Configuration design method of multi-uav cooperative hoisting system based on adaptive anti-collision constraint

Abstract

This invention relates to the field of UAV collaborative control technology and discloses a configuration design method for a multi-UAV collaborative hoisting system based on adaptive collision avoidance constraints. The method constructs a standard library of multi-UAV collaborative hoisting system configurations, designs adaptive collision avoidance constraints for all configurations within the library, dynamically adjusts the spacing between UAVs based on the minimum safe distance, and selects candidate configurations from the library. It selects the optimal solution through multi-level feasibility checks and multi-objective cost evaluations, performs secondary verification on the optimal solution, and generates the final configuration scheme. This method achieves unified scheduling of multiple UAV models, self-adjustment of collision avoidance, and self-optimization of tasks, significantly improving the safety, stability, and adaptability of the hoisting system. It is suitable for complex scenarios such as power emergency transportation, enabling adaptive selection of the optimal configuration based on load characteristics while ensuring system safety and stability.

Description

Technical Field

[0001] This invention relates to the field of UAV collaborative control technology, and more specifically to a configuration design method for a multi-UAV collaborative hoisting system based on adaptive collision avoidance constraints. Background Technology

[0002] With the rapid development of drone technology, multi-drone collaborative operations have shown broad application prospects in logistics, emergency rescue, and engineering construction. In multi-drone collaborative hoisting systems, multiple drones work together via slings to lift a single load, overcoming the limitations of a single drone's load capacity and enabling the transport of heavy and large-sized objects. In power emergency rescue, power repair materials are heavy and require stringent transportation standards. Disaster-stricken areas are often impassable to traditional ground vehicles due to landslides and flooded roads, while manual handling is inefficient and carries high safety risks, failing to meet the timeliness requirements of emergency repairs. Multi-drone collaborative hoisting, as an emerging aerial transportation method, demonstrates broad development prospects due to its high flexibility, deployment efficiency, and environmental adaptability. It provides a new means for transporting emergency relief materials, significantly improving hoisting efficiency, compensating for the shortcomings of traditional transportation, and is crucial for ensuring the safe and stable operation of the power grid.

[0003] Optimal configuration design of multi-UAV collaborative lifting systems is a crucial component of multi-UAV collaborative lifting technology. Through rational configuration design, lifting efficiency and energy utilization can be improved while ensuring system stability, and the robustness and fault tolerance of the lifting system in complex environments can be enhanced. However, current research on the configuration design of multi-UAV collaborative lifting systems is limited. Considering optimization indicators such as UAV energy consumption, transport stability, and system robustness, research is urgently needed on lifting configurations suitable for different types of loads and different numbers of UAVs. A method is required that, through reasonable dynamic configuration and control strategies, improve the transportation safety, stability, and efficiency of the entire lifting system. Summary of the Invention

[0004] To overcome the aforementioned technical problems, this invention provides a configuration design method for a multi-UAV collaborative hoisting system based on adaptive collision avoidance constraints. This method constructs a standard library of multi-UAV collaborative hoisting system configurations, designs adaptive collision avoidance constraints for all configurations within the library, dynamically adjusts the spacing between UAVs based on the minimum safe distance, and selects candidate configurations from the library. It then selects the optimal solution through multi-level feasibility checks and multi-objective cost evaluations. The optimal solution undergoes secondary verification to generate the final configuration scheme. This method achieves unified scheduling of multiple UAV models, self-adjustment of collision avoidance, and self-optimization of tasks, significantly improving the safety, stability, and adaptability of the hoisting system. It is suitable for complex scenarios such as power emergency transportation, enabling adaptive selection of the optimal configuration based on load characteristics while ensuring system safety and stability.

[0005] To achieve the above objectives, the present invention provides a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints, the configuration design method comprising: Construct a standard library of configurations for multi-UAV collaborative lifting systems; Adaptive collision avoidance constraints for all multi-UAV collaborative hoisting system configurations in the design standard library; Obtain parameters for the drone and payload, and filter candidate configurations from the standard library; Examine and evaluate the candidate configurations to obtain the optimal configuration. Verify the optimal solution and generate the final configuration scheme.

[0006] Preferably, a standard library of configurations for multi-UAV collaborative lifting systems is constructed, including: A flexible, massless suspending rope is used to connect the drone's mass point and load; Obtain the shape of the load and determine whether the load is a rod-shaped load or a cubic load; When the load is rod-shaped, determine whether the mass distribution of the load is uniform. When the mass distribution of the load is uniform, the connection points between the multiple UAVs and the load are equidistantly distributed along the length of the rod-shaped load based on centroid symmetry, generating a uniform rod-shaped configuration. When the mass distribution of the load is non-uniform, the connection points between the multiple UAVs and the load are distributed on both sides of the center of mass along the length of the rod-shaped load according to the gravitational torque balance on both sides of the center of mass, generating a rod-shaped non-uniform configuration. Given a cube-shaped load, determine the number of drones. A standard configuration is built based on the number of drones.

[0007] Preferably, a standard configuration is constructed based on the number of drones, including: Number of drones In this case, the connection points of multiple drones and the load are symmetrically distributed on both sides of the top surface of the cubic load, and all drones are in the same plane to generate a standard dual-drone symmetrical configuration. Number of drones In this case, the connection points of multiple drones and the load are distributed at two vertices on the top surface of the cube load and the midpoint of the side between the two vertices relative to the other side, and all drones are on the same plane to generate a standard three-drone triangular configuration. Number of drones In the case of multiple drones and loads, the connection points are distributed on the four vertices of the top surface of the cube load, and all drones are on the same plane to generate a standard four-drone square configuration. Alternatively, the connection points are distributed on the midpoints of the four sides of the top surface of the cube load, and all drones are on the same plane to generate a standard four-drone cross configuration. Number of drones In the case of multiple drones and loads, the connection points are distributed on the five vertices of the embedded regular pentagon on the top surface of the cube load. All drones are on the same plane to generate a standard configuration of five drones and regular pentagons. Alternatively, the connection points are distributed on the center point and four vertices of the top surface of the cube load. All drones are on the same plane to generate a standard configuration of five drones with a central radial orientation. Number of drones In the case of multiple drones and loads, the connection points are distributed on the six vertices of the embedded regular hexagon on the top surface of the cube load. All drones are on the same plane to generate a standard six-drone regular hexagonal configuration. Alternatively, the connection points are distributed on the midpoint of the top surface of the cube load and the five vertices of the embedded regular pentagon. All drones are on the same plane to generate a standard six-drone central radial configuration. Number of drones In this scenario, every six drones form a central radial configuration, with all drones at the same height. Traversing all drones, the connection points between the outermost central radial configuration drones and their loads are distributed among the six vertices of the embedded regular hexagons on the top face of the cubic load. The connection points between the innermost layer drones and their loads are distributed among the vertices of the embedded regular hexagons within the outermost layer's embedded regular hexagons, and so on, until all drone and load connection points are distributed, thus generating... The standard configuration of the machine's center radiation.

[0008] Preferably, adaptive collision avoidance constraints are provided for all multi-UAV collaborative lifting system configurations within the design standard library, including: The position parameters of all UAVs in the multi-UAV collaborative hoisting system configuration are obtained based on the minimum safe distance; Adaptive collision avoidance constraints are designed for the configuration of a multi-UAV collaborative hoisting system based on position parameters.

[0009] Preferably, the position parameters of all UAVs in the multi-UAV collaborative lifting system configuration are obtained based on the minimum safe distance, including: The minimum safe distance is defined using formula (1).

[0010] in, For minimum safe distance, This is the maximum unfolding size of the drone. For flight control error, This refers to the pose error caused by external disturbances. For safety margin, For safety factor; Define the mass point along the length of the rod-shaped load as the origin, and construct a one-dimensional coordinate system along the length of the rod-shaped load; The position parameters of the UAV on the rod-shaped uniform configuration are obtained using formulas (2)-(3). (2) (3) in, For the first The horizontal coordinates of the drone For the length of the load, The total number of drones, This is the length of the suspension rope; The position parameters of the UAV on the rod-shaped non-uniform configuration are obtained using formulas (4)-(6). (4) (5) (6) in, This is the centroid offset. Given the centroid offset position, Let be the number of steps the particle takes to expand to the left and right. For the particle to expand to the left The horizontal coordinates of the connection point between the drone and the load during the flight. For the particle to expand to the right The horizontal coordinates of the connection point between the drone and the load during the flight. This is the step size scaling factor; Based on the fact that the projection of the center of all configurations of the cube load onto the top surface of the cube load is the same as the initial position of the center of the top surface of the cube load, the center of the configuration is taken as the origin of the two-dimensional inertial coordinate system, and the X-axis and Y-axis of the two-dimensional inertial coordinate system are set to be parallel to the two adjacent sides of the top surface, respectively, to form a two-dimensional inertial coordinate system. The position parameters of standard configuration UAVs with a number range of 2-6 are obtained using formulas (7)-(10). (7) (8) (9) (10) in, Let the radius of the minimum circumcircle of the planar figure formed by a UAV with a standard configuration based on minimum safe distance be . For based on An empirical coefficient for the number of drones The length of the suspension rope. Let be the radius of the final circumcircle of the planar figure formed by a standard configuration UAV. For the first The horizontal coordinate of the drone on the X-axis For the first The ordinate of the drone on the Y-axis; Obtain using formulas (11)-(14) Position parameters of a UAV with a standard configuration radiating from the center of the aircraft. (11) (12) (13) (14) in, For the number of floors, This is the floor function, which rounds up the value to the nearest integer not less than the value within the parentheses. For the first The radius of the circumcircle of the planar shape formed by the drone in the layer. The radius of the final circumcircle of the planar figure formed by a standard configuration UAV is the radius of the innermost circumcircle. For the first The first layer The horizontal coordinate of the drone on the X-axis For the first The first layer The ordinate of the drone on the Y-axis For the first The number of drones on the floor, For the first Interlayer deflection angle.

[0011] Preferably, the adaptive collision avoidance constraints for the configuration of the multi-UAV collaborative hoisting system, designed based on position parameters, include: Adaptive distance constraints between adjacent UAVs are set for all configurations using formulas (15)-(16). (15) (16) in, An adaptive minimum distance constraint between adjacent UAVs. For the first The location parameters of the drone For the first The location parameters of the drone This is the scaling factor; Using formula (17) The standard configuration with central radiation of the machine sets adaptive distance constraints between adjacent layers. (17) in, For adaptive distance constraints between adjacent layers, This is the radius scaling factor.

[0012] Preferably, the process of examining and evaluating candidate configurations to obtain the optimal configuration includes: Candidate configurations are selected by using multi-level feasibility constraints, and the selected configurations are obtained. A multi-objective cost evaluation method is used to process the selected configurations and obtain the cost value of each selected configuration. Select the configuration with the lowest cost after filtering as the current optimal solution.

[0013] Preferably, candidate configurations are screened using multi-level feasibility constraints to obtain the screened configurations, including: The candidate configurations are filtered by distance constraints using formula (18) to obtain the first configuration set. (18) in, The first minimum safe distance, For the first The location parameters of the drone For the first Position parameters of the drone; Formula (19) is used to filter the first configuration set by maximum swing angle constraint to obtain the second configuration set. (19) in, The first maximum swing angle, The maximum outer contour dimension of the load; The load-bearing constraints of the second configuration set are filtered using formula (20) to obtain the third configuration set. (20) in, Let be the radius of the circumcircle of the largest shape formed by the UAV in the configuration. This is the capacity factor; The third configuration set is used as the final filtered configuration.

[0014] Preferably, a multi-objective cost evaluation method is used to process the selected configurations, obtaining the cost value of each selected configuration, including: The multi-objective cost function is defined using formula (21). ,(twenty one) in, For a multi-objective cost function, Let the stability cost function be... Let redundancy cost function, Let the efficiency cost function be... To stabilize the weighting coefficients, This is the redundancy weighting coefficient. Efficiency weighting coefficient; The stability cost function is constructed using formulas (22)-(24). ,(twenty two) ,(twenty three) ,(twenty four) in, The angle of the suspension rope. For the rotational inertia of the unmanned aerial vehicle system, This is the reference moment of inertia for the unmanned aerial vehicle (UAV) system. For the first The quality of the drone For the first The distance from the drone to the center of the configuration; The redundancy cost function is constructed using formulas (25)-(26). (25) (26) in, The number of drones in the central area set in the configuration. The penalty coefficient is... The radius threshold of the central region; The efficiency cost function is constructed using formula (27). (27) in, The reference radius for the circumcircle of the UAV planar graphic formed in the configuration; The cost of each filtered configuration is obtained based on the multi-objective cost function.

[0015] Preferably, the optimal solution is verified, and the final configuration scheme is generated, including: The optimal solution is the configuration with the lowest cost. Lower the threshold of the first minimum safe distance and verify the distance constraint of the optimal solution again; reduce the threshold of the first large swing angle and verify the maximum swing angle constraint of the optimal solution again. Determine whether the optimal solution passes the verification; If the optimal solution passes verification, the final configuration scheme is generated based on the optimal solution; If the optimal solution fails to pass verification, the adaptive collision avoidance constraint steps for all multi-UAV collaborative hoisting system configurations in the design standard library are re-executed.

[0016] Through the above technical solution, this method achieves intelligent configuration optimization through systematic steps, constructs a standard library of configurations for multi-UAV collaborative hoisting systems, covering various standard configurations for rod-shaped loads and cubic loads, such as uniform and non-uniform mass distribution schemes, ensuring the basic completeness of the configuration design; designs adaptive collision avoidance constraints for all configurations in the standard library, dynamically adjusting the distances between UAVs and between layers based on the minimum safe distance formula (considering UAV size, control error, and external interference) to prevent collision risks; obtains parameters such as UAV model, load mass, and size, and filters theoretically feasible candidate configurations from the standard library; and conducts multi-level feasibility checks (including geometric constraints, maximum swing angle constraints, and load capacity constraints) and multi-objective cost evaluation. The algorithm estimates (balancing stability, redundancy, and efficiency), calculates the comprehensive cost of candidate configurations, and selects the optimal solution. It then performs secondary verification on the optimal solution, such as stress testing with a lowered safety threshold, to ensure robustness before generating the final configuration scheme. A configuration standard library enables unified scheduling of multiple UAV models, adapting to different load shapes and mass distributions and improving resource utilization. An adaptive collision avoidance constraint mechanism adjusts the safety distance according to real-time operating conditions, enhancing the system's anti-interference capability in complex environments. Multi-objective optimization evaluation ensures a balance between stability, fault tolerance, and energy consumption in the configuration, making it suitable for various scenarios such as emergency rescue and industrial hoisting. This method overcomes the limitations of traditional configuration design, which relies on experience and fixed collision avoidance, achieving intelligent, safe, and reliable collaborative hoisting. Attached Figure Description

[0017] Figure 1 This is a connection block diagram of a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints according to an embodiment of the present invention; Figure 2 This is a connection diagram constructed based on a configuration standard library of different shaped loads, according to an embodiment of the present invention, for a configuration design method of a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints. Figure 3 This is a connection block diagram based on a standard configuration of a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints, according to an embodiment of the present invention. Figure 4 This is a connection block diagram of a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints according to an embodiment of the present invention, which obtains UAV parameters under different standard configurations. Figure 5 This is a connection block diagram of the adaptive collision avoidance constraint of the configuration design of a multi-UAV cooperative hoisting system based on an adaptive collision avoidance constraint according to an embodiment of the present invention. Figure 6 This is a connection block diagram of the optimal solution in the screening configuration of a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints according to an embodiment of the present invention. Figure 7 This is a connection block diagram of a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints, according to an embodiment of the present invention, which uses multi-level feasibility constraints to screen candidate configurations. Figure 8 This is a connection diagram of a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints, according to an embodiment of the present invention, which obtains the cost value of configuration configuration based on a multi-objective cost evaluation method. Figure 9 This is a connection block diagram for generating the final configuration scheme of a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints according to an embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0019] like Figure 1 The diagram shown is a connection block diagram of a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints according to an embodiment of the present invention; Figure 1 In this configuration design method, the following steps may be included: In step S10, a standard library of configurations for multi-UAV collaborative hoisting systems is constructed; In step S11, adaptive collision avoidance constraints are designed for all multi-UAV collaborative hoisting system configurations in the standard library; In step S12, the parameters of the drone and the payload are obtained, and candidate configurations are selected from the standard library; In step S13, the candidate configurations are examined and evaluated to obtain the optimal solution for the configuration. In step S14, the optimal solution is verified and the final configuration scheme is generated.

[0020] Step S10 establishes a configuration library containing various standard configurations to provide standardized configuration schemes for loads of different shapes and masses; Step S11 ensures a safe distance between drones by designing corresponding collision avoidance constraints for each configuration in the configuration library; Step S12 involves selecting candidate configurations that meet the criteria from the configuration library based on the parameters of the UAV and payload in the actual mission. Step S13 selects the optimal solution from candidate configurations through multi-level feasibility constraints and multi-objective cost evaluation; Step S14 verifies the optimal solution a second time to ensure its safety and feasibility, and generates the final configuration scheme.

[0021] Through the above technical solution, this method constructs a standard library of configurations for multi-UAV collaborative hoisting systems, designs adaptive collision avoidance constraints for all configurations in the standard library, dynamically adjusts the spacing between UAVs based on the minimum safe distance, and selects candidate configurations from the library; selects the optimal solution through multi-level feasibility testing and multi-objective cost evaluation; performs secondary verification on the optimal solution, and generates the final configuration scheme; realizes unified scheduling of multiple UAV models, self-adjustment of collision avoidance, and self-optimization of tasks, significantly improving the safety, stability, and adaptability of the hoisting system. It is suitable for complex scenarios such as power emergency transportation, and can adaptively select the optimal configuration according to load characteristics, while ensuring the safety and stability of the system.

[0022] like Figure 2 The diagram shown is a connection block diagram constructed based on a standard library of configurations for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints, according to an embodiment of the present invention; Figure 2 In this invention, considering the different shapes of loads and the varying numbers of drones, multiple standard configurations can be constructed. In one embodiment of the invention, constructing a standard library of configurations for a multi-drone collaborative lifting system may include the following steps: In step S20, a flexible, massless suspension rope is used to connect the mass point of the UAV and the load; In step S21, the shape of the load is obtained, and it is determined whether the load is a rod-shaped load or a cubic load. In step S22, when the load is rod-shaped, it is determined whether the mass distribution of the load is uniform. In step S23, when the mass distribution of the load is uniform, the connection points between the multiple UAVs and the load are equidistantly distributed along the length of the rod-shaped load based on centroid symmetry, generating a uniform rod-shaped configuration. In step S24, when the mass distribution of the load is non-uniform, the connection points between the multiple UAVs and the load are distributed on both sides of the center of mass along the length of the rod-shaped load according to the gravitational torque balance on both sides of the center of mass, generating a rod-shaped non-uniform configuration. In step S25, the number of drones is obtained when the load shape is a cube load; In step S26, a standard configuration is constructed based on the number of drones.

[0023] The system model establishes a flexible, massless suspension cable to connect the UAV and the payload, and classifies the payload according to its shape: for rod-shaped payloads, a corresponding configuration is generated based on the mass distribution characteristics—for uniform distribution, an equidistant suspension point distribution based on centroid symmetry is used to form a uniform rod-shaped configuration; for non-uniform distribution, suspension points are distributed on both sides of the centroid according to the principle of gravitational moment balance to form a non-uniform rod-shaped configuration. Meanwhile, for cubic payloads, a corresponding standard configuration scheme is matched according to the number of UAVs.

[0024] like Figure 3 The diagram shown is a connection block diagram of a standard configuration based on a cubic load, representing a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints according to an embodiment of the present invention; Figure 3 In this invention, considering that different numbers of drones can generate different standard configurations due to different connection points with the load, in one embodiment of the invention, constructing a standard configuration based on the number of drones may include the following steps: In step S30, the number of drones In this case, the connection points of multiple drones and the load are symmetrically distributed on both sides of the top surface of the cubic load, and all drones are in the same plane to generate a standard dual-drone symmetrical configuration. In step S31, the number of drones In this case, the connection points of multiple drones and the load are distributed at two vertices on the top surface of the cube load and the midpoint of the side between the two vertices relative to the other side, and all drones are on the same plane to generate a standard three-drone triangular configuration. In step S32, the number of drones In the case of multiple drones and loads, the connection points are distributed on the four vertices of the top surface of the cube load, and all drones are on the same plane to generate a standard four-drone square configuration. Alternatively, the connection points are distributed on the midpoints of the four sides of the top surface of the cube load, and all drones are on the same plane to generate a standard four-drone cross configuration. In step S33, the number of drones In the case of multiple drones and loads, the connection points are distributed on the five vertices of the embedded regular pentagon on the top surface of the cube load. All drones are on the same plane to generate a standard configuration of five drones and regular pentagons. Alternatively, the connection points are distributed on the center point and four vertices of the top surface of the cube load. All drones are on the same plane to generate a standard configuration of five drones with a central radial orientation. In step S34, the number of drones In the case of multiple drones and loads, the connection points are distributed on the six vertices of the embedded regular hexagon on the top surface of the cube load. All drones are on the same plane to generate a standard six-drone regular hexagonal configuration. Alternatively, the connection points are distributed on the midpoint of the top surface of the cube load and the five vertices of the embedded regular pentagon. All drones are on the same plane to generate a standard six-drone central radial configuration. In step S35, the number of drones In this scenario, every six drones form a central radial configuration, with all drones at the same height. Traversing all drones, the connection points between the outermost central radial configuration drones and their loads are distributed among the six vertices of the embedded regular hexagons on the top face of the cubic load. The connection points between the innermost layer drones and their loads are distributed among the vertices of the embedded regular hexagons within the outermost layer's embedded regular hexagons, and so on, until all drone and load connection points are distributed, thus generating... The standard configuration of the machine's center radiation.

[0025] A method for constructing standard configurations based on the number of drones has developed a complete gradient scheme for cubic loads. This method precisely matches the optimal configuration based on the number of drones (n=2 to n≥7): symmetrical configurations for two drones and triangular configurations for three drones lay the foundation for stability, while four-, five-, and six-drone systems each provide multiple configuration options (such as square and cross shapes, regular pentagons and central radial shapes) to achieve performance optimization; when n≥7, an innovative multi-layer central radial configuration is adopted, with drones arranged in a hexagonal pattern. Through a systematic configuration library, it ensures that drone swarms of different sizes can achieve optimal stability, load distribution efficiency, and scalability in lifting operations, significantly improving the system's adaptability and overall performance.

[0026] Considering the design of multi-UAV collaborative hoisting system configurations while ensuring safe distances, in one embodiment of the present invention, designing adaptive collision avoidance constraints for all multi-UAV collaborative hoisting system configurations in the standard library may include the following steps: obtaining the position parameters of the UAVs in all multi-UAV collaborative hoisting system configurations based on the minimum safe distance, and designing adaptive collision avoidance constraints for the multi-UAV collaborative hoisting system configurations based on the position parameters. The minimum safe distance is calculated based on factors such as the maximum deployment size of the UAVs, control errors, and external interference, and the precise position parameters of the UAVs in each configuration are determined accordingly. Based on these position parameters, two types of core constraints are designed—a minimum safe distance constraint between UAVs for any configuration, ensuring that the distance between any two UAVs meets dynamic collision avoidance requirements, and a minimum safe distance constraint between layers specifically designed for center-radial configurations.

[0027] like Figure 4 The diagram shown is a connection block diagram illustrating the acquisition of UAV parameters under different standard configurations in a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints according to an embodiment of the present invention; Figure 4 In this invention, considering that the position parameters of the UAVs are based on the minimum safe distance required in the overall configuration, in one embodiment of the invention, obtaining the position parameters of all UAVs in the multi-UAV cooperative hoisting system configuration based on the minimum safe distance may include the following steps: In step S40, the minimum safety distance is defined using formula (1).

[0028] in, For minimum safe distance, This is the maximum unfolding size of the drone. For flight control error, This refers to the pose error caused by external disturbances. For safety margin, For safety factor; In step S41, the origin is defined as the mass point along the length direction of the rod-shaped load, and a one-dimensional coordinate system is constructed along the length direction of the rod-shaped load. In step S42, the position parameters of the UAV on the rod-shaped uniform configuration are obtained using formulas (2)-(3). (2) (3) in, For the first The horizontal coordinates of the drone For the length of the load, The total number of drones, This is the length of the suspension rope; In step S43, the position parameters of the UAV on the rod-shaped non-uniform configuration are obtained using formulas (4)-(6). (4) (5) (6) in, This is the centroid offset. Given the centroid offset position, Let be the number of steps the particle takes to expand to the left and right. For the particle to expand to the left The horizontal coordinates of the connection point between the drone and the load during the flight. For the particle to expand to the right The horizontal coordinates of the connection point between the drone and the load during the flight. This is the step size scaling factor; In step S44, the projection of the center of all configurations of the cube load onto the top surface of the cube load is the same as the initial position of the center of the top surface of the cube load. The center of the configuration is taken as the origin of the two-dimensional inertial coordinate system, and the X-axis and Y-axis of the two-dimensional inertial coordinate system are set to be parallel to the two adjacent sides of the top surface, respectively, to form a two-dimensional inertial coordinate system. In step S45, the position parameters of standard configuration UAVs with a number range of 2-6 are obtained using formulas (7)-(10). (7) (8) (9) (10) in, Let the radius of the minimum circumcircle of the planar figure formed by a UAV with a standard configuration based on minimum safe distance be . For based on An empirical coefficient for the number of drones The length of the suspension rope. Let be the radius of the final circumcircle of the planar figure formed by a standard configuration UAV. For the first The horizontal coordinate of the drone on the X-axis For the first The ordinate of the drone on the Y-axis; In step S46, formulas (11)-(14) are used to obtain... Position parameters of a UAV with a standard configuration radiating from the center of the aircraft. (11) (12) (13) (14) in, For the number of floors, This is the floor function, which rounds up the value to the nearest integer not less than the value within the parentheses. For the first The radius of the circumcircle of the planar shape formed by the drone in the layer. The radius of the final circumcircle of the planar figure formed by a standard configuration UAV is the radius of the innermost circumcircle. For the first The first layer The horizontal coordinate of the drone on the X-axis For the first The first layer The ordinate of the drone on the Y-axis For the first The number of drones on the floor, For the first Interlayer deflection angle.

[0029] The minimum safe distance is defined by comprehensively considering the UAV size, control error, external interference, and safety margin. For rod-shaped loads, the position is calculated in a one-dimensional coordinate system based on the mass distribution characteristics: for uniform distribution, an equidistant symmetrical layout is adopted; for non-uniform distribution, the lifting points are distributed according to the principle of gravitational moment balance based on the centroid offset. For cubic loads, in a two-dimensional inertial coordinate system, the circumcircle radius and coordinates are calculated using a regular polygon configuration based on the number of UAVs (2-6). When the number is ≥7, a layered central radial configuration is adopted, and the positions are distributed layer by layer according to the regular hexagonal pattern. Parametric modeling ensures that all configurations meet the dynamic collision avoidance requirements, improving the system's safety and adaptability, while optimizing the load distribution efficiency.

[0030] like Figure 5 The diagram shown is a connection block diagram of the adaptive collision avoidance constraint of a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints according to an embodiment of the present invention; in Figure 5 In order to achieve distance collision avoidance constraints for UAVs, in one embodiment of the present invention, designing adaptive collision avoidance constraints for the configuration of a multi-UAV cooperative hoisting system based on position parameters may include the following steps: In step S50, adaptive distance constraints between adjacent UAVs are set for all configurations using formulas (15)-(16). (15) (16) in, An adaptive minimum distance constraint between adjacent UAVs. For the first The location parameters of the drone For the first The location parameters of the drone This is the scaling factor; In step S51, formula (17) is used to... The standard configuration with central radiation of the machine sets adaptive distance constraints between adjacent layers. (17) in, For adaptive distance constraints between adjacent layers, This is the radius scaling factor.

[0031] For all configurations, adaptive distance constraints are set between adjacent UAVs: the minimum distance is determined by calculating the position parameters of any two UAVs, and a dynamic scaling factor is introduced. When the measured distance is lower than the safety threshold, the configuration scale is automatically enlarged to ensure that the distance between UAVs always meets the collision avoidance requirements. At the same time, adaptive distance constraints between layers are designed specifically for multi-layer center-radial configurations: the spacing between concentric circle layers is adjusted based on the radius scaling factor to avoid collisions between UAVs due to attitude changes or external interference. By dynamically adjusting the safety boundary by evaluating position parameters in real time, the collision avoidance capability and adaptability of the system in complex environments are significantly improved, while taking into account configuration stability and flight efficiency, effectively reducing the risk of collision.

[0032] like Figure 6 The diagram shown is a connection block diagram of the optimal solution in the configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints according to an embodiment of the present invention; Figure 6 In order to generate the optimal configuration solution and facilitate the subsequent parameter generation of configuration schemes, in one embodiment of the present invention, verifying and evaluating candidate configurations to obtain the optimal configuration solution may include the following steps: In step S60, candidate configurations are screened using multi-level feasibility constraints to obtain the screened configurations; In step S61, a multi-objective cost evaluation method is used to process the filtered configurations and obtain the cost value of each filtered configuration. In step S62, the configuration with the lowest cost after filtering is selected as the current optimal solution.

[0033] Candidate configurations are screened using multi-level feasibility constraints (including geometric and physical constraints) to exclude schemes that do not meet basic security and physical realizability requirements. For the selected configurations, a multi-objective cost evaluation method is used to quantitatively calculate the comprehensive cost of each configuration from three dimensions: stability, redundancy, and efficiency. The configuration with the minimum comprehensive cost value is selected as the current optimal solution. Through a systematic screening and quantitative evaluation mechanism, the optimal configuration is ensured to have the best balance in the three dimensions, which significantly improves the scientific nature of decision-making and the overall effectiveness of the system.

[0034] like Figure 7 The diagram shown is a connection block diagram of a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints, according to an embodiment of the present invention, which uses multi-level feasibility constraints to screen candidate configurations. Figure 7 In order to achieve multi-level screening of candidate configurations, in one embodiment of the present invention, multi-level feasibility constraints are used to screen candidate configurations, and obtaining the screened configurations may include the following steps: In step S70, the candidate configurations are filtered by distance constraints using formula (18) to obtain the first configuration set. (18) in, The first minimum safe distance, For the first The location parameters of the drone For the first Position parameters of the drone; In step S71, the first configuration set is filtered for maximum swing angle constraint using formula (19) to obtain the second configuration set. (19) in, The first maximum swing angle, The maximum outer contour dimension of the load; In step S72, the load-bearing constraints of the second configuration set are filtered using formula (20) to obtain the third configuration set. (20) in, Let be the radius of the circumcircle of the largest shape formed by the UAV in the configuration. This is the capacity factor; In step S73, the third configuration set is used as the final filtered configuration.

[0035] The process of screening candidate configurations using multi-level feasibility constraints first involves distance constraint screening, verifying whether the distance between any two UAVs meets the first minimum safe distance requirement, and eliminating configurations with collision risks. Next, maximum swing angle constraint screening is implemented, calculating the geometric relationship between the load's outer contour dimensions and the minimum safe distance to ensure the load's swing angle remains within a stable threshold during lifting. Finally, load capacity constraint verification is performed, comparing the load dimensions with the capacity ratio of the configuration's circumcircle radius to confirm that the configuration can completely enclose the load. This hierarchical screening mechanism progressively optimizes candidate schemes, ensuring basic safety while also considering motion stability and spatial rationality, significantly improving the reliability and efficiency of configuration selection.

[0036] like Figure 8 The diagram shown is a connection block diagram illustrating the cost-value acquisition of configuration configuration based on a multi-objective cost evaluation method in a configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints, according to an embodiment of the present invention. Figure 8 In order to obtain the cost value of the selected configurations, a decision is made based on the quantified value. In one embodiment of the present invention, a multi-objective cost evaluation method is used to process the selected configurations. Obtaining the cost value of each selected configuration may include the following steps: In step S80, the multi-objective cost function is defined using formula (21). ,(twenty one) in, For a multi-objective cost function, Let the stability cost function be... Let redundancy cost function, Let the efficiency cost function be... To stabilize the weighting coefficients, This is the redundancy weighting coefficient. Efficiency weighting coefficient; In step S81, the stability cost function is constructed using formulas (22)-(24). ,(twenty two) ,(twenty three) ,(twenty four) in, The angle of the suspension rope. For the rotational inertia of the unmanned aerial vehicle system, This is the reference moment of inertia for the unmanned aerial vehicle (UAV) system. For the first The quality of the drone For the first The distance from the drone to the center of the configuration; In step S82, the redundancy cost function is constructed using formulas (25)-(26). (25) (26) in, The number of drones in the central area set in the configuration. The penalty coefficient is... The radius threshold of the central region; In step S83, the efficiency cost function is constructed using formula (27). (27) in, The reference radius for the circumcircle of the UAV planar graphic formed in the configuration; In step S84, the cost of each filtered configuration is obtained based on the multi-objective cost function.

[0037] A multi-objective cost evaluation method is adopted to process the selected configuration configurations. The performance of each configuration is quantified by constructing a comprehensive cost function, which integrates the costs of stability, redundancy and efficiency. The stability cost is differentiated according to the number of UAVs. When there are few UAVs, the lanyard swing angle is used as the indicator, and when there are many UAVs, the system rotational inertia ratio is used to evaluate the anti-interference capability. The redundancy cost reflects the fault tolerance of the configuration by counting the number of UAVs in the central area and applying a penalty coefficient. The efficiency cost measures the maneuverability and energy consumption based on the ratio of the configuration radius to the reference value. The weight coefficient can be dynamically adjusted according to the task priority. In one embodiment of the present invention, the weight coefficient can be obtained by formula (28). (28) in, This is a task priority mode, where, When this is the dominant factor, the system is in a stability-first mode, prioritizing the configuration with the best stability, which is suitable for performing delicate lifting tasks. When redundancy is the dominant factor, the system is in redundancy priority mode, prioritizing the configuration with the highest fault tolerance, which is suitable for performing critical lifting tasks; When this is the dominant factor, the system operates in efficiency-first mode, prioritizing the configuration with the highest efficiency, which is suitable for performing rapid and mobile lifting tasks. When the proportions are balanced, the system is in balanced mode, which is suitable for performing general hoisting tasks.

[0038] like Figure 9The diagram shown is a connection block diagram for generating the final configuration scheme of a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints, according to an embodiment of the present invention; Figure 9 In order to generate the optimal configuration solution and ensure the feasibility of the configuration scheme, in one embodiment of the present invention, verifying the optimal solution and generating the final configuration scheme may include the following steps: In step S90, the configuration with the lowest cost is obtained as the optimal solution; In step S91, the threshold of the first minimum safe distance is reduced to verify the distance constraint of the optimal solution again, and the threshold of the first large swing angle is reduced to verify the maximum swing angle constraint of the optimal solution again. In step S92, it is determined whether the optimal solution passes the verification. In step S93, if the optimal solution passes verification, the final configuration scheme is generated based on the optimal solution; In step S94, if the optimal solution fails to pass verification, the adaptive collision avoidance constraint steps for all multi-UAV collaborative hoisting system configurations in the design standard library are re-executed.

[0039] The configuration with the lowest cost in the multi-objective evaluation is selected as the optimal solution, followed by rigorous secondary constraint verification: stress testing is conducted by reducing the safety distance threshold and swing angle threshold. If the verification passes, a complete configuration scheme including the precise coordinates of the UAV, tether parameters, and safety redundancy is generated based on the solution; if it fails, it is fed back to the adaptive collision avoidance constraint design step for iterative optimization. This mechanism forms a closed-loop quality control, ensuring that the output scheme has both theoretical optimality and engineering feasibility, significantly improving the reliability and safety of the system in real-world scenarios.

[0040] Through the above technical solution, this method achieves intelligent configuration optimization through systematic steps, constructs a standard library of configurations for multi-UAV collaborative hoisting systems, covering various standard configurations for rod-shaped loads and cubic loads, such as uniform and non-uniform mass distribution schemes, ensuring the basic completeness of the configuration design; designs adaptive collision avoidance constraints for all configurations in the standard library, dynamically adjusting the distances between UAVs and between layers based on the minimum safe distance formula (considering UAV size, control error, and external interference) to prevent collision risks; obtains parameters such as UAV model, load mass, and size, and filters theoretically feasible candidate configurations from the standard library; and conducts multi-level feasibility checks (including geometric constraints, maximum swing angle constraints, and load capacity constraints) and multi-objective cost evaluation. The algorithm estimates (balancing stability, redundancy, and efficiency), calculates the comprehensive cost of candidate configurations, and selects the optimal solution. It then performs secondary verification on the optimal solution, such as stress testing with a lowered safety threshold, to ensure robustness before generating the final configuration scheme. A configuration standard library enables unified scheduling of multiple UAV models, adapting to different load shapes and mass distributions and improving resource utilization. An adaptive collision avoidance constraint mechanism adjusts the safety distance according to real-time operating conditions, enhancing the system's anti-interference capability in complex environments. Multi-objective optimization evaluation ensures a balance between stability, fault tolerance, and energy consumption in the configuration, making it suitable for various scenarios such as emergency rescue and industrial hoisting. This method overcomes the limitations of traditional configuration design, which relies on experience and fixed collision avoidance, achieving intelligent, safe, and reliable collaborative hoisting.

[0041] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, and these simple modifications all fall within the protection scope of the present invention. Furthermore, it should be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

Claims

1. A configuration design method for a multi-UAV cooperative hoisting system based on adaptive collision avoidance constraints, characterized in that, The configuration design method includes: Construct a standard library of configurations for multi-UAV collaborative lifting systems; Adaptive collision avoidance constraints for all multi-UAV collaborative hoisting system configurations in the design standard library; Obtain parameters for the drone and payload, and filter candidate configurations from the standard library; Examine and evaluate the candidate configurations to obtain the optimal configuration. Verify the optimal solution and generate the final configuration scheme.

2. The configuration design method according to claim 1, characterized in that, Construct a standard library of configurations for multi-UAV collaborative lifting systems, including: A flexible, massless suspending rope is used to connect the drone's mass point and load; Obtain the shape of the load and determine whether the load is a rod-shaped load or a cubic load; When the load is rod-shaped, determine whether the mass distribution of the load is uniform. When the mass distribution of the load is uniform, the connection points between the multiple UAVs and the load are equidistantly distributed along the length of the rod-shaped load based on centroid symmetry, generating a uniform rod-shaped configuration. When the mass distribution of the load is non-uniform, the connection points between the multiple UAVs and the load are distributed on both sides of the center of mass along the length of the rod-shaped load according to the gravitational torque balance on both sides of the center of mass, generating a rod-shaped non-uniform configuration. Given a cube-shaped load, determine the number of drones. A standard configuration is built based on the number of drones.

3. The configuration design method according to claim 2, characterized in that, Standard configurations are built based on the number of drones, including: Number of drones In this case, the connection points of multiple drones and the load are symmetrically distributed on both sides of the top surface of the cubic load, and all drones are in the same plane to generate a standard dual-drone symmetrical configuration. Number of drones In this case, the connection points of multiple drones and the load are distributed at two vertices on the top surface of the cube load and the midpoint of the side between the two vertices relative to the other side, and all drones are on the same plane to generate a standard three-drone triangular configuration. Number of drones In the case of multiple drones and loads, the connection points are distributed on the four vertices of the top surface of the cube load, and all drones are on the same plane to generate a standard four-drone square configuration. Alternatively, the connection points are distributed on the midpoints of the four sides of the top surface of the cube load, and all drones are on the same plane to generate a standard four-drone cross configuration. Number of drones In the case of multiple drones and loads, the connection points are distributed on the five vertices of the embedded regular pentagon on the top surface of the cube load. All drones are on the same plane to generate a standard configuration of five drones and regular pentagons. Alternatively, the connection points are distributed on the center point and four vertices of the top surface of the cube load. All drones are on the same plane to generate a standard configuration of five drones with a central radial orientation. Number of drones In the case of multiple drones and loads, the connection points are distributed on the six vertices of the embedded regular hexagon on the top surface of the cube load. All drones are on the same plane to generate a standard six-drone regular hexagonal configuration. Alternatively, the connection points are distributed on the midpoint of the top surface of the cube load and the five vertices of the embedded regular pentagon. All drones are on the same plane to generate a standard six-drone central radial configuration. Number of drones In this scenario, every six drones form a central radial configuration, with all drones at the same height. Traversing all drones, the connection points between the outermost central radial configuration drones and their loads are distributed among the six vertices of the embedded regular hexagons on the top face of the cubic load. The connection points between the innermost layer drones and their loads are distributed among the vertices of the embedded regular hexagons within the outermost layer's embedded regular hexagons, and so on, until all drone and load connection points are distributed, thus generating... The standard configuration of the machine's center radiation.

4. The configuration design method according to claim 3, characterized in that, Design adaptive collision avoidance constraints for all multi-UAV collaborative lifting system configurations in the standard library, including: The position parameters of all UAVs in the multi-UAV collaborative hoisting system configuration are obtained based on the minimum safe distance; Adaptive collision avoidance constraints are designed for the configuration of a multi-UAV collaborative hoisting system based on position parameters.

5. The configuration design method according to claim 4, characterized in that, The position parameters of all UAVs in the multi-UAV collaborative lifting system configuration are obtained based on the minimum safe distance, including: The minimum safe distance is defined using formula (1). in, For minimum safe distance, This is the maximum unfolding size of the drone. For flight control error, This refers to the pose error caused by external disturbances. For safety margin, For safety factor; Define the mass point along the length of the rod-shaped load as the origin, and construct a one-dimensional coordinate system along the length of the rod-shaped load; The position parameters of the UAV on the rod-shaped uniform configuration are obtained using formulas (2)-(3). ，（2） ，（3） in, For the first The horizontal coordinates of the drone For the length of the load, The total number of drones, This is the length of the suspension rope; The position parameters of the UAV on the rod-shaped non-uniform configuration are obtained using formulas (4)-(6). ，（4） ，（5） ，（6） in, This is the centroid offset. Given the centroid offset position, Let be the number of steps the particle takes to expand to the left and right. For the particle to expand to the left The horizontal coordinates of the connection point between the drone and the load during the flight. For the particle to expand to the right The horizontal coordinates of the connection point between the drone and the load during the flight. This is the step size scaling factor; Based on the fact that the projection of the center of all configurations of the cube load onto the top surface of the cube load is the same as the initial position of the center of the top surface of the cube load, the center of the configuration is taken as the origin of the two-dimensional inertial coordinate system, and the X-axis and Y-axis of the two-dimensional inertial coordinate system are set to be parallel to the two adjacent sides of the top surface, respectively, to form a two-dimensional inertial coordinate system. The position parameters of standard configuration UAVs with a number range of 2-6 are obtained using formulas (7)-(10). ，（7） ，（8） ，（9） ，（10） in, Let the radius of the minimum circumcircle of the planar figure formed by a UAV with a standard configuration based on minimum safety distance be denoted as . For based on An empirical coefficient for the number of drones The length of the suspension rope. Let be the radius of the circumcircle of the final planar figure formed by a standard configuration UAV. For the first The horizontal coordinate of the drone on the X-axis For the first The ordinate of the drone on the Y-axis; Obtain using formulas (11)-(14) Position parameters of a UAV with a standard configuration radiating from the center of the aircraft. ，（11） ，（12） ，（13） ，（14） in, For the number of floors, This is the floor function, which rounds up the value to the nearest integer not less than the value within the parentheses. For the first The radius of the circumcircle of the planar shape formed by the drone in the layer. The radius of the final circumcircle of the planar figure formed by a standard configuration UAV is the radius of the innermost circumcircle. For the first The first layer The horizontal coordinate of the drone on the X-axis For the first The first layer The ordinate of the drone on the Y-axis For the first The number of drones on the floor, For the first Interlayer deflection angle.

6. The configuration design method according to claim 5, characterized in that, Based on position parameters, design adaptive collision avoidance constraints for the configuration of a multi-UAV collaborative hoisting system, including: Adaptive distance constraints between adjacent UAVs are set for all configurations using formulas (15)-(16). ，（15） ，（16） in, An adaptive minimum distance constraint between adjacent UAVs. For the first The location parameters of the drone For the first The location parameters of the drone This is the scaling factor; Using formula (17) The standard configuration with central radiation of the machine sets adaptive distance constraints between adjacent layers. ，（17） in, For adaptive distance constraints between adjacent layers, This is the radius scaling factor.

7. The configuration design method according to claim 6, characterized in that, Examine and evaluate the candidate configurations to obtain the optimal configuration, including: Candidate configurations are selected by using multi-level feasibility constraints, and the selected configurations are obtained. A multi-objective cost evaluation method is used to process the selected configurations and obtain the cost value of each selected configuration. Select the configuration with the lowest cost after filtering as the current optimal solution.

8. The configuration design method according to claim 7, characterized in that, Candidate configurations are screened using multi-level feasibility constraints to obtain the screened configurations, including: The candidate configurations are filtered by distance constraints using formula (18) to obtain the first configuration set. ，（18） in, The first minimum safe distance, For the first The location parameters of the drone For the first Position parameters of the drone; Formula (19) is used to filter the first configuration set by maximum swing angle constraint to obtain the second configuration set. ，（19） in, The first maximum swing angle, The maximum outer contour dimension of the load; The load-bearing constraints of the second configuration set are filtered using formula (20) to obtain the third configuration set. ，（20） in, Let be the radius of the circumcircle of the largest shape formed by the UAV in the configuration. This is the capacity factor; The third configuration set is used as the final filtered configuration.

9. The configuration design method according to claim 7, characterized in that, A multi-objective cost evaluation method is used to process the selected configurations, and the cost value of each selected configuration is obtained, including: The multi-objective cost function is defined using formula (21). ，（21） in, For a multi-objective cost function, Let the stability cost function be... Let redundancy cost function, Let the efficiency cost function be... To stabilize the weighting coefficients, This is the redundancy weighting coefficient. Efficiency weighting coefficient; The stability cost function is constructed using formulas (22)-(24). ，（22） ，（23） ，（24） in, The angle of the suspension rope. For the rotational inertia of the unmanned aerial vehicle system, This is the reference moment of inertia for the unmanned aerial vehicle (UAV) system. For the first The quality of the drone For the first The distance from the drone to the center of the configuration; The redundancy cost function is constructed using formulas (25)-(26). ，（25） ，（26） in, The number of drones in the central area set in the configuration. The penalty coefficient is... The radius threshold of the central region; The efficiency cost function is constructed using formula (27). ，（27） in, The reference radius for the circumcircle of the UAV planar graphic formed in the configuration; The cost of each filtered configuration is obtained based on the multi-objective cost function.

10. The configuration design method according to claim 8, characterized in that, Verify the optimal solution and generate the final configuration scheme, including: The optimal solution is the configuration with the lowest cost. Lower the threshold of the first minimum safe distance and verify the distance constraint of the optimal solution again; reduce the threshold of the first large swing angle and verify the maximum swing angle constraint of the optimal solution again. Determine whether the optimal solution passes the verification. If the optimal solution passes verification, the final configuration scheme is generated based on the optimal solution; If the optimal solution fails to pass verification, the adaptive collision avoidance constraint steps for all multi-UAV collaborative hoisting system configurations in the design standard library are re-executed.

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