robot system on various types of mobile bases creates various types of mobile rope-driven parallel robots. These robots are characterized by mobility, deployability, and automatic adjustment according to task requirements.

[0036] This application integrates the collaborative control of UAV formations and the trajectory tracking control technology of rope-driven parallel robots to develop a multi-UAV rope-driven parallel cooperative system. It can effectively utilize the advantages of these two technologies and make up for the shortcomings of a single UAV and a single rope-driven parallel robot system. It has the advantages of being mobile, deployable, automatically adjustable according to task requirements, improving system efficiency, spatial coverage and task robustness.

[0037] Please see Figure 1 The multi-UAV rope-driven parallel cooperative system in this application embodiment includes:

[0038] The maintenance robot, multiple anchoring and traction drones, and a traction and guidance system connecting the maintenance robot and each of the anchoring and traction drones;

[0039] The maintenance robot is equipped with maintenance equipment, a winch, and a first magnetic claw anchoring module.

[0040] Each of the aforementioned anchoring and traction UAVs is equipped with a second magnetic grappling hook anchoring module;

[0041] One end of the traction and guiding system is wound around the winch of the maintenance robot, and the other end is connected to the anchoring and traction drone.

[0042] The system also includes a control unit connected to the anchoring and towing drone to control the flight status of the anchoring and towing drone;

[0043] The control unit is also connected to the winch to control the winch to tighten or release the traction rope of the traction guide system.

[0044] For example, such as Figure 1As shown in the embodiment of this application, a multi-UAV rope-driven parallel cooperative system is designed. The system includes three parts: a multi-UAV group driving a work platform in the air for flight or operation, carrying a variety of work platforms. Taking a maintenance robot as an example, the work platform robot carries maintenance equipment (including magnetic flux leakage detection sensors, ultrasonic flaw detectors, visual inspection cameras, mechanical repair tools, etc.), four (or more) anchoring and traction UAVs, and a steel wire rope traction and guidance system between them. Both the UAVs and the work platform robots have specially designed magnetic pawl anchoring modules to stably adhere to the steel structure surface, achieving stable docking and operation. In formation flight mode, the task and formation switching need to be completed through formation control. Then, the work platform robot is pulled to the center of the geometric formation through rope-driven parallel robot trajectory tracking control to ensure the stability of the multi-UAV system flight control. When the robot is in single-UAV and multi-UAV cooperative climbing operation motion modes, the mobile maintenance robot tightens the steel wire rope through its own small winch, so that the anchoring and traction UAVs are attached to the mobile maintenance robot platform. In this state, the multi-drone cluster is in a stopped state, and the mobile maintenance robot platform performs inspections on the steel structure surface in single-machine form, or it can perform cross-cable maintenance on the steel structure surface through multi-machine collaboration.

[0045] like Figure 2 As shown, both the drone and the maintenance robot are equipped with a magnetic grappling hook anchoring module. This module features magnetic grappling hooks that can magnetically attach to the surface of the structure to be inspected, thus anchoring the drone or robot to the steel structure. When maintenance work is required, the maintenance robot can tighten or release the traction rope of the traction guidance system using its equipped winch. This allows the robot to move backward by releasing the rope or forward by tightening it, maintaining the stability and coordination of the entire system. Furthermore, through precise control of the control unit, the flight status, position, and tightening or releasing of the traction rope of both the maintenance robot and the anchored drone can be finely adjusted to meet the needs of different maintenance tasks.

[0046] During the transition between formation flight and rope-driven parallel operations, the system must ensure smooth and efficient collaborative operation between the UAVs and the maintenance robot. To achieve this goal, the system employs an innovative control strategy that integrates multi-UAV cooperative formation control technology and trajectory tracking control technology for rope-driven parallel robots. During the formation flight phase, the UAV swarm maintains a preset geometric formation through precise collaborative control to ensure system stability and operational efficiency. In this process, the communication topology between UAVs is effectively utilized to construct an error state describing the collaborative relationship between them. Based on a mass dynamics model, a distributed formation controller is designed to converge the relative positions of the UAVs to the desired relative distance, thereby establishing and maintaining the preset geometric formation.

[0047] During the rope-driven parallel operation phase, the maintenance robot tightens the traction rope of the traction guidance system via its built-in winch, forming a stable parallel connection with the anchored and traction drones. At this time, the drone swarm is in a stopped state, while the maintenance robot, based on its configured magnetic grappling hook anchoring module, is stably anchored to the steel structure surface, performing single-robot or multi-robot collaborative climbing maintenance operations. Throughout this process, the system uses precise trajectory tracking control to ensure that the maintenance robot operates along a predetermined trajectory while maintaining a stable connection with the drone swarm.

[0048] Furthermore, to further enhance the system's adaptability and flexibility, the multi-UAV rope-driven parallel cooperative system in this embodiment also possesses reconfigurability. By adjusting the geometry of the ropes and the motion state of the mobile base, the system can automatically adjust its configuration and operating mode according to different operational needs and scenarios. This reconfigurability not only expands the system's workspace but also improves its payload capacity and operational efficiency.

[0049] like Figure 3 As shown, the multi-drone rope-driven parallel collaborative system can be applied to various scenarios, such as the maintenance of steel structure roofs in passenger stations. Taking this scenario as an example, the multi-drone rope-driven parallel collaborative system can perform climbing maintenance operations in either a single-drone mode or a multi-drone collaborative mode. Regardless of the climbing maintenance method, the maintenance robot can move and climb by releasing or tightening ropes while multiple drones are anchored to the surface of the steel structure, thus achieving maintenance operations at various locations on the steel structure.

[0050] In this embodiment, a magnetic grappling hook anchoring module is designed based on biomimetic principles, enabling the grappling hook of a maintenance robot or drone to stably adhere to the surface of a steel structure. Through research on the interaction mechanism between controllable magnetic adhesion and the steel structure surface, and combined with multi-machine collaborative traction technology, the load-bearing capacity is improved. Optimization of climbing gait and anchor point configuration strategies significantly enhances system stability and operational efficiency. Ultimately, an integrated system is constructed, combining a climbing motor, magnetic grappling hook, and lightweight maintenance tools, achieving high-efficiency inspection and heavy-duty maintenance of complex steel structure surfaces.

[0051] In one alternative implementation, multiple mobile module carriers are modularly combined and designed for collaborative operation. This multi-machine collaborative integrated design increases the system's functionality, enabling tasks that a single robot system cannot accomplish. For example, when the work carrier is heavy, multiple anchoring and traction of multiple drones (mobile modules) collaboratively transport the large work carrier to a designated location. Furthermore, the first and second magnetic grappling hook anchoring modules have the same structure, both including a claw-like structure designed based on biomimetic principles and a controllable electromagnetic adsorption component to generate controllable magnetic force for adhesion to the steel structure surface. This allows for the switching of any drone from flight mode to fixed climbing mode.

[0052] Specifically, the claw-like structure is made of high-strength, lightweight materials to ensure sufficient gripping force while reducing overall weight. The controllable electromagnetic adsorption component adjusts the magnetic force by regulating the current, allowing the maintenance robot or drone to stably adhere to steel surfaces of varying materials and thicknesses. This design not only improves the system's adaptability and flexibility but also ensures safety and stability during operation. Furthermore, the combined use of the claw-like structure and the controllable electromagnetic adsorption component allows the maintenance robot or drone to quickly and easily release or re-adhere to the steel surface when needed, significantly improving operational efficiency.

[0053] In another optional implementation, the anchoring and towing UAVs establish a communication network via wireless communication modules. The topology of this communication network is a minimum spanning tree structure built based on the Prim algorithm, used to optimize data transmission paths between the UAVs. Specifically, when constructing the communication network, the system first uses the Prim algorithm to calculate the optimal connection path between the UAVs, forming a minimum spanning tree structure. This structure not only ensures unimpeded data communication between the UAVs but also minimizes redundancy in communication links, improving the efficiency and stability of data transmission. Through this communication network, the UAV swarm can share location information, operational status, and task instructions in real time, thereby achieving closer and more efficient collaborative operations.

[0054] Furthermore, to enhance the robustness and reliability of the system, the multi-UAV rope-driven parallel cooperative system in this embodiment also employs a redundancy design. In the UAV swarm, each UAV has the ability to independently execute tasks and can be used as a backup UAV when necessary. When a UAV malfunctions or cannot continue operating, the system can quickly adjust its operational strategy and redistribute tasks to other UAVs to ensure the continuous and stable operation of the entire system.

[0055] Furthermore, the system possesses powerful data processing and analysis capabilities. Through built-in sensors and detection equipment, the system can collect various data on the steel structure surface in real time, such as the degree of corrosion, crack width, and coating condition. This data is transmitted to the control unit for processing and analysis to generate detailed maintenance reports and repair recommendations. This not only provides strong decision support for maintenance personnel but also significantly improves the accuracy and efficiency of maintenance operations.

[0056] In another alternative implementation, the control unit includes:

[0057] The formation control module is used to generate distributed control commands based on the dual integral dynamics model of the anchored and towed UAVs, and control all UAVs to form and maintain a preset geometric formation.

[0058] The multi-machine collaboration module connects the UAV and the work platform through ropes to form an interconnected system for stable flight or operation, and collaboratively completes multiple work tasks of the work platform.

[0059] The trajectory tracking control module is used to calculate the optimal tension distribution of the wire rope based on the rope-driven parallel dynamics model of the maintenance robot, and to control the motion trajectory of the maintenance robot.

[0060] Among them, the multi-machine collaboration module can combine drones and operational robots into an interconnected system that can fly or operate stably through ropes, and collaboratively complete operational tasks such as inspection, maintenance, rescue, and capture of operational robots.

[0061] Specifically, the formation control module utilizes the relative position information among the drones and employs a distributed control algorithm to precisely calculate the flight speed and direction of each drone, ensuring they can quickly and stably form a preset geometric formation. During this process, a dual-integral dynamics model is used to describe the drones' motion states, thereby achieving accurate prediction and control of their flight trajectories. By continuously adjusting and optimizing the drones' flight states, the formation control module ensures that the drone swarm maintains a stable formation in complex environments, providing a solid foundation for subsequent tethered parallel operations.

[0062] The trajectory tracking control module focuses on the motion control of the maintenance robot. Based on a rope-driven parallel dynamics model, it comprehensively considers factors such as the tension of the traction rope, the weight of the maintenance robot, and the friction of the steel structure surface, and uses a model predictive control algorithm to calculate the optimal tension distribution scheme. This scheme ensures that the maintenance robot climbs along a predetermined trajectory while minimizing wear and energy consumption of the traction rope. By adjusting the tension of the traction rope in real time, the trajectory tracking control module can achieve precise control of the maintenance robot's motion trajectory, thereby improving work efficiency and safety.

[0063] In practical applications, the formation control module and the trajectory tracking control module work together to achieve the efficient operation of the multi-UAV rope-driven parallel collaborative system. During the formation flight phase, the formation control module ensures that the UAV swarm forms and maintains a stable geometric formation; during the rope-driven parallel operation phase, the trajectory tracking control module ensures that the maintenance robot operates according to the predetermined trajectory. The seamless integration and efficient collaboration of the two modules enable the multi-UAV rope-driven parallel collaborative system to complete various challenging maintenance tasks in complex environments.

[0064] In another alternative implementation, the maintenance robot is also integrated with a first environmental perception sensor, and the anchoring and towing drone is integrated with a second environmental perception sensor; the control unit is also configured to fuse data from the first environmental perception sensor and the second environmental perception sensor for real-time environmental modeling and operation decision-making.

[0065] Specifically, the first and second environmental perception sensors are responsible for collecting data on the surrounding environment of the maintenance robot and the anchoring and towing drone, respectively. This data includes, but is not limited to, distance, obstacle locations, light intensity, and temperature. By fusing this data, the control unit can construct a more accurate and comprehensive environmental model, providing a more reliable basis for operational decisions. For example, when performing maintenance work on complex and variable steel structure surfaces, the environmental perception sensors can detect surrounding obstacles and potential hazardous areas in real time, thereby guiding the maintenance robot and drone to avoid these areas and ensuring the safety and efficiency of the operation. Simultaneously, environmental perception data can also be used to optimize work paths and strategies, further improving the system's operational efficiency and adaptability.

[0066] In another optional implementation, the inspection equipment includes one or a combination of the following: a magnetic flux leakage sensor, an ultrasonic flaw detector, a visual inspection camera, and mechanical repair tools. Specifically, the magnetic flux leakage sensor can detect minute cracks and corrosion on the surface of the steel structure, identifying potential defects by measuring changes in the magnetic field. The ultrasonic flaw detector utilizes the propagation characteristics of ultrasonic waves in materials to perform in-depth inspection of the steel structure, detecting defects such as internal cracks and inclusions. The visual inspection camera uses high-resolution image acquisition and processing technology to visually inspect the surface of the steel structure, identifying problems such as surface rust and coating peeling. Mechanical repair tools are used to repair and treat the detected defects, such as grinding, welding, and spraying. The integrated use of these inspection devices greatly improves the system's inspection and maintenance capabilities, ensuring the safe and stable operation of the steel structure.

[0067] Besides operational platforms used in critical infrastructure projects such as power plants and bridges, these platforms also serve other purposes. For example, in forest fire rescue, they can carry integrated thermal imaging and gas detection modules to accurately locate fire sources. In natural disaster scenarios, drone swarms can quickly establish temporary communication relay stations to support rescue operations or carry emergency relief supplies. Multiple drone groups and operational platforms can also carry optical cameras, infrared sensors, lidar, gas detectors, and other equipment to collect multimodal data.

[0068] In another alternative implementation, the control unit is configured to control the system to switch between a formation flight mode and a climbing maintenance mode; in the formation flight mode, the anchoring and traction drones are controlled to form a predetermined formation and fly together to ensure that the work carrier, which is pulled by multiple anchoring and traction drones via ropes, flies synchronously and stably; in the climbing maintenance mode, the winch is controlled to tighten the wire rope, so that the anchoring and traction drones are anchored to the work surface by the second magnetic grappling hook anchoring module, and the maintenance robot performs the climbing operation.

[0069] Specifically, in climbing maintenance mode, the system can flexibly adjust its operational strategy to adapt to different maintenance needs and scenarios. When a climbing maintenance task needs to be performed, the control unit instructs the anchoring and traction drone to stably anchor itself to the steel structure surface using its equipped second magnetic grappling hook anchoring module. Simultaneously, the maintenance robot tightens the steel cable using a winch, establishing a stable connection with the drone. In this state, the maintenance robot can climb along a predetermined trajectory to perform detailed inspection and maintenance of the steel structure.

[0070] When switching to formation flight mode, the system automatically adjusts the flight status and position of the drones, enabling them to quickly form a predetermined geometric formation and fly together to the designated work area. During this process, the control unit makes full use of the communication topology and cooperative control algorithms between the drones to ensure that the drone swarm can maintain a stable formation and high operational efficiency.

[0071] Furthermore, to further improve the system's intelligence and operational efficiency, the multi-UAV rope-driven parallel collaborative system in this embodiment can also integrate advanced navigation and positioning technologies. Through built-in GPS modules, inertial navigation systems, and visual positioning sensors, the system can acquire real-time position information and attitude data of the UAVs and maintenance robots. This data will be transmitted to the control unit for processing and analysis to generate more accurate navigation and positioning commands. This not only further improves the system's operational accuracy and stability but also provides maintenance personnel with more intuitive and convenient monitoring and management methods.

[0072] This application also provides a multi-robot collaborative operation system for the maintenance of large steel structures, which includes the multi-UAV rope-driven parallel collaborative system described in the foregoing embodiments and their various optional implementations. This system can be applied to the maintenance of large power grid towers, wind turbines, cable bridges, railway stations, and stadiums, or other special scenarios such as the transportation of relief supplies in emergencies like forest fires and earthquakes, and the tracking and capture of unauthorized UAVs, small aircraft, or ground moving targets.

[0073] The following is combined Figures 1 to 3 The multi-UAV tethered parallel cooperative system shown in the embodiment describes the multi-UAV tethered parallel cooperative control method in this application:

[0074] Please see Figure 4 One embodiment of the multi-UAV tethered parallel cooperative control method in this application includes:

[0075] 401. When a mission instruction is received, if the mission instruction indicates that a flight operation should be performed, then the system is determined to enter the mobile base cable driven parallel robot mode. The mobile base cable driven parallel robot mode is used to indicate that the maintenance robot lands at the target position under the flight traction of the multiple UAVs.

[0076] The method of this embodiment can be applied to a multi-UAV rope-driven parallel cooperative system, which includes a maintenance robot and multiple UAVs connected to the maintenance robot. In this step, when a task instruction is received, the system first determines the content of the task instruction. If the task instruction indicates the execution of a flight operation, it means that the system needs to enter the mobile base rope-driven parallel robot mode. In this mode, multiple UAVs will work together as a traction force to pull the maintenance robot to land at the designated target location. The realization of this step relies on the precise formation control and trajectory tracking control of the UAV swarm to ensure that the maintenance robot can safely and accurately reach the predetermined location.

[0077] 402. In the mobile base-rope driven parallel robot mode, based on the communication topology and mass dynamics model among the multiple UAVs, a distributed cooperative control algorithm is used to control the multiple UAVs to establish and maintain a preset geometric formation for flight.

[0078] 403. Control the multiple drones located in the preset geometric formation to jointly pull the maintenance robot so that the maintenance robot is positioned at the geometric center of the preset geometric formation;

[0079] Once the system enters the mobile, rope-driven parallel robot mode, it will further execute a series of precise control operations. Based on the communication topology and mass dynamics model among multiple UAVs, and through a distributed cooperative control algorithm, the system can precisely control these UAVs to establish and maintain a preset geometric formation during flight. Achieving this step requires not only close communication between the UAVs, but also their ability to dynamically adjust their flight state based on real-time position information and dynamic parameters to ensure the entire UAV swarm maintains a stable formation.

[0080] While maintaining a preset geometric formation, the system also controls these drones to jointly pull the maintenance robot. By precisely calculating the relative positions and traction force distribution among the drones, the system ensures that the maintenance robot is stably positioned at the geometric center of the preset formation. This design not only improves the stability and safety of the maintenance robot during flight but also provides a solid foundation for its subsequent climbing and maintenance operations.

[0081] Based on the communication topology and mass dynamics model among multiple UAVs, a distributed cooperative control algorithm is used to control the multiple UAVs to establish and maintain a preset geometric formation. One optional implementation is to construct an error state based on the communication topology among the multiple UAVs to describe the cooperative relationship between the UAVs, determine the expected relative distance between each UAV and the geometric center defined by the preset geometric formation, design a distributed formation controller based on the error state and the mass dynamics model, and use the expected relative distance as the input of the distributed formation controller, so as to control the relative positions among the multiple UAVs to converge to the flight state corresponding to the expected relative distance, thereby establishing and maintaining the preset geometric formation of the multiple UAVs.

[0082] The establishment of the particle dynamics model may include the following steps: Based on the six-degree-of-freedom nonlinear dynamics model of the UAV, by ignoring attitude kinematics and assuming that the thrust is along the velocity vector direction and the sideslip angle is zero, a particle dynamics model for formation control can be established. The particle dynamics model is used to characterize the relationship between the position, velocity, acceleration and control input of the UAV.

[0083] When constructing an error state to describe the collaborative relationship between multiple UAVs based on the communication topology between the multiple UAVs, an optional implementation is to construct a communication topology graph between the multiple UAVs, wherein the communication topology graph is based on an undirected graph representation, the nodes in the communication topology graph represent UAVs, and the edges connecting the nodes represent communication links; a graph theory matrix is ​​generated based on the communication topology graph, and the graph theory matrix is ​​used to construct an error state to describe the collaborative relationship between the UAVs.

[0084] For example, in the drone swarm problem, drones are treated as intelligent agents, and the communication relationships between drones are represented by an undirected graph. Assume there exists... One drone, The drones form a regular geometric formation (this patent considers four drones, i.e.) Communication between drones is handled by... This indicates that each node represents the corresponding drone. Represents a set of nodes. This represents the set of communication edges between nodes. Used to represent the adjacency matrix, when the... drones and the first When there is a communication link between the drones, ,otherwise .

[0085] For drone communication maps Its Laplacian matrix It can be defined as:

[0086] ;

[0087] Of course, in addition to the Laplacian matrix mentioned above, the graph theory matrix can also be other types of graph theory matrices, such as adjacency matrices, degree matrices, and other graph theory-based descriptive matrices.

[0088] The motion of an unmanned aerial vehicle (UAV) is a six-degree-of-freedom nonlinear model that integrates kinematics, dynamics, and force description. The UAV's attitude is described by defining a coordinate system and attitude representations (such as Euler angles or quaternions). Kinematic equations describe the UAV's motion in the body coordinate system, and the Newton-Euler equations are used to establish a dynamic model to describe the forces and torques acting on the UAV. Furthermore, these control inputs (control surfaces and thrust control) are used to modify aerodynamic forces and torques to achieve the desired flight state.

[0089] The design of error states is crucial in constructing the error states used to describe the cooperative relationships among UAVs. These error states reflect the difference between the actual and desired positions of the UAVs, providing necessary feedback information for the distributed cooperative control algorithm. By designing error states appropriately, the algorithm can accurately calculate the adjustments required by the UAVs to maintain and adjust their formation. This step relies on a deep understanding of the communication topology between UAVs and accurate modeling of the mass dynamics model.

[0090] First, calculate the aerodynamic forces and aerodynamic torque of the drone. According to aerodynamic theory, the lift force acting on the drone... ,resistance and lateral forces It can be calculated using the following equation:

[0091]

[0092] in, , and These represent airspeed, air density, and wing reference area, respectively. , and These represent the lift coefficient, drag coefficient, and lateral force coefficient, respectively.

[0093] The above aerodynamic coefficients , and It can also be calculated using the following equation:

[0094]

[0095] in, and These are the angle of attack and the sideslip angle, respectively. , , , , , , The correlation coefficient.

[0096] After calculating the aerodynamic forces and torques, they need to be transformed into the UAV's body coordinate system to be correlated with the UAV's control inputs. Through coordinate transformation, the forces and torques of the UAV in the body coordinate system can be obtained, and then a corresponding controller can be designed to adjust the UAV's flight state.

[0097] Based on the aerodynamic equations, the rolling torque of the UAV is calculated. Pitch moment and yaw moment for:

[0098] ;

[0099] in, and These represent wingspan and mean chord length, respectively.

[0100] Its parameters in roll moment, pitch moment and yaw moment , , It can be calculated as:

[0101] ;

[0102] in, , , , This includes aileron deflection, rudder deflection, and elevator deflection. , , These are the basic coefficients for roll moment, pitch moment, and yaw moment, respectively. , , It is a parameter affecting the angle of attack. , , It is a parameter that affects the sideslip angle. , It is a parameter that affects the roll rate. , It is a parameter that affects yaw rate. Pitch rate affects parameters. , These are parameters affecting aileron deflection. , These are parameters affecting rudder deflection. These are parameters affecting elevator deflection. and These represent the roll rate and yaw rate, respectively.

[0103] In controlling the formation flight of drones, flight constraints such as maximum speed, minimum altitude, and maximum pitch angle must be considered. These constraints are crucial for ensuring the safety and stability of the drones during flight. Therefore, the design of distributed cooperative control algorithms must incorporate these constraints to ensure that the drones maintain the preset geometric formation without violating any flight constraints.

[0104] To achieve this goal, constraint handling mechanisms can be introduced into distributed cooperative control algorithms. For example, by designing a constraint optimization problem, the flight constraints of the UAV can be transformed into part of the optimization objective, thereby satisfying the flight constraints while controlling the UAV's flight state. Furthermore, advanced control methods such as model predictive control can be employed to predict the future state of the UAV and adjust the control input in advance to avoid violating flight constraints.

[0105] In addition to considering flight constraints, it is also necessary to pay attention to various disturbances that UAVs may encounter during flight, such as wind shear and gusts. These disturbances can affect the flight state of UAVs, thereby impacting the stability and accuracy of UAV formations. To address these disturbances, robust control strategies can be introduced into distributed cooperative control algorithms to improve the anti-interference capability of UAV formations. For example, robust control methods such as sliding mode control and active disturbance rejection control can be employed. By designing appropriate control laws and compensators, the impact of disturbances on the flight state of UAVs can be suppressed, thereby improving the stability and accuracy of UAV formations.

[0106] Therefore, by designing a distributed cooperative control algorithm based on the communication topology and mass dynamics model among multiple UAVs, and combining it with constraint handling mechanisms and robust control strategies, precise control of UAV formation flight can be achieved. This method is not only applicable to the positioning task of maintenance robots in the mobile base-rope driven parallel robot mode, but can also be widely applied to other scenarios requiring UAV formation flight, such as aerial photography, agricultural plant protection, and logistics distribution.

[0107] This application's embodiments use a point mass aircraft model, such as... Figure 5 As shown, it captures most of the aircraft's dynamic effects. The UAV motion model is considered as a three-dimensional point mass model, neglecting the kinematic model of attitude. In the point mass model, it is assumed that the aircraft's thrust is along the velocity vector direction, and that the aircraft always performs coordinated maneuvers. Furthermore, it is assumed that the Earth is flat and does not rotate. These assumptions are reasonable for aircraft operating within a 200-nautical-mile range. Since collisions typically occur within this range, the fidelity provided by the point mass model is sufficient to describe these problems. Assuming the sideslip angle on Earth is zero, the... The flight of an aircraft follows the following three-dimensional equation of motion for a point mass:

[0108] ;

[0109] ;

[0110] in, This indicates the drone's serial number index. Indicates the first The location of the drone Indicates ground speed. and These represent the track angle and heading angle, respectively. , , , , , They represent the first The thrust, drag, lift, mass, gravitational acceleration, and tilt angle of the drone.

[0111] For simplicity, the vectors are integrated and defined as follows:

[0112]

[0113] Taking the derivative of the position vector and substituting it into the equation, we get:

[0114]

[0115] in, , , .

[0116] It is a newly defined variable, and its relationship with the actual control variable is given by the following equation:

[0117] ;

[0118] in, This indicates the gravity load controlled by the elevator. and These represent the effective factors of the load factor and the load coefficient, respectively.

[0119] Furthermore, to simplify the design and implementation of the control algorithm, the aforementioned particle model can be linearized. The basic idea of ​​linearization is to perform a Taylor series expansion of the nonlinear equations around a certain equilibrium point, retaining the first-order terms, thereby obtaining the linearized state-space equations. In this way, complex nonlinear control problems can be transformed into relatively simple linear control problems, facilitating the application of classical control theory and methods in controller design.

[0120] During linearization, it is necessary to determine the system's equilibrium state. The equilibrium state refers to the state in which the system remains constant in the absence of external disturbances and control inputs. For a UAV formation flight system, the equilibrium state typically refers to the UAVs maintaining a preset geometric formation with constant speed and attitude. After determining the equilibrium state, the system's state equations can be linearized to obtain linearized state-space equations.

[0121] Next, controller design can be performed using methods such as Linear Quadratic Optimal Control (LQR). The LQR method is an optimal control strategy based on state feedback. It obtains the optimal state feedback gain matrix by solving the algebraic Riccati equation, thereby achieving optimal system control. During the design process, an appropriate weight matrix can be selected based on the system's performance requirements (such as formation maintaining accuracy and convergence speed), and the optimal controller parameters can be obtained by solving the LQR problem.

[0122] Finally, the designed controller needs to be simulated and tested experimentally. Simulation verification can be performed on a computer by building a simulation model of the UAV formation flight system to test the controller's effectiveness and performance. Experimental testing, on the other hand, needs to be conducted in a real-world environment. Flight experiments are used to evaluate the controller's actual control effect, and the controller can be further adjusted and optimized based on the experimental results.

[0123] Among them, the aforementioned heading angle and track angle The calculation formula is:

[0124] ,

[0125] Based on the above description, resistance It can be calculated as follows:

[0126]

[0127] in, This represents the induced drag coefficient.

[0128] Here, we assume all drones fly at the same altitude and simplify the drone dynamics model to a double integral model, which is expressed as:

[0129]

[0130] in, . , , and The first The position, speed, acceleration, and control inputs of the drone.

[0131] The drone's flight is controlled by inner and outer loops. The inner loop manages the drone's attitude through a proportional-differential controller, while the outer loop adjusts the drone's position and speed through a formation controller. This application's embodiments primarily focus on the control of the drone's position and speed; therefore, attitude loop control is not considered.

[0132] Design No. The control input for the drone is:

[0133] ;

[0134] in, , It is the gain coupling coefficient. (Vector) Indicates the first The relative distance between the drone and the geometric center.

[0135] Through the aforementioned distributed controller design, four drones can form a fixed diamond formation and maintain stable flight, while maintaining the same speed and direction of motion, achieving efficient cooperative formation of multiple drones, such as... Figure 6 As shown. From Figure 6 As can be seen, the four drones formed a stable diamond formation in three-dimensional space, with each drone precisely maintaining its preset position. The entire formation flew smoothly at a uniform speed and direction. The choice of a diamond formation not only enhances the stability of the formation but also facilitates communication and cooperation between the drones, thereby improving the overall mission execution efficiency.

[0136] In simulation tests, the distributed cooperative control algorithm demonstrated excellent performance. Even under external disturbances and uncertainties, the UAV formation was able to quickly adjust and maintain a stable formation. This is attributed to the constraint handling mechanism and robust control strategy introduced in the algorithm, which effectively improve the UAV formation's adaptability and anti-interference capability in complex environments.

[0137] In the experimental test, four drones conducted formation flight experiments within a designated area. The results showed that the designed controller can accurately control the position and speed of the drones, enabling the drone formation to fly stably according to the preset geometric formation. Furthermore, the drone formation can react quickly to unexpected situations and external interference, maintaining the stability and accuracy of its formation.

[0138] 404. If the task instruction indicates to perform climbing maintenance work, then the system is determined to enter the fixed base rope-driven parallel robot mode. The fixed base rope-driven parallel robot mode is used to indicate that when the multiple drones are anchored at the target position, the maintenance robot climbs to the maintenance position based on the pull of the multiple drones to perform maintenance.

[0139] In this step, if the task instruction calls for climbing and maintenance work, the system must enter the fixed-base-rope-driven parallel robot mode. In this mode, multiple drones will be anchored at the target location, forming a stable working platform. The maintenance robot then climbs using the steel cables pulled by the drones until it reaches the location to be inspected. To ensure the safety and stability of the climbing process, the system will adjust the anchoring position of the drones and the tension of the pulling ropes in real time to adapt to different steel structure surfaces and operational requirements. Simultaneously, the maintenance robot will utilize integrated maintenance equipment, such as magnetic flux leakage sensors and ultrasonic flaw detectors, to perform detailed inspection and maintenance of the steel structure, ensuring the accuracy and efficiency of the maintenance work.

[0140] 405. In the fixed-base rope-driven parallel robot mode, a quadratic programming problem is constructed based on the nonlinear dynamic model of the maintenance robot, with tracking error and control input increment as optimization objectives;

[0141] In this embodiment, a quadratic programming problem with tracking error and control input increment as optimization objectives is constructed based on the nonlinear dynamic model of the maintenance robot. One optional implementation is to establish a nonlinear dynamic model of the maintenance robot based on the Newton-Euler equations. The nonlinear dynamic model is used to characterize the mapping relationship between the position, velocity, acceleration of the maintenance robot and the rope tension. The nonlinear dynamic model is transformed into a linear time-varying model in the prediction time domain based on the linear parameter variation method, and a quadratic programming problem with tracking error and control input increment as optimization objectives is constructed based on the linear time-varying model.

[0142] In one possible implementation of the method for transforming the nonlinear dynamic model into a linear time-varying model in the prediction time domain based on linear parameter variation, the system matrix in the nonlinear dynamic model is represented as a function of scheduling parameters; in each prediction time domain, the current scheduling parameters are substituted into the function of the system matrix to calculate the system matrix of the linear time-varying model at the current time, thus obtaining the linear time-varying model.

[0143] For example, when modeling and controlling the dynamics of a cable-driven parallel robot, the mass of the cable distribution can be ignored, and the force of each cable is applied to the moving platform. Considering a cable-driven parallel robot with n degrees of freedom driven by m cables (a motorized maintenance robot), its Newton-Euler form dynamic model is as follows:

[0144] U;

[0145] in, This indicates the state of the rope-driven parallel robot system; This represents the speed of the mobile platform; This represents the acceleration of the mobile platform. For more details... Indicates the planar position of the mobile platform. These represent the orientation of the mobile platform. Let be the rotation angle of the mobile platform around the Z-axis. It is the control input, representing the tension vector of the four ropes.

[0146] The inertia matrix of dimension 3×3 is represented as:

[0147] ;

[0148] The nonlinear Coriolis centrifugal moment matrix has the following dimension: , represented as:

[0149] ;

[0150] Let be the gravity vector of the mobile platform, with dimensions 3×1, represented as ;

[0151] The aerodynamic load on the mobile platform is denoted as , These represent the interference force and interference torque experienced by the mobile platform, respectively.

[0152] The Jacobian matrix of the system is expressed as:

[0153] .

[0154] The detailed derivation process is as follows:

[0155] According to Newton's second law, the translational dynamics model can be obtained:

[0156] ;

[0157] Written in matrix form as follows:

[0158]

[0159] in, For the first The unit vector in the direction of the root rope. , This is the gravitational acceleration matrix.

[0160] The rotational dynamics equations can be derived from Euler's equations, as follows:

[0161] ;

[0162] In matrix form, we get:

[0163] ;

[0164] Since it has only rotational inertia about the z-axis, it only exists All other items are 0.

[0165] yes The antisymmetric matrix; ;

[0166] Represents the mobile platform relative to the local coordinate system The cable attachment location.

[0167] Furthermore, by combining the translational and rotational dynamic equations, the complete dynamic equations can be obtained.

[0168] Therefore, an accurate dynamic model of the maintenance robot can be obtained through the above method. This model not only considers the position, velocity, and acceleration of the moving platform, but also integrates the effects of rope tension, aerodynamic loads, and interference factors. Based on this dynamic model, the behavior of the maintenance robot during the climbing process can be predicted and controlled more accurately, thereby improving the safety and efficiency of the operation.

[0169] In constructing the quadratic programming problem, tracking error and control input increment are used as optimization objectives. Tracking error reflects the difference between the actual and desired positions of the maintenance robot, while the control input increment represents the additional control force required by the system to reduce the tracking error. By solving this quadratic programming problem, the optimal control input can be obtained, enabling the maintenance robot to track the desired trajectory with minimal error and energy consumption.

[0170] Furthermore, the method of transforming nonlinear dynamic models into linear time-varying models within the prediction time domain provides an effective approach for handling complex nonlinear systems. By representing the system matrix as a function of scheduling parameters and substituting the current scheduling parameters into each prediction time domain, a series of linear time-varying models can be obtained, which can be more easily used for controller design and optimization.

[0171] Next, trajectory tracking control design can be carried out, as described in the following steps.

[0172] 406. Solve the quadratic programming problem to determine the optimal rope tension, and control the rope drive mechanism to perform trajectory tracking control on the maintenance robot based on the optimal rope tension;

[0173] In this embodiment, the quadratic programming problem is solved to determine the optimal rope tension, and the rope drive mechanism is controlled to perform trajectory tracking control on the maintenance robot based on the optimal rope tension. One optional implementation is to solve the quadratic programming problem to obtain the optimal rope tension sequence in the future time domain; the first control variable in the optimal rope tension sequence is applied to the rope drive mechanism, so that the rope drive mechanism performs trajectory tracking control on the maintenance robot based on the first control variable.

[0174] For example, trajectory tracking control methods applicable to rope-driven parallel robot systems mainly include PD control, model predictive control, adaptive robust control, and sliding mode control. This application will use a predictive control design based on a linear parameter variation model as an example to introduce its control scheme as follows.

[0175] The above dynamic system model can be rewritten in the following general form of a nonlinear mathematical model:

[0176] ;

[0177] This can then be transformed into a linear parameter variation (LPV) model:

[0178] ;

[0179] in , , n, m, and l represent the dimensions of the state, control input, and output, respectively. Matrices A(.) and B(.) are the scheduling parameter vectors. Continuous functions, scheduling parameters It can be selected as a status variable or a control input.

[0180] Therefore, the cost function can be written as:

[0181] ;

[0182] Where Q, R, and T are weight matrices, and e is the tracking error. The commonly used representation of constraints in the control input is as follows:

[0183] ;

[0184] The aforementioned cost function with constraints can be efficiently solved as a quadratic programming problem using a fast solver in Matlab (such as quadprog). In Model Predictive Control (MPC), the solver solves the quadratic programming problem in each control cycle, obtaining a series of optimal control variables over a finite future time domain. Since the control variables in this embodiment are rope tension, solving this quadratic programming problem yields the optimal rope tension sequence over the future time domain.

[0185] Furthermore, the standard, default operating procedure of the MPC method is called Receding Horizon Control. MPC does not execute the entire sequence at once; instead, it applies only the first control variable from the sequence to the system. In the next control cycle, the optimization problem is solved again based on the new state, resulting in a new sequence, and the first variable is executed again, and so on. Therefore, the first control variable in this optimal rope tension sequence can be applied to the rope drive mechanism, which then uses this first control variable to perform trajectory tracking control on the maintenance robot.

[0186] Through system dynamics modeling and control method design in the above flight mode and climbing maintenance mode, collaborative control of heterogeneous robots can be realized. It has the advantages of strong load-bearing capacity, strong obstacle crossing ability, and strong adaptability to multiple scenarios, and can complete the maintenance tasks of complex steel structures.

[0187] In this embodiment, during climbing and maintenance operations, the maintenance robot can interact with the steel structure surface of the structure to be maintained based on its configured magnetic grappling hook anchoring module, thereby anchoring the maintenance robot to the steel structure surface. The magnetic grappling hook anchoring module is equipped with magnetic grappling hooks, which are used to release and re-attach the maintenance robot to the steel structure surface based on the dynamically adjustable magnetic force of the magnetic grappling hooks.

[0188] In the design of the magnetic grappling hook anchoring module, the magnetic force of the grappling hook can be dynamically adjusted using electromagnets or permanent magnets. Electromagnets, by controlling the magnitude and direction of the current, can flexibly adjust the magnetic force, thus achieving precise control over the anchoring state of the maintenance robot. Permanent magnets, through mechanical structure design, such as rotation or sliding, change the relative position of the magnetic poles to the steel structure surface, thereby adjusting the magnetic force. This design allows the maintenance robot to release or re-attach to the steel structure surface as needed during climbing, improving operational flexibility and safety.

[0189] Furthermore, the magnetic grappling hook anchoring module possesses self-sensing capabilities, enabling it to monitor the magnitude and status of the magnetic force in real time. When the magnetic force weakens or disappears, the module immediately issues an alarm and takes appropriate safety measures to prevent the maintenance robot from accidentally detaching. Simultaneously, the module can automatically adjust the distribution of the magnetic force based on the material and shape of the steel structure surface, ensuring the maintenance robot remains stably anchored in various complex environments.

[0190] By employing a magnetic grappling hook anchoring module, the maintenance robot can achieve flexible anchoring and stable control over the surface of steel structures during climbing and maintenance operations. This not only improves the safety and efficiency of the operation but also provides more reliable technical support for the maintenance of complex steel structures.

[0191] The computer device in the embodiments of this application is described below. Please refer to [link / reference]. Figure 7 One embodiment of the computer device in this application includes:

[0192] The computer device 700 may include one or more central processing units (CPUs) 701 and a memory 705, in which one or more applications or data are stored.

[0193] The memory 705 can be volatile or persistent storage. The program stored in the memory 705 can include one or more modules, each module including a series of instruction operations on the computer device. Furthermore, the central processing unit 701 can be configured to communicate with the memory 705 and execute the series of instruction operations stored in the memory 705 on the computer device 700.

[0194] The computer device 700 may also include one or more power supplies 702, one or more wired or wireless network interfaces 703, one or more input / output interfaces 704, and / or one or more operating systems, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0195] The central processing unit 701 can perform the aforementioned... Figure 4 The operations performed by the computer device in the illustrated embodiments and their various alternative embodiments are not described in detail here.

[0196] This application also provides a computer storage medium, one embodiment of which includes: the computer storage medium storing instructions, which, when executed on a computer, cause the computer to perform the aforementioned... Figure 4 The operations performed by the computer device in the illustrated embodiments and their various alternative embodiments.

[0197] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0198] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0199] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0200] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0201] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A multi-UAV tethered parallel cooperative system, characterized in that, include: Multiple anchoring and traction drones, a working platform, and a traction and guidance system connecting the working platform and each of the anchoring and traction drones; The working carrier may include maintenance equipment, a winch, and a first magnetic claw anchoring module. Each of the aforementioned anchoring and traction UAVs is equipped with a second magnetic grappling hook anchoring module; One end of the traction and guiding system is wound around the winch of the working carrier, and the other end is connected to the anchoring and traction drone. The system also includes a control unit connected to the anchoring and towing drone to control the flight status of the anchoring and towing drone; The control unit is also connected to the winch to control the winch to tighten or release the traction rope of the traction guide system.

2. The multi-UAV tethered parallel cooperative system according to claim 1, characterized in that, The first magnetic claw anchoring module and the second magnetic claw anchoring module have the same structure, both including a claw structure designed based on bionic principles and a controllable electromagnetic adsorption component. In the design of multi-machine collaboration, they are used to generate controllable magnetic attraction force to adhere to the surface of the steel structure.

3. The multi-UAV rope-driven parallel cooperative system according to claim 1, characterized in that, The anchoring and traction UAVs form a communication network through wireless communication modules.

4. The multi-UAV tethered parallel cooperative system according to claim 1, characterized in that, The working platform is also integrated with a first environmental perception sensor, and the anchoring and traction UAV is integrated with a second environmental perception sensor.

5. The multi-UAV tethered parallel cooperative system according to claim 1, characterized in that, The maintenance equipment is one or a combination of the following: magnetic flux leakage sensor, ultrasonic flaw detector, visual inspection camera, and mechanical maintenance tools.

6. The multi-UAV tethered parallel cooperative system according to any one of claims 1 to 5, characterized in that, The traction and guidance system includes steel wire rope; The system includes a formation flight mode and a climbing and maintenance mode; in the formation flight mode, the anchoring and traction drones form a predetermined formation and fly together, with multiple anchoring and traction drones pulling the work carrier synchronously and stably through rope drive. In the climbing maintenance mode, the winch tightens the wire rope, the anchoring traction drone is anchored to the working surface by the second magnetic claw anchoring module, and the working carrier performs the climbing operation.

Images