Multi-unmanned aerial vehicle cooperative transportation control method, device, equipment and storage medium

Abstract

The invention provides a multi-unmanned aerial vehicle cooperative transportation control method and device, equipment and a storage medium. A hoisting cluster controlled by the method comprises multiple unmanned aerial vehicles and a load. Each unmanned aerial vehicle is provided with a hanging interface; the plurality of unmanned aerial vehicles and the load are connected into a preset topological structure through the hanging interfaces and the high-strength flexible static ropes; a pulley is embedded into each rope; the method comprises the following steps: determining a tension vector borne by each unmanned aerial vehicle in a hoisting cluster; according to the position and the speed of each unmanned aerial vehicle, determining a speed instruction value of a virtual pilot in the hoisting cluster; determining the adjacency constraint control quantity of each unmanned aerial vehicle according to the speed instruction value; determining the tension compensation control quantity of each unmanned aerial vehicle according to the tension vector; determining an attitude error vector of each unmanned aerial vehicle according to the adjacency constraint control quantity and the tension compensation control quantity; determining the control moment of each unmanned aerial vehicle according to the attitude error vector; and the pose of each unmanned aerial vehicle is adjusted based on the control torque of each unmanned aerial vehicle, and stable lifting flight is achieved.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) collaborative technology, and in particular to a method, apparatus, equipment, and storage medium for multi-UAV collaborative transportation control. Background Technology

[0002] Multi-UAV collaborative transportation technology has broad application prospects in emergency rescue, material transportation and other fields. It can overcome terrain limitations and meet the needs of rapid transportation of heavy or high-value materials (even wounded people) in complex terrain environments (mountains, plateaus, river networks), dangerous environments (battlefields, strong radiation fields) and non-intrusive environments (high-value sites, urban transportation networks, karst landforms).

[0003] Multi-drone collaborative transport refers to the use of multiple drones working together to lift large loads exceeding the weight limit of a single drone. This enables rapid lifting of large loads in the field, overcoming the limitation of single drones in lifting capacity. Individual drones are highly maneuverable and can be used for solo transport or in collaborative operations to lift large loads.

[0004] like Figure 1 As shown, traditional multi-drone collaborative transportation uses a rigid rope structure to directly connect multiple drones to the load; that is, rigid ropes are used to connect the drones and the goods. To maintain the stability of the entire lifting cluster and prevent the goods being lifted from tipping over, the traditional connection method must rely on high-frequency data acquisition between drones, complex collaborative control algorithms, and high-precision flight altitude maintenance.

[0005] Traditional methods of directly connecting multiple drones and payloads via ropes are problematic. Because ropes are under stress and lack flexibility, strict altitude control is required among the drones during flight to ensure even stress distribution. Currently, factors such as RTK (Real-time kinematic) positioning accuracy, drone mechanical control precision, weather conditions, airflow disturbances between drones, and lateral displacement of drones make it impossible to achieve absolute positioning in both position and altitude during multi-drone collaborative operations. This leads to uneven stress on the ropes, and in some cases, individual drones may experience insufficient load on their ropes, causing the entire multi-drone collaborative lifting cluster to collapse due to overload. Summary of the Invention

[0006] To address one of the aforementioned technical deficiencies, this application provides a method, apparatus, device, and storage medium for multi-UAV collaborative transportation control.

[0007] In a first aspect, the application provides a multi-UAV cooperative transportation control method. The hoisting cluster controlled by the method includes multiple UAVs and a load. Each UAV is equipped with a hoisting interface. The multiple UAVs and the load are connected into a preset topological structure through the hoisting interfaces and high-strength flexible static ropes. Pulleys are embedded on each rope.

[0008] The method includes:

[0009] determining the tension vector of each UAV in the hoisting cluster;

[0010] determining the speed command value of the virtual leader in the hoisting cluster according to the position and speed of each UAV;

[0011] determining the adjacency constraint control quantity of each UAV according to the speed command value;

[0012] determining the tension compensation control quantity of each UAV according to the tension vector wherein i is the UAV identifier, n is the total number of UAVs in the hoisting cluster, i = 1, 2, …, n, ΔTC i is the tension compensation control quantity of the i-th UAV, γ is the compensation gain coefficient, F ref is the target tension, F i is the tension vector of the i-th UAV, l i is the direction vector of the rope connected to the i-th UAV, |l i is the length of the rope connected to the i-th UAV;

[0013] determining the attitude error vector of each UAV according to the adjacency constraint control quantity and the tension compensation control quantity ΔTC i

[0014] determining the control moment of each UAV according to the attitude error vector;

[0015] adjusting the pose of each UAV based on the control moment of each UAV.

[0016] Optionally, the determination of the tension vector of each UAV in the hoisting cluster includes:

[0017] determining the tension vector of each UAV in the hoisting cluster according to the following equation set:

[0018]

[0019] wherein i is the UAV identifier, n is the total number of UAVs in the hoisting cluster, i = 1, 2, …, n; j is the UAV identifier, j = 1, 2, …, n, and i ≠ j; F i is the tension vector of the i-th UAV, |F i ​| is the tension of the i-th UAV; F j | is the tension vector of the j-th UAV, |F j | is the tension of the j-th UAV; E i,j Table is whether there is a rope connection between the i-th UAV and the j-th UAV, E i,j =1 is there is a rope connection between the i-th UAV and the j-th UAV; θ i is the angle between the tension of the i-th UAV and the direction of the gravity of the load, θ j is the angle between the tension of the j-th UAV and the direction of the gravity of the load; G is the gravity of the load.

[0020] Optionally, according to the positions and speeds of the UAVs, a speed instruction value of a virtual leader in the hoisting cluster is determined, comprising:

[0021] The speed instruction value of the virtual leader is determined by the formula

[0022] wherein, is the speed instruction value of the virtual leader at the next moment, k p is a proportional gain, k d is a differential gain, x i is the position of the i-th UAV, x vir is the position of the virtual leader at the current moment, v i is the speed of the i-th UAV, v vir is the speed of the virtual leader at the current moment.

[0023] Optionally, according to the speed instruction value, a neighboring constraint control quantity of each UAV is determined, comprising:

[0024] The neighboring constraint control quantity of each UAV is determined by the formula

[0025] wherein, is the neighboring constraint control quantity of the i-th UAV, α is a neighboring weight coefficient, N i is a neighboring UAV set of the i-th UAV, the neighboring UAV set comprises the virtual leader and a UAV having a physical constraint with the i-th UAV, m is a neighboring UAV identifier of the i-th UAV, x m is the position of the neighboring UAV m of the i-th UAV, x i is the position of the i-th UAV.

[0026] Optionally, according to the speed instruction value, a neighboring constraint control quantity of each UAV is determined, comprising:

[0027] The neighboring constraint control quantity of each UAV is determined by the formula ​​​

[0028] wherein, is the adjacency constraint control quantity of the ith UAV, a is an adjacency weight coefficient, N i is the adjacency UAV set of the ith UAV, the adjacency UAV set includes a virtual leader and a UAV having a physical constraint with the ith UAV, m is an adjacency UAV identifier of the ith UAV, x m is the position of the adjacency UAV m of the ith UAV, x i is the position of the ith UAV, is the desired distance difference between the ith UAV and the mth adjacency UAV of the ith UAV.

[0029] Optionally, the total desired force of each UAV is determined according to the adjacency constraint control quantity and the tension compensation control quantity ATC i , and the attitude error vector of each UAV is determined, including:

[0030] The total desired force of each UAV is determined by the formula wherein, U i is the total desired force of the ith UAV, is the adjacency constraint control quantity of the ith UAV, ATC i is the tension compensation control quantity of the ith UAV;

[0031] Based on Newton's second law, the acceleration, velocity, and displacement of each UAV are determined according to the total desired force of each UAV.

[0032] According to the acceleration, velocity, displacement, and angle desired value of each UAV, the current attitude quaternion of each UAV is obtained.

[0033] The attitude error vector of each UAV is determined by the formula

[0034] wherein, is the attitude error vector of the ith UAV, q e is the desired attitude quaternion, q i is the current attitude quaternion of the ith UAV, is a quaternion multiplication operator, and vec is a vector extraction operation.

[0035] Optionally, the control moment of each UAV is determined according to the attitude error vector, including:

[0036] The control moment of each UAV is determined by the formula

[0037] wherein, τ i is the control moment of the ith UAV, k a is an attitude proportional gain,​​ is the attitude error vector of the ith UAV, k ω is the angular velocity proportional gain, is the angular velocity error of the ith UAV;

[0038] is the sliding mode parameter of the ith UAV, or, k s is the sliding mode surface proportional gain, ω i is the current angular velocity of the ith UAV, and are the sliding mode surface matrices of the ith UAV, is the position error vector of the ith UAV, is the derivative of, λ is a positive diagonal matrix; η is the sliding mode robust gain, sgn() is the sign function,

[0039] In a second aspect, the application provides a multi-UAV cooperative transportation control device, which controls a hoisting cluster including multiple UAVs and a load; each UAV is equipped with a hoisting interface; the multiple UAVs and the load are connected into a preset topological structure through the hoisting interfaces and high-strength flexible static ropes; a pulley is embedded on each rope;

[0040] The device includes:

[0041] A first determination module for determining the tension vector borne by each UAV in the hoisting cluster;

[0042] A second determination module for determining the speed command value of a virtual leader in the hoisting cluster according to the positions and speeds of the UAVs;

[0043] A third determination module for determining the adjacency constraint control quantity of each UAV according to the speed command value;

[0044] A fourth determination module for determining the tension compensation control quantity of each UAV according to the tension vector Wherein, i is the UAV identifier, n is the total number of UAVs in the hoisting cluster, i = 1, 2, …, n, ΔTC i is the tension compensation control quantity of the ith UAV, γ is the compensation gain coefficient, F ref is the target tension, F i is the tension vector borne by the ith UAV, l i is the direction vector of the rope connected to the ith UAV, |l i is the length of the rope connected to the ith UAV;

[0045] The fifth determining module is used to determine the adjacent constraint control quantity and the tension compensation control quantity ΔTC. i Determine the attitude error vector of each UAV;

[0046] The sixth determining module is used to determine the control torque of each UAV based on the attitude error vector;

[0047] The control module is used to adjust the attitude of each drone based on the control torque of each drone.

[0048] A third aspect of this application provides an electronic device, comprising:

[0049] Memory;

[0050] Processor; and

[0051] Computer programs;

[0052] The computer program is stored in the memory and configured to be executed by the processor to implement the method described in the first aspect above.

[0053] In a fourth aspect, this application provides a computer-readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement the method described in the first aspect above.

[0054] This application provides a multi-UAV cooperative transportation control method, apparatus, equipment, and storage medium. The method controls a hoisting cluster including multiple UAVs and a payload; each UAV is equipped with a hoisting interface; the multiple UAVs and the payload are connected to each other through the hoisting interface and high-strength flexible static ropes to form a preset topology; pulleys are embedded in each rope; the method includes: determining the tension vector of each UAV in the hoisting cluster; determining the speed command value of the virtual navigator in the hoisting cluster based on the position and speed of each UAV; determining the adjacency constraint control quantity of each UAV based on the speed command value; determining the tension compensation control quantity of each UAV based on the tension vector; determining the attitude error vector of each UAV based on the adjacency constraint control quantity and the tension compensation control quantity; determining the control torque of each UAV based on the attitude error vector; and adjusting the attitude of each UAV based on the control torque of each UAV. The method provided in this application controls a hoisting cluster comprising multiple drones and a payload. The drones and payload are connected in a preset topology via sling interfaces and high-strength, flexible static ropes, with pulleys embedded in each rope. These pulleys can adjust the force distribution between connected drones, improving stability and reducing control complexity. Based on this structure, the attitude of each drone can be adjusted according to the tension acting on it, enabling automatic adjustment of the drones' attitudes for stable hoisting flight, reducing control complexity, and enhancing control robustness. BRIEF DESCRIPTION OF DRAWINGS

[0055] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0056] Figure 1 Structure diagram of a hoisting cluster controlled by a multi- UAV cooperative transportation control method provided by an embodiment of the application;

[0057] Figure 2 Structure diagram of a hoisting cluster controlled by a multi- UAV cooperative transportation control method provided by an embodiment of the application;

[0058] Figure 3 Flowchart of a multi- UAV cooperative transportation control method provided by an embodiment of the application;

[0059] Figure 4 Distributed control algorithm logic diagram of a multi- UAV cooperative transportation control method provided by an embodiment of the application;

[0060] Figure 5 Cooperative trajectory of four UAVs obtained by a multi- UAV cooperative transportation control method provided by an embodiment of the application;

[0061] Figure 6 Adjustment of positions of four UAVs obtained by a multi- UAV cooperative transportation control method provided by an embodiment of the application in the x direction under the action of a change in tension;

[0062] Figure 7 Adjustment of positions of four UAVs obtained by a multi- UAV cooperative transportation control method provided by an embodiment of the application in the y direction under the action of a change in tension;

[0063] Figure 8 Adjustment of positions of four UAVs obtained by a multi- UAV cooperative transportation control method provided by an embodiment of the application in the z direction under the action of a change in tension;

[0064] Figure 9 Diagram showing that a multi- UAV cooperative transportation control method provided by an embodiment of the application maintains the stability of UAV flight;

[0065] Figure 10 Diagram showing the pitch angle pose adjustment capability of a UAV in a multi- UAV cooperative transportation control method provided by an embodiment of the application;

[0066] Figure 11 Diagram showing the roll angle pose adjustment capability of a UAV in a multi- UAV cooperative transportation control method provided by an embodiment of the application;

[0067] Figure 12 A practical application scenario diagram of a multi-unmanned aerial vehicle cooperative transportation control method provided for an embodiment of the present application is shown in FIG. 1.

[0068] Figure 13 A test diagram of a multi-unmanned aerial vehicle cooperative transportation control method provided for an embodiment of the present application is shown in FIG. 2.

[0069] Figure 14 A structural diagram of a multi-unmanned aerial vehicle cooperative transportation control device provided for an embodiment of the present application is shown in FIG. 3.

[0070] Figure 15 A structural diagram of an electronic device provided for an embodiment of the present application is shown in FIG. 4. DETAILED DESCRIPTION

[0071] In order to make the technical solutions and advantages in the embodiments of the present application clearer, the exemplary embodiments of the present application are further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0072] In the process of implementing the present application, the inventors found that the traditional multi-unmanned aerial vehicle and load are directly connected by a rope. Because the rope is under stress and does not have ductility, it is required to strictly maintain the flight height between each unmanned aerial vehicle during flight in order to evenly distribute the stress. At present, affected by RTK (Real-time kinematic, real-time difference) positioning accuracy, unmanned aerial vehicle mechanical control accuracy, weather reasons, airflow disturbance between multiple machines, unmanned aerial vehicle lateral displacement and many other factors, the position and height cannot be absolutely positioned during the multi-unmanned aerial vehicle cooperative operation process, causing uneven stress between the ropes, and even the situation that the hoisting ropes of individual unmanned aerial vehicles are not under stress. After the remaining unmanned aerial vehicles are overloaded, the hoisting cluster collapses, and the entire multi-unmanned aerial vehicle cooperative hoisting cluster collapses.

[0073] To solve the above problems, the embodiment of the present application provides a multi-unmanned aerial vehicle cooperative transportation control method, device, equipment and storage medium. The hoisting cluster controlled by the method comprises multiple unmanned aerial vehicles and a load. Each unmanned aerial vehicle is equipped with a hanging interface. The multiple unmanned aerial vehicles and the load are connected into a preset topological structure through the hanging interfaces and high-strength flexible static ropes. A pulley is embedded on each rope. The method comprises the following steps: determining the tension vector borne by each unmanned aerial vehicle in the hoisting cluster; determining the speed instruction value of a virtual leader in the hoisting cluster according to the position and speed of each unmanned aerial vehicle; determining the adjacency constraint control quantity of each unmanned aerial vehicle according to the speed instruction value; determining the tension compensation control quantity of each unmanned aerial vehicle according to the tension vector; determining the attitude error vector of each unmanned aerial vehicle according to the adjacency constraint control quantity and the tension compensation control quantity; determining the control moment of each unmanned aerial vehicle according to the attitude error vector; and adjusting the pose of each unmanned aerial vehicle based on the control moment of each unmanned aerial vehicle. The hoisting cluster controlled by the method provided by the present application comprises multiple unmanned aerial vehicles and a load. The multiple unmanned aerial vehicles and the load are connected into a preset topological structure through the hanging interfaces and high-strength flexible static ropes, and a pulley is embedded on each rope. The pulley can adjust the force borne between the two unmanned aerial vehicles connected by the pulley, improve the stability, and reduce the control difficulty. Based on the structure, the pose of each unmanned aerial vehicle can be adjusted according to the tension borne by each unmanned aerial vehicle in the hoisting cluster. The pose of each unmanned aerial vehicle can be automatically adjusted to realize stable hoisting flight, reduce the control complexity, and enhance the robustness of control.

[0074] The embodiment provides a multi-unmanned aerial vehicle cooperative transportation control method. The hoisting cluster controlled by the method comprises multiple unmanned aerial vehicles and a load. Each unmanned aerial vehicle is equipped with a hanging interface. The multiple unmanned aerial vehicles and the load are connected into a preset topological structure through the hanging interfaces and high-strength flexible static ropes. A pulley is embedded on each rope. As shown in Figure 2 the hoisting cluster comprises four unmanned aerial vehicles and a load. Two of the unmanned aerial vehicles are connected through the hanging interfaces and high-strength flexible static ropes, the other two unmanned aerial vehicles are connected through the hanging interfaces and high-strength flexible static ropes, and the pulleys on the ropes between the unmanned aerial vehicles are connected with the load through the pulleys.

[0075] The shape of the topological structure can be a polygonal shape, or a tree shape, or a radial shape, etc. The topological structure can be preset according to an actual control task.

[0076] In addition, according to the actual topological structure, in addition to the ropes with embedded pulleys between the unmanned aerial vehicles and between the unmanned aerial vehicles and the load, there can also be ropes with embedded pulleys between the pulleys and between the pulleys and the unmanned aerial vehicles. The embodiment and subsequent embodiments do not limit the shape of the topological structure, the number of ropes, and the objects connected at both ends of each rope.

[0077] That is, the force distribution of the rope tension between the unmanned aerial vehicle and the load is achieved through single-stage or multi-stage pulleys.

[0078] Referring to Figure 3 The implementation process of the multi-unmanned aerial vehicle cooperative transportation control method provided in this embodiment is as follows:

[0079] 301, determining the tension vectors of each unmanned aerial vehicle in the hoisting cluster.

[0080] In a specific implementation, the hoisting cluster controlled by the method provided in this embodiment adopts a rigid rope structure in which multiple unmanned aerial vehicles are directly connected to the load, or a flexible connection structure in which the unmanned aerial vehicles are connected to each other by ropes, and the load is hung by a movable pulley on the rope. In step 301, the tension vectors of each unmanned aerial vehicle in the hoisting cluster can be determined according to the rope topology structure, the weight of the load, the hoisting pose of the unmanned aerial vehicle, and the self-balancing of the tension of the same-stage rope.

[0081] For example, the tension vectors of each unmanned aerial vehicle in the hoisting cluster are determined according to the following equation set:

[0082]

[0083] Where i is the unmanned aerial vehicle identifier, n is the total number of unmanned aerial vehicles in the hoisting cluster, i = 1, 2, …, n.

[0084] j is the unmanned aerial vehicle identifier, j = 1, 2, …, n, and i ≠ j.

[0085] F i is the tension vector of the i-th unmanned aerial vehicle (i.e., the current tension vector of the i-th unmanned aerial vehicle), |F i | is the tension of the i-th unmanned aerial vehicle (i.e., the current tension of the i-th unmanned aerial vehicle).

[0086] F j is the tension vector of the j-th unmanned aerial vehicle (i.e., the current tension vector of the j-th unmanned aerial vehicle), |F j | is the tension of the j-th unmanned aerial vehicle (i.e., the current tension of the j-th unmanned aerial vehicle).

[0087] E i,j is whether there is a rope connection between the i-th unmanned aerial vehicle and the j-th unmanned aerial vehicle, E i,j = 1 indicates that there is a rope connection between the i-th unmanned aerial vehicle and the j-th unmanned aerial vehicle, E i,j = 0 indicates that there is no rope connection between the i-th unmanned aerial vehicle and the j-th unmanned aerial vehicle.

[0088] θ iLet θ be the angle between the pulling force of the i-th UAV and the gravitational direction of the load (i.e., the current angle between the pulling force of the i-th UAV and the gravitational direction of the load). j Let G be the angle between the pulling force of the j-th UAV and the direction of gravity of the load (i.e., the current angle between the pulling force of the j-th UAV and the direction of gravity of the load). Let G be the gravity of the load.

[0089] Through |F i |=|F j This demonstrates that the pulling force of any two drones is the same. (Through F) i sinθ i +F j sinθ j =0,E i,j =1 indicates that if there is a rope connection between the i-th drone and the j-th drone, then F i sinθ i +F j sinθ j =0. Passed This demonstrates that the sum of the pulling forces of all the drones is equal in magnitude and opposite in direction to the weight of the load.

[0090] by Figure 2 Taking the hoisting cluster as an example, if the load's gravity is 1000 Newtons, the tensile force on each drone in the hoisting cluster can be determined according to the following set of equations:

[0091]

[0092] The tension vector of each UAV in the hoisting cluster obtained through step 301 is the expected tension vector of the rope acting on each UAV in the current state.

[0093] 302. Determine the speed command value of the virtual navigator in the hoisting cluster based on the position and speed of each drone.

[0094] A virtual navigator can be set up, with its initial position being the center point of all drones in the slinglift cluster. Subsequently, step 302 determines the speed command value for the next moment based on the current position, and then controls the movement of the virtual navigator via the speed command, achieving real-time changes in the virtual navigator's position.

[0095] That is, the virtual leader is not a certain entity UAV in the cluster, but a virtual center point of the cluster. It is equivalent to finding a center according to the desired formation configuration of the cluster, establishing the position and velocity relationship between the entity aircraft in the cluster and the center, and then controlling the velocity of the center point at each time through step 302 to realize the position change of the center point at each time (i.e. the motion of the center point), and then control the position change of each entity UAV in the cluster (i.e. the motion of each entity UAV).

[0096] The speed command value of the virtual leader is determined by the formula .

[0097] Where i is the UAV identifier, n is the total number of UAVs in the cluster, i = 1, 2, …, n, is the speed command value of the virtual leader at the next time, with the unit of m / s (meters / second). represents the speed that the virtual leader should have at the next time, used to guide the motion direction and speed of the UAVs in the entire cluster.

[0098] k p is the proportional gain (i.e. position error weight). k p determines the correction strength of the method provided by the embodiment to the position deviation, k p the larger, the more sensitive the response of each UAV in the cluster to the position deviation, the faster the convergence speed, but too large may cause the cluster to shake, so the value can be selected according to the maximum position deviation input by the user, such as a cluster with a formation distance of 5m, the user input maximum position deviation is 0.2m deviation, when 4.8m<‖x i -x vir ‖<5.2m, the value of k p can be located in 0.5~1.5, when ‖x i -x vir ‖≤4.8m, or, ‖x i -x vir ‖≥5.2m, the value of k p can be located in 2.5~4.

[0099] k d is the differential gain (i.e. speed error weight), which provides damping effect to suppress the shaking of the cluster caused by k p too large, and enhances stability, such as setting the ratio of k p to k d between 5:1 and 1:1.

[0100] x i is the position of the i-th UAV (unit: m), x viris the position of the virtual leader at the current time (unit: m). The position in this embodiment is the position in the global coordinate system, such as x i may be obtained according to the coordinate value of the ith unmanned aerial vehicle in the global coordinate system. In a specific application, the global coordinate system can refer to a general coordinate system such as the WGS84 coordinate system, the ENU coordinate system, and the like, three coordinate axes are established, the initial position of any unmanned aerial vehicle is taken as a coordinate origin, and a coordinate system expressed in international units “m” is established. The general coordinate system such as the WGS84 coordinate system uses longitude and latitude values, and when the coordinate origin is obtained, any point in the general coordinate system can be converted into an international unit “m” by using an existing method. In addition, the position herein is a current position, that is, x i is the current position of the ith unmanned aerial vehicle, x vir is the current position of the virtual leader.

[0101] v i is the speed of the ith unmanned aerial vehicle (unit: m / s), v vir is the speed of the virtual leader at the current time (unit: m / s). In addition, v i is the actual speed of the ith unmanned aerial vehicle at the current time, v vir is the actual speed of the virtual leader at the current time.

[0102] The speed difference and the position difference are added to form a state space, the cooperative control of the unmanned aerial vehicle cluster is realized, and the unmanned aerial vehicles can fly according to a certain formation and movement requirement. For the virtual leader, k p is a weight value (dimension of time inverse) and k d is a dimensionless weight value, which are adjusted to the same dimension as the control instruction (speed dimension).

[0103] Taking the unmanned aerial vehicle cluster shown in FIG. 1 as an example, k p = 1.2, k d = 0.8, Figure 2

[0104] 303, the adjacent constraint control quantity of each unmanned aerial vehicle is determined according to the speed instruction value.

[0105] If it is not desired that the unmanned aerial vehicles change the formation configuration, the adjacent constraint control quantity of each unmanned aerial vehicle can be determined by formula If it is desired that the unmanned aerial vehicles change the formation configuration, the adjacent constraint control quantity of each unmanned aerial vehicle can be determined by formula .

[0106] ​For example, if a user confirms the need to change the formation configuration, a change configuration instruction is transmitted. The method provided in the embodiment receives the instruction, determines that the unmanned aerial vehicle is expected to change the formation configuration, and determines the adjacency constraint control quantity of each unmanned aerial vehicle through the formula If the instruction is not received, it is determined that the unmanned aerial vehicle is not expected to change the formation configuration, and the adjacency constraint control quantity of each unmanned aerial vehicle is determined through the formula The adjacency constraint control quantity of each unmanned aerial vehicle is determined.

[0107] For another example, if the hoisting cluster has the capability of automatically identifying obstacles, although the method provided in the embodiment does not receive the change configuration instruction, when the hoisting cluster identifies an obstacle through the capability of automatically identifying obstacles and needs to avoid the obstacle, it is still determined that the unmanned aerial vehicle is expected to change the formation configuration, and the adjacency constraint control quantity of each unmanned aerial vehicle is determined through the formula If the change configuration instruction is not received, and the hoisting cluster does not identify an obstacle through the capability of automatically identifying obstacles and does not need to avoid the obstacle, it is determined that the unmanned aerial vehicle is not expected to change the formation configuration, and the adjacency constraint control quantity of each unmanned aerial vehicle is determined through the formula The adjacency constraint control quantity of each unmanned aerial vehicle is determined.

[0108] wherein, is the adjacency constraint control quantity of the ith unmanned aerial vehicle, and the unit is N (Newton).

[0109] α is an adjacency weight coefficient, α is a positive real number with a value of 0.1 to 1, and in the formula , α is a proportional gain and determines the influence weight of the position error between the adjacent unmanned aerial vehicles on the control quantity, and the physical meaning of α is essentially a virtual spring stiffness coefficient. When α is relatively large, the hoisting cluster behaves like a "rigid connection", and the position constraint between the unmanned aerial vehicles is strong. When α is relatively small, the hoisting cluster behaves like a "flexible connection", and a certain relative motion is allowed. In the specific implementation, the value of α can be adjusted according to the task situation. For example, in the uniform hoisting stage, α takes a medium value (such as 0.4 to 0.6), the stability and energy consumption are considered, α is reduced (such as 0.2 to 0.4) in the acceleration / turning stage, and α is increased (such as 0.8 to 1.0) in the obstacle avoidance / topology reconstruction stage, so that the hoisting cluster is not scattered in the obstacle avoidance process.

[0110] N i is the set of adjacent unmanned aerial vehicles of the ith unmanned aerial vehicle, and the set of adjacent unmanned aerial vehicles includes a virtual leader and unmanned aerial vehicles that have physical constraints with the ith unmanned aerial vehicle. For example, the hoisting cluster shown in FIG. Figure 2 includes the two unmanned aerial vehicles on the left and right sides closest to the unmanned aerial vehicle and the virtual leader, and does not include the unmanned aerial vehicles in the diagonal position. Figure 2 In the formula, each entity unmanned aerial vehicle has a virtual physical constraint with the virtual leader.

[0111] m is the adjacent unmanned aerial vehicle identifier of the ith unmanned aerial vehicle, x m is the position of the adjacent unmanned aerial vehicle m of the ith unmanned aerial vehicle (unit: m), x i is the position of the ith unmanned aerial vehicle (unit: m).

[0112] If the adjacent unmanned aerial vehicle is a virtual leader, the position of the virtual leader is obtained by For example, the position of the virtual leader is obtained by integrating the value of The position is obtained by integrating the value. Since the unmanned aerial vehicle has a virtual connection relationship with the virtual leader, the value obtained by integration will be used to calculate the adjacent constraint term, that is, the position is used as a reference value for all entity unmanned aerial vehicles in the adjacent constraint control quantity. If the adjacent unmanned aerial vehicle is an entity unmanned aerial vehicle, the position of the entity unmanned aerial vehicle is the current position of the entity unmanned aerial vehicle.

[0113] The state space composed of the sum of the speed difference and the position difference can realize the cooperative control of the multi-unmanned aerial vehicle hoisting cluster, so that the unmanned aerial vehicles can fly in a certain formation and according to the movement requirements. For each unmanned aerial vehicle, the adjacent weight coefficient a (dimension kg / N 2 ) and the dimension of the adjusted adjacent constraint control quantity.

[0114] is the expected distance difference between the ith unmanned aerial vehicle and the mth adjacent unmanned aerial vehicle of the ith unmanned aerial vehicle, The role of is to give an additional distance limit, so that the unmanned aerial vehicles can change the configuration. The value of can be obtained from the configuration change instruction (that is, the configuration change instruction includes the value of ), or can be obtained from the automatic obstacle recognition capability (that is, through the automatic obstacle recognition capability, the existence of obstacles is obtained, and the value of is also obtained).

[0115] In addition, if the mth unmanned aerial vehicle is directly connected to the ith unmanned aerial vehicle through a rope (that is, there is a rope connection between the ith unmanned aerial vehicle and the mth unmanned aerial vehicle), the mth unmanned aerial vehicle is the adjacent unmanned aerial vehicle of the ith unmanned aerial vehicle. The unit of the position of the mth unmanned aerial vehicle is consistent with the unit of the position of the ith unmanned aerial vehicle.

[0116] Each unmanned aerial vehicle has an adjacent constraint control quantity, and through the adjacent constraint control quantities of each unmanned aerial vehicle, global stability can be ensured, fault tolerance and scalability of the hoisting cluster can be improved, and distributed cooperation can be realized.

[0117] Taking the hoisting cluster shown in Figure 2 and a = 0.5 as an example,

[0118] 304, determine the tension compensation control quantity of each unmanned aerial vehicle according to the tension vector.​​

[0119] The tension compensation control amount of each drone can be determined according to the pulling force

[0120] Wherein, i is the drone identifier, n is the total number of drones in the hoisting cluster, i = 1, 2, …, n, ΔTC i is the tension compensation control amount of the i-th drone (unit: N).

[0121] γ is the compensation gain coefficient, γ is the proportional coefficient for converting the tension error into the control force increment, and the core function is to adjust the response speed and stability of the tension balance, which can be set according to the task scene, such as light load, small wind speed, γ is taken as 0.1-0.3, and if heavy load, large wind speed, γ is taken as 0.3-0.6.

[0122] F ref is the target tension (unit: N), which needs to be calculated according to the total weight of the load, the user setting or the adaptive solution of the formation configuration, and the number of drones. Generally speaking, the target tension can be determined after a given working condition, such as F ref = 250 N.

[0123] F i is the pulling force vector of the i-th drone, which is obtained in step 301.

[0124] l i is the direction vector of the rope connected to the i-th drone (unit: m), l i describes the vector of the space direction of the rope connected to the i-th drone, and the core function is to ensure that the direction of the compensation control amount is consistent with the direction of the rope tension. The direction of the pulling force is determined by the coordinate difference calculation or the pulling force measured by the six-dimensional force sensor. The unit dimension here is m, which is matched with |l i |to form a dimensionless direction.

[0125] |l i |is the length of the rope connected to the i-th drone (unit: m).

[0126] It should be noted that l i is the pulling force direction of the rope connected to the i-th drone, not the gravity direction of the rope. Because the pulling force direction of the rope is collinear with the rope, l i here is the "direction vector of the rope".

[0127] l i is a numerical value of the direction vector of the rope length, which represents the direction of the dimensionless unit vector. When determining the tension compensation control amount ΔTC i , the tension compensation control amount ΔTC ithe value of the number has no effect, essentially, removing (i.e. ΔTC i = γ(F ref - F i )) does not affect the value of the tension of the single rope, considering that in the control algorithm of the physical model γ(F ref - F i ) must be multiplied by the direction vector of the tension, therefore, the tension compensation control quantity ΔTC i thus obtained complies with the physical description of the system dynamics.

[0128] With the cluster of cranes shown in Figure 2 , γ = 0.3, F ref = 250 N, as an example,

[0129] 305, the attitude error vector of each unmanned aerial vehicle is determined according to the adjacency constraint control quantity and the tension compensation control quantity ΔTC i .

[0130] 1. The total expected force of each unmanned aerial vehicle is determined by the formula .

[0131] wherein U i is the total expected force of the i-th unmanned aerial vehicle, is the adjacency constraint control quantity of the i-th unmanned aerial vehicle (obtained in step 303), ΔTC i is the tension compensation control quantity of the i-th unmanned aerial vehicle (obtained in step 304).

[0132] 2. Based on Newton's second law, the acceleration, speed and displacement of each unmanned aerial vehicle are determined according to the total expected force of each unmanned aerial vehicle.

[0133] For example, based on Newton's second law F = ma, U i is taken as F in Newton's second law, the acceleration is solved by force U i using the existing known method, and the speed and displacement are obtained by continuous integration.

[0134] 3. The current attitude quaternion of each unmanned aerial vehicle is obtained according to the acceleration, speed, displacement and angle expected value of each unmanned aerial vehicle.

[0135] The current attitude quaternion of each unmanned aerial vehicle can be obtained according to the acceleration, speed, displacement and angle expected value of each unmanned aerial vehicle through the quaternion conversion rule commonly used in the field of unmanned aerial vehicle control.

[0136] Quaternion is a professional expression of attitude in the field of UAV control. Its form is Q = [Q0, Q1, Q2, Q3]. Q0 is the rotation angle (scalar), and Q1-Q3 is the rotation axis (vector). Through the common knowledge in the field of UAV control, quaternion can be converted into the numerical value of three-axis rotation angle.

[0137] 4. The attitude error vector of each UAV is determined by the formula

[0138] Wherein, is the attitude error vector of the i-th UAV (in quaternion form).

[0139] q e is the expected attitude quaternion, q e is generated by the virtual leader and tension balance control through the expected attitude angle conversion.

[0140] q i is the current attitude quaternion of the i-th UAV, q i is obtained by converting the actual attitude angle at the current time. In the specific implementation, each UAV is configured with an attitude sensor, such as an IMU (Inertial Measurement Unit), and the actual attitude angle at the current time can be obtained through the IMU of the UAV.

[0141] is the quaternion multiplication operator.

[0142] vec is the extraction vector operation, that is, extracting the vector part of the quaternion. For example, if the quaternion is [Q0, Q1, Q2, Q3], vec can extract the last three elements (i.e. Q1-Q3).

[0143] On the basis of the conventional quaternion error calculation , the antisymmetric term is introduced to offset the attitude mutation and increase the stability during large-angle adjustment.

[0144] 306. The control torque of each UAV is determined according to the attitude error vector.

[0145] The control torque of each UAV can be determined by the formula

[0146] Wherein, i is the control torque of the i-th UAV (unit: N·m).

[0147] k a is the attitude proportional gain, k a is used to measure the contribution of attitude deviation to the control torque, k a ​​The greater the correction of the attitude deviation, according to the task scene setting, the value range can be 10-50. For example, for heavy load, stable lifting scene, k a can be taken 10-20.

[0148] is the attitude error vector of the ith unmanned aerial vehicle, which is obtained in step 305.

[0149] k ω is the angular velocity proportional gain, k ω is used to provide damping action, k ω The greater the inhibition of the angular velocity deviation, according to the task scene setting, the value range is 1-10. For example, for heavy load, stable lifting scene, k ω can be taken 5-10.

[0150] is the angular velocity error of the ith unmanned aerial vehicle, with the unit of rad / s (radian / second). is the difference between the expected angular velocity and the actual angular velocity of the ith unmanned aerial vehicle, reflecting the degree of deviation of the rotation speed of the ith unmanned aerial vehicle from the target. Wherein, the expected angular velocity of the ith unmanned aerial vehicle can be calculated based on the formula , which is a common knowledge in the field of unmanned aerial vehicle control. The actual angular velocity of the ith unmanned aerial vehicle can be obtained by the IMU of the unmanned aerial vehicle.

[0151] is the sliding mode parameter of the ith unmanned aerial vehicle, if the unmanned aerial vehicle is not expected to transform the formation configuration, or if the unmanned aerial vehicle is expected to transform the formation configuration,

[0152] For example, the user confirms the need to transform the formation configuration, and transmits an instruction to transform the configuration, the method provided in the embodiment receives the instruction, determines that the unmanned aerial vehicle is expected to transform the formation configuration, and can determine the control torque of each unmanned aerial vehicle through the formula If the instruction is not received, it is determined that the unmanned aerial vehicle is not expected to transform the formation configuration, and the control torque of each unmanned aerial vehicle can be determined through the formula .

[0153] For another example, if the lifting cluster has the ability to automatically identify obstacles, although the method provided in the embodiment does not receive the instruction to transform the configuration, when the lifting cluster identifies an obstacle through its ability to automatically identify obstacles and needs to avoid the obstacle, it is still determined that the unmanned aerial vehicle is expected to transform the formation configuration, and the control torque of each unmanned aerial vehicle can be determined through the formula The control moment of each UAV is determined. If no instruction of changing formation is received and no obstacle is identified by the ability of the cluster to automatically identify obstacles, no obstacle avoidance is needed, it is determined that the UAVs do not need to change formation, and the control moment of each UAV can be determined by the formula The control moment of each UAV is determined.

[0154] k s is the proportional gain of the sliding mode surface.

[0155] ω i is the current angular velocity of the i-th UAV (in rad / s).

[0156] is the sliding mode surface matrix of the i-th UAV, which is determined by Chattering of the cluster can be inhibited.

[0157] is the position error vector of the i-th UAV (where the position error can be obtained by subtracting the actual position from the desired position), is the derivative of .

[0158] λ is a positive diagonal matrix, the value of each element is inversely proportional to the moment of inertia of the corresponding axis of the UAV, and the moment of inertia is a fixed parameter tested when the UAV is manufactured.

[0159] If the design method of changing formation is used in the design of the aforementioned neighborhood constraint control amount, the rapid convergence of the control effect needs to be considered when generating the control moment of each UAV to ensure that the change of the formation can be quickly tracked, and at this time, the chattering phenomenon is acceptable, so the formula The control moment is generated.

[0160] η is the sliding mode robust gain, which ensures that the cluster still converges to the sliding mode surface quickly in the presence of disturbances (such as wind, parameter errors), and the value of this variable is proportional to the environmental disturbance in the actual task, for example, when the weather is very good, η can be taken as 1-2, and when the wind disturbance is very large, η can be taken as 5-10.

[0161] sgn() is a sign function, which outputs a unit vector in the same direction as , producing a discontinuous control amount and forcing the cluster to move along the sliding mode surface.

[0162] is the sliding mode surface matrix of the i-th UAV,

[0163] In the determination process of the control moment, the attitude, angular velocity, and sliding mode surface errors are fully considered, and the dimensions of each type of error are determined by k a , kω and k s Or η can be adjusted.

[0164] by Figure 2 The hoisting cluster shown, k a =30,k ω For example, =5

[0165] 307. Adjust the attitude of each UAV based on the control torque of each UAV.

[0166] For example, flight operations are conducted based on the onboard control module of the UAV, with sliding mode control acting on the UAV torque controller. Therefore, through steps 301 to 307, the magnitude of the tension on each UAV can be changed by attitude adjustment, transferring part of the load caused by inaccurate relative attitude to other UAVs. The rope system automatically balances the forces between the UAVs, preventing the collapse of the hoisting cluster due to overload of a single UAV.

[0167] Steps 301 to 307 can control each UAV to change its posture to generate different tension changes, which is equivalent to transferring part of the load to other UAVs.

[0168] The lifting cluster controlled by the method provided in this embodiment can automatically adjust rope tension according to the tensile requirements of the desired configuration, reducing the load distribution error rate. Furthermore, the buffering effect of the flexible ropes further enhances the wind resistance of the lifting cluster. In addition, the number of drones in the lifting cluster can be flexibly adjusted to adapt to different weight class load requirements. In practical implementation, the cost is significantly reduced by simplifying the control accuracy requirements.

[0169] Throughout the hoisting process, the method provided in this embodiment can automatically adjust the force on each rope to achieve automatic force balance among multiple machines, thereby improving the fault tolerance and robustness of the hoisting cluster, while reducing the requirements for the attitude control accuracy of the UAV.

[0170] The relationship between the UAV tension compensation control quantity, the virtual navigator speed command value, the adjacency constraint control quantity, the attitude error vector, the control torque, and the tension force on each UAV in the method provided in this embodiment (i.e., the distributed control algorithm logic of the method provided in this embodiment) is as follows: Figure 4 As shown.

[0171] like Figures 5 to 8 This demonstrates the use of distributed control methods (i.e. Figure 4 The logic described above refers to the simulation results obtained in a simulation scenario. Figure 5 It is evident that the drone has achieved attitude adjustment. Figures 6 to 8 yes Figure 4In the corresponding scene, the position of each unmanned aerial vehicle changes, and the change in tension leads to a change in the relative position between unmanned aerial vehicles. As can be seen, in the case of a change in tension, the relative positions of each unmanned aerial vehicle change constantly. Among them, Figure 6 is the case of the x direction, Figure 7 is the case of the y direction, Figure 8 is the case of the z direction.

[0172] Figure 9 The stability of the sliding mode control is shown. As the control proceeds, the error between the attitude angle of the unmanned aerial vehicle and the balance attitude angle required for force balance can be stabilized at about 0.004 rad, i.e., 0.5°. It can be seen that the method of the embodiment can maintain the flight stability of the unmanned aerial vehicle.

[0173] Figure 10 and Figure 11 The adjustment capability of the method provided by the embodiment for the unmanned aerial vehicle pose is shown. Among them Figure 10 shows the pitch angle pose adjustment capability, Figure 11 shows the roll angle pose adjustment capability. The method provided by the embodiment realizes the adjustment of the pose of the unmanned aerial vehicle through a closed-loop control chain of tension-attitude: change in load tension → change in force of the unmanned aerial vehicle → change in pose → distributed control of position → sliding mode control of attitude → control torque τ i adjustment → change in motor controlled quantity (such as speed / tilt angle) → adjustment of pose.

[0174] In the method provided by the embodiment, each unmanned aerial vehicle adopts a distributed control method for dynamic balance, automatically adjusts the pose to achieve stable hoisting flight.

[0175] As the tension compensation control quantity of each unmanned aerial vehicle, the speed instruction value of the virtual leader, the adjacency constraint control quantity of each unmanned aerial vehicle, the attitude error vector of each unmanned aerial vehicle, and the control torque of each unmanned aerial vehicle are input into the meter-level positioning accuracy controller, the meter-level positioning accuracy controller can adjust the pose of the unmanned aerial vehicle based on the tension compensation control quantity of each unmanned aerial vehicle, the speed instruction value of the virtual leader, the adjacency constraint control quantity of each unmanned aerial vehicle, the attitude error vector of each unmanned aerial vehicle, and the control torque of each unmanned aerial vehicle, and then transfer part of the load generated due to the inaccurate relative pose to other unmanned aerial vehicles, automatically balance the forces between the unmanned aerial vehicles by using the ropes, and prevent the collapse caused by the overload of a single machine.

[0176] This can enable each unmanned aerial vehicle to dynamically balance, automatically adjust the pose, and achieve stable hoisting flight.

[0177] The method provided in the embodiment controls the hoisting cluster to change the rope connection mode of the unmanned aerial vehicle, that is, the hoisting cluster controlled by the method includes multiple unmanned aerial vehicles and a load, each unmanned aerial vehicle is provided with a hanging interface, the multiple unmanned aerial vehicles and the load are connected into a preset topological structure through the hanging interfaces and high-strength flexible static ropes, and a pulley is embedded on each rope. Based on the preset topological structure, the unmanned aerial vehicles in the hoisting cluster controlled by the method are connected through the ropes, the pulley is embedded on the rope, the force balance between the unmanned aerial vehicles is achieved by using the pulley, the stability of the hoisting cluster is improved, and the control difficulty of the hoisting cluster is reduced. In addition, after the new connection mode is adopted, the unmanned aerial vehicles participating in the cooperative hoisting need to maintain a position and a height (meter level), the force of each unmanned aerial vehicle is automatically distributed by the rope system, the force balance between the unmanned aerial vehicles is automatically adjusted, and the stability of the hoisting cluster is ensured.

[0178] The multi-unmanned aerial vehicle cooperative transportation control method provided in the embodiment can solve the stability problem of the multi-unmanned aerial vehicle cooperative hoisting of a large load. The multi-unmanned aerial vehicle cooperative transportation control method provided in the embodiment realizes the multi-unmanned aerial vehicle cooperative hoisting of a large load through the self-distribution of the force by the pulley. The pulley is used as a hardware to form a force self-distribution device installed on the rope, and the rope is connected with the unmanned aerial vehicle or other pulleys at both ends. The automatic balance of the rope force is realized by the pulley, and a traditional complex and high-precision force sensor is not needed. On the basis of the force self-distribution capability of the pulley, each unmanned aerial vehicle can be dynamically balanced through the multi-unmanned aerial vehicle cooperative transportation control method provided in the embodiment, and the pose is automatically adjusted to realize stable hoisting flight. The multi-unmanned aerial vehicle cooperative transportation control method provided in the embodiment reduces the control complexity of the hoisting cluster, enhances the robustness of the hoisting cluster, and supports modular expansion. The method is suitable for the rapid hoisting of heavy materials with a load limit exceeding the single unmanned aerial vehicle in the field. Therefore, the multi-unmanned aerial vehicle cooperative transportation control method provided in the embodiment is a multi-unmanned aerial vehicle cooperative hoisting distributed control method based on adaptive dynamic force feedback. In the multi-unmanned aerial vehicle cooperative transportation control method provided in the embodiment, each unmanned aerial vehicle adopts a new distributed control method to dynamically balance and automatically adjust the pose to realize stable hoisting flight, thereby solving the problem of limited position tracking control capability of the load in the existing scheme, improving the stability and safety of transportation, reducing the control complexity of the hoisting cluster, enhancing the robustness of the hoisting cluster, and supporting modular expansion.

[0179] The multi-unmanned aerial vehicle cooperative transportation control method provided in the embodiment reduces the control complexity through the dynamic force balance mechanism of the pulley. The load can be automatically distributed through the pulley, and the unmanned aerial vehicle group is allowed to cooperatively fly within a meter-level precision range without relying on high-precision real-time positioning and complex mechanical calculation.

[0180] Furthermore, the multi-UAV collaborative transportation control method provided in this embodiment enhances the robustness of the hoisting cluster through a combination of flexible ropes and pulleys. The combination of flexible ropes and pulleys can buffer the impact of airflow disturbances and the lateral displacement of the UAVs, improving stability in dynamic environments.

[0181] Furthermore, the multi-UAV collaborative transportation control method provided in this embodiment enhances the scalability of the hoisting cluster by connecting two UAVs with the same rope. It supports modular addition and removal of the number of UAVs to adapt to different weight class payload requirements.

[0182] The multi-UAV collaborative transport control method provided in this embodiment can solve the technical problems of high precision requirements, poor fault tolerance of transport clusters, high R&D costs, and limited applicability in existing multi-UAV collaborative hoisting technologies. Specifically, 1) it achieves multi-UAV collaborative hoisting of large loads by using pulleys. A tension self-distribution device with movable pulleys as hardware is installed on the rope, and the two ends of the rope are connected to UAVs or other pulleys. The pulleys realize the automatic balance of rope tension, eliminating the need for traditional force sensors. This solves the problem of difficulty in achieving average load distribution among UAVs in existing solutions, thus improving transport efficiency; 2) based on the force self-distribution capability of the pulleys, the multi-UAV collaborative transport control method provided in this embodiment dynamically balances each UAV and automatically adjusts its posture to achieve stable hoisting flight. This solves the problem of limited position tracking control capability of existing hoisting clusters for loads, thus improving transport stability and safety; 3) it eliminates the need to determine the constraint force of the target load on the target UAV, adapting to changes in the dynamic environment and avoiding the problem of existing methods needing to consider introducing more influencing factors to improve the accuracy of the constraint force; 4) it supports modular expansion, allowing flexible addition or reduction of the number of UAVs as needed, improving the applicability and scalability of the hoisting cluster.

[0183] like Figure 12 As shown, the multi-UAV collaborative transport control method provided in this embodiment can be used in a scenario where four UAVs are working together for hoisting. The pulleys in the hoisting cluster controlled by this method can automatically adjust rope tension, reducing the load distribution error rate. The buffering effect of the flexible ropes and the mechanical advantages of the multi-pulley combination further enhance the wind resistance of the hoisting cluster. The number of UAVs can be flexibly adjusted to adapt to different weight load requirements. By simplifying the precision requirements of the control module, the implementation cost is significantly reduced. Throughout the hoisting process, the pulley structure can automatically adjust the force on each rope, achieving automatic force balance among the multiple UAVs, thereby improving the fault tolerance and robustness of the hoisting cluster, while reducing the requirements for UAV attitude control precision.

[0184] Figure 13A test scene schematic diagram of the multi-unmanned aerial vehicle cooperative transportation control method provided in this embodiment is shown. In the test, the pulley automatically adjusts the rope tension, the load distribution is uniform, and the load distribution error rate is less than 5%; the buffering effect of the flexible rope enhances the wind resistance of the hoisting cluster, and the wind resistance of the hoisting cluster is improved by 40%; the pose of each unmanned aerial vehicle is adjusted by the control module with meter-level positioning accuracy, and the cost is reduced by 90% compared with the traditional scheme.

[0185] The multi-unmanned aerial vehicle cooperative transportation control method provided in this embodiment, the hoisting cluster controlled by the method includes multiple unmanned aerial vehicles and a load; each unmanned aerial vehicle is equipped with a hanging interface; the multiple unmanned aerial vehicles and the load are connected into a preset topological structure through the hanging interface and a high-strength flexible static rope; a pulley is embedded on each rope; the method includes: determining a tension vector borne by each unmanned aerial vehicle in the hoisting cluster; determining a speed instruction value of a virtual leader in the hoisting cluster according to the position and speed of each unmanned aerial vehicle; determining an adjacency constraint control quantity of each unmanned aerial vehicle according to the speed instruction value; determining a tension compensation control quantity of each unmanned aerial vehicle according to the tension vector; determining an attitude error vector of each unmanned aerial vehicle according to the adjacency constraint control quantity and the tension compensation control quantity; determining a control moment of each unmanned aerial vehicle according to the attitude error vector; and adjusting the pose of each unmanned aerial vehicle based on the control moment of each unmanned aerial vehicle. The hoisting cluster controlled by the method provided in this embodiment includes multiple unmanned aerial vehicles and a load, the multiple unmanned aerial vehicles and the load are connected into a preset topological structure through the hanging interface and a high-strength flexible static rope, and a pulley is embedded on each rope. The pulley can adjust the force between the two unmanned aerial vehicles connected by the pulley, improve the stability, and reduce the control difficulty. Based on the structure, the pose of each unmanned aerial vehicle can be adjusted according to the tension borne by each unmanned aerial vehicle in the hoisting cluster, the pose of each unmanned aerial vehicle can be automatically adjusted to realize stable hoisting flight, the control complexity is reduced, and the robustness of control is enhanced.

[0186] Based on the same inventive concept of the multi-unmanned aerial vehicle cooperative transportation control method, the multi-unmanned aerial vehicle cooperative transportation control device provided in this embodiment controls a hoisting cluster including multiple unmanned aerial vehicles and a load. Each unmanned aerial vehicle is equipped with a hanging interface. The multiple unmanned aerial vehicles and the load are connected into a preset topological structure through the hanging interface and a high-strength flexible static rope. A pulley is embedded on each rope.

[0187] Referring to Figure 14 , the device includes:

[0188] The first determination module 1401 is configured to determine a tension vector borne by each unmanned aerial vehicle in the hoisting cluster.

[0189] The second determination module 1402 is configured to determine a speed instruction value of a virtual leader in the hoisting cluster according to the position and speed of each unmanned aerial vehicle.

[0190] The third determining module 1403 is configured to determine the adjacency constraint control quantity of each unmanned aerial vehicle according to the speed instruction value.

[0191] The fourth determining module 1404 is configured to determine the tension compensation control quantity of each unmanned aerial vehicle according to the tension vector. Wherein, i is the unmanned aerial vehicle identifier, n is the total number of unmanned aerial vehicles in the hoisting cluster, i = 1, 2, …, n, ΔTC i is the tension compensation control quantity of the i th unmanned aerial vehicle, γ is the compensation gain coefficient, F ref is the target tension, F i is the tension vector of the i th unmanned aerial vehicle, |F i is the direction vector of the rope connected to the i th unmanned aerial vehicle, |F i is the length of the rope connected to the i th unmanned aerial vehicle.

[0192] The fifth determining module 1405 is configured to determine the attitude error vector of each unmanned aerial vehicle according to the adjacency constraint control quantity and the tension compensation control quantity ΔTC i .

[0193] The sixth determining module 1406 is configured to determine the control moment of each unmanned aerial vehicle according to the attitude error vector.

[0194] The control module 1407 is configured to adjust the pose of each unmanned aerial vehicle based on the control moment of each unmanned aerial vehicle.

[0195] Wherein, the first determining module 1401 is configured to determine the tension vector of each unmanned aerial vehicle in the hoisting cluster according to the following equation group:

[0196]

[0197] Wherein, i is the unmanned aerial vehicle identifier, n is the total number of unmanned aerial vehicles in the hoisting cluster, i = 1, 2, …, n. j is the unmanned aerial vehicle identifier, j = 1, 2, …, n, and i ≠ j. F i is the tension vector of the i th unmanned aerial vehicle, |F i is the tension of the i th unmanned aerial vehicle. F j is the tension vector of the j th unmanned aerial vehicle, |F j is the tension of the j th unmanned aerial vehicle. E i,j The table indicates whether there is a rope connection between the i th unmanned aerial vehicle and the j th unmanned aerial vehicle, E i,j = 1 indicates that there is a rope connection between the i th unmanned aerial vehicle and the j th unmanned aerial vehicle. θ i is the angle between the tension of the i th unmanned aerial vehicle and the gravity direction of the load, θ j is the angle between the tension of the j th unmanned aerial vehicle and the gravity direction of the load. G is the gravity of the load.

[0198] wherein the second determining module 1402 is configured to determine the speed instruction value of the virtual leader by the formula .

[0199] wherein, is the speed instruction value of the virtual leader at the next time, k p is the proportional gain, k d is the differential gain, x i is the position of the i-th UAV, x vir is the position of the virtual leader at the current time, v i is the speed of the i-th UAV, v vir is the speed of the virtual leader at the current time.

[0200] wherein the third determining module 1403 is configured to determine the adjacency constraint control quantity of each UAV by the formula .

[0201] wherein, is the adjacency constraint control quantity of the i-th UAV, a is the adjacency weight coefficient, N i is the set of adjacent UAVs of the i-th UAV, the set of adjacent UAVs includes the virtual leader and the UAVs having physical constraints with the i-th UAV, m is the identifier of the adjacent UAV of the i-th UAV, x m is the position of the adjacent UAV m of the i-th UAV, x i is the position of the i-th UAV.

[0202] wherein the third determining module 1403 is configured to determine the adjacency constraint control quantity of each UAV by the formula .

[0203] wherein, is the adjacency constraint control quantity of the i-th UAV, a is the adjacency weight coefficient, N i is the set of adjacent UAVs of the i-th UAV, the set of adjacent UAVs includes the virtual leader and the UAVs having physical constraints with the i-th UAV, m is the identifier of the adjacent UAV of the i-th UAV, x m is the position of the adjacent UAV m of the i-th UAV, x i is the position of the i-th UAV, is the expected distance difference between the i-th UAV and the m-th adjacent UAV of the i-th UAV.

[0204] wherein the fifth determining module 1405 is configured to determine the total expected force of each UAV by the formula . i is the total expected force of the i-th UAV, The adjacent constraint control quantity of the ith unmanned aerial vehicle is ΔTC i The tension compensation control quantity of the ith unmanned aerial vehicle is ΔTC

[0205] Based on Newton's second law, the acceleration, speed, and displacement of each unmanned aerial vehicle are determined according to the total expected force of each unmanned aerial vehicle.

[0206] Based on the acceleration, speed, displacement, and angle expected value of each unmanned aerial vehicle, the current attitude quaternion of each unmanned aerial vehicle is obtained.

[0207] The attitude error vector of each unmanned aerial vehicle is determined by the formula

[0208] Wherein, The attitude error vector of the ith unmanned aerial vehicle is q e The expected attitude quaternion is q i The current attitude quaternion of the ith unmanned aerial vehicle is q The quaternion multiplication operator is vec, and the vector part is extracted.

[0209] Wherein, the current attitude quaternion of each unmanned aerial vehicle is determined according to the adjacent constraint control quantity and the tension compensation control quantity ΔTC i , including:

[0210] The total expected force of each unmanned aerial vehicle is determined by the formula Wherein, U i The total expected force of the ith unmanned aerial vehicle is U The adjacent constraint control quantity of the ith unmanned aerial vehicle is ΔTC i The tension compensation control quantity of the ith unmanned aerial vehicle is ΔTC

[0211] Based on Newton's second law, the acceleration, speed, and displacement of each unmanned aerial vehicle are determined according to the total expected force of each unmanned aerial vehicle.

[0212] Based on the acceleration, speed, displacement, and angle expected value of each unmanned aerial vehicle, the current attitude quaternion of each unmanned aerial vehicle is obtained.

[0213] Wherein, the sixth determination module 1406 is configured to determine the control moment of each unmanned aerial vehicle by the formula

[0214] Wherein, τ i The control moment of the ith unmanned aerial vehicle is τ a The attitude proportional gain is k ω The attitude error vector of the ith unmanned aerial vehicle is q s The angular velocity proportional gain is k i The angular velocity error of the ith unmanned aerial vehicle is ω

[0215] ​​ is a sliding mode parameter of the i-th UAV, or, k s is a sliding mode surface proportional gain, ω i is a current angular velocity of the i-th UAV, and are sliding mode surface matrices of the i-th UAV, is a position error vector of the i-th UAV, is a derivative of , λ is a positive diagonal matrix; η is a sliding mode robust gain, sgn() is a sign function,

[0216] The device provided by the embodiment can adjust the poses of the UAVs in the hoisting cluster according to the tension forces borne by the UAVs, and can automatically adjust the poses of the UAVs to realize stable hoisting flight, thereby reducing control complexity and enhancing control robustness.

[0217] Based on the same inventive concept as the multi-UAV cooperative transportation control method, the embodiment provides an electronic device, as shown in Figure 15 , which includes a memory 1501, a processor 1502, and a computer program.

[0218] The computer program is stored in the memory 1501 and is configured to be executed by the processor 1502 to implement the above-mentioned multi-UAV cooperative transportation control method.

[0219] Specifically, the hoisting cluster controlled by the method includes multiple UAVs and a load; each UAV is equipped with a hoisting interface; the multiple UAVs and the load are connected into a preset topological structure through the hoisting interfaces and high-strength flexible static ropes; and a pulley is embedded on each rope.

[0220] The implementation process of the method is as follows:

[0221] Determine the tension force vector borne by each UAV in the hoisting cluster.

[0222] Determine the speed command value of a virtual leader in the hoisting cluster according to the positions and speeds of the UAVs.

[0223] Determine the adjacency constraint control quantity of each UAV according to the speed command value.

[0224] Determine the tension compensation control quantity of each UAV according to the tension force vector wherein i is a UAV identifier, n is the total number of UAVs in the hoisting cluster, i = 1, 2, …, n, ΔTC i is the tension compensation control quantity of the i-th UAV, γ is a compensation gain coefficient, Fref F is the tension vector of the i-th unmanned aerial vehicle i F is the tension vector of the i-th unmanned aerial vehicle i |F is the direction vector of the rope connected to the i-th unmanned aerial vehicle i |F is the length of the rope connected to the i-th unmanned aerial vehicle

[0225] According to the abutment constraint control quantity and the tension compensation control quantity ΔTC i , the attitude error vector of each unmanned aerial vehicle is determined.

[0226] According to the attitude error vector, the control moment of each unmanned aerial vehicle is determined.

[0227] Based on the control moment of each unmanned aerial vehicle, the pose of each unmanned aerial vehicle is adjusted.

[0228] Wherein, the tension vector of each unmanned aerial vehicle in the hoisting cluster is determined, comprising:

[0229] The tension vector of each unmanned aerial vehicle in the hoisting cluster is determined according to the following equation set:

[0230]

[0231] Wherein, i is the unmanned aerial vehicle identifier, n is the total number of unmanned aerial vehicles in the hoisting cluster, i = 1, 2, …, n. J is the unmanned aerial vehicle identifier, j = 1, 2, …, n, and i ≠ j. F i F is the tension vector of the i-th unmanned aerial vehicle i |F is the tension vector of the i-th unmanned aerial vehicle j F is the tension vector of the j-th unmanned aerial vehicle j |F is the tension vector of the j-th unmanned aerial vehicle i,j Table is whether there is a rope connection between the i-th unmanned aerial vehicle and the j-th unmanned aerial vehicle i,j = 1 is that there is a rope connection between the i-th unmanned aerial vehicle and the j-th unmanned aerial vehicle i θ is the angle between the tension of the i-th unmanned aerial vehicle and the direction of the gravity of the load j θ is the angle between the tension of the j-th unmanned aerial vehicle and the direction of the gravity of the load

[0232] Wherein, according to the position and speed of each unmanned aerial vehicle, the speed command value of the virtual leader in the hoisting cluster is determined, comprising:

[0233] The speed command value of the virtual leader is determined by the formula

[0234] Wherein, is the speed command value of the virtual leader at the next moment, k p k is the proportional gain​d is a differential gain, x i is a position of the i-th UAV, x vir is a position of the virtual leader at the current time, v i is a velocity of the i-th UAV, v vir is a velocity of the virtual leader at the current time.

[0235] wherein the adjacency constraint control quantity of each UAV is determined according to the velocity command value, comprising:

[0236] The adjacency constraint control quantity of each UAV is determined through the formula

[0237] wherein, is the adjacency constraint control quantity of the i-th UAV, a is an adjacency weight coefficient, N i is an adjacency UAV set of the i-th UAV, the adjacency UAV set comprises the virtual leader and a UAV having a physical constraint with the i-th UAV, m is an adjacency UAV identifier of the i-th UAV, x m is a position of the adjacency UAV m of the i-th UAV, x i is a position of the i-th UAV.

[0238] wherein the adjacency constraint control quantity of each UAV is determined according to the velocity command value, comprising:

[0239] The adjacency constraint control quantity of each UAV is determined through the formula

[0240] wherein, is the adjacency constraint control quantity of the i-th UAV, a is an adjacency weight coefficient, N i is an adjacency UAV set of the i-th UAV, the adjacency UAV set comprises the virtual leader and a UAV having a physical constraint with the i-th UAV, m is an adjacency UAV identifier of the i-th UAV, x m is a position of the adjacency UAV m of the i-th UAV, x i is a position of the i-th UAV, is a desired distance difference between the i-th UAV and the m-th adjacency UAV of the i-th UAV.

[0241] wherein the attitude error vector of each UAV is determined according to the adjacency constraint control quantity and the tension compensation control quantity ΔTC i , comprising:

[0242] The total desired force of each UAV is determined through the formula i is the total desired force of the i-th UAV, is the adjacency constraint control quantity of the i-th UAV, ΔTC​​​i is the tension compensation control amount of the i-th UAV.

[0243] According to the total expected force of each UAV, the acceleration, speed, and displacement of each UAV are determined based on Newton's second law.

[0244] According to the acceleration, speed, displacement, and angle expected value of each UAV, the current attitude quaternion of each UAV is obtained.

[0245] The attitude error vector of each UAV is determined by the formula

[0246] wherein, is the attitude error vector of the i-th UAV, q e is the expected attitude quaternion, q i is the current attitude quaternion of the i-th UAV, is the quaternion multiplication operator, and vec is the extraction vector part operation.

[0247] wherein, according to the adjacency constraint control amount and the tension compensation control amount ΔTC i , the current attitude quaternion of each UAV is determined, including:

[0248] The total expected force of each UAV is determined by the formula i is the total expected force of the i-th UAV, is the adjacency constraint control amount of the i-th UAV, ΔTC i is the tension compensation control amount of the i-th UAV.

[0249] According to the total expected force of each UAV, the acceleration, speed, and displacement of each UAV are determined based on Newton's second law.

[0250] According to the acceleration, speed, displacement, and angle expected value of each UAV, the current attitude quaternion of each UAV is obtained.

[0251] wherein, according to the attitude error vector, the control moment of each UAV is determined, including:

[0252] The control moment of each UAV is determined by the formula

[0253] wherein, τ i is the control moment of the i-th UAV, k a is the attitude proportional gain, is the attitude error vector of the i-th UAV, k ω is the angular velocity proportional gain, is the angular velocity error of the i-th UAV. ​​​

[0254] is the sliding mode parameter of the i-th UAV, or, k s is the sliding mode surface ratio

[0255] gain, ω i is the current angular velocity of the i-th UAV, and are the sliding mode surface matrices of the i-th UAV, is the position error vector of the i-th UAV, is the derivative of, λ is a positive diagonal matrix; η is a sliding mode robustness gain, sgn() is a sign function,

[0256] The electronic device provided by the embodiment, on which a computer program is executed by a processor to adjust the poses of each UAV in the hoisting cluster according to the tension experienced by each UAV in the hoisting cluster, can automatically adjust the poses of each UAV to achieve stable hoisting flight, thereby reducing control complexity and enhancing the robustness of control.

[0257] Based on the same inventive concept as the multi-UAV cooperative transportation control method, the embodiment provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium. The computer program is executed by a processor to implement the multi-UAV cooperative transportation control method.

[0258] Specifically, the hoisting cluster controlled by the method includes multiple UAVs and a load; each UAV is equipped with a hoisting interface; the multiple UAVs and the load are connected into a preset topological structure through the hoisting interfaces and high-strength flexible static ropes; and a pulley is embedded on each rope.

[0259] The implementation process of the method is as follows:

[0260] Determine the tension vector experienced by each UAV in the hoisting cluster.

[0261] Determine the speed command value of the virtual leader in the hoisting cluster according to the positions and speeds of each UAV.

[0262] Determine the adjacency constraint control quantity of each UAV according to the speed command value.

[0263] Determine the tension compensation control quantity of each UAV according to the tension vector Wherein, i is the UAV identifier, n is the total number of UAVs in the hoisting cluster, i = 1, 2, …, n, ΔTC i is the tension compensation control quantity of the i-th UAV, γ is a compensation gain coefficient, F ref is the target tension, F iis the tension vector of the ith unmanned aerial vehicle, |F i is the direction vector of the rope connected to the ith unmanned aerial vehicle, |F i is the length of the rope connected to the ith unmanned aerial vehicle.

[0264] According to the adjacent constraint control quantity and the tension compensation control quantity ΔTC i , the attitude error vector of each unmanned aerial vehicle is determined.

[0265] According to the attitude error vector, the control moment of each unmanned aerial vehicle is determined.

[0266] Based on the control moment of each unmanned aerial vehicle, the pose of each unmanned aerial vehicle is adjusted.

[0267] Wherein, the tension vector of each unmanned aerial vehicle in the hoisting cluster is determined, comprising:

[0268] The tension vector of each unmanned aerial vehicle in the hoisting cluster is determined according to the following equation set:

[0269]

[0270] Wherein, i is the unmanned aerial vehicle identifier, n is the total number of unmanned aerial vehicles in the hoisting cluster, i = 1, 2, …, n. J is the unmanned aerial vehicle identifier, j = 1, 2, …, n, and i ≠ j. F i is the tension vector of the ith unmanned aerial vehicle, |F i is the tension of the ith unmanned aerial vehicle. F j is the tension vector of the jth unmanned aerial vehicle, |F j is the tension of the jth unmanned aerial vehicle. E i,j is whether there is a rope connection between the ith unmanned aerial vehicle and the jth unmanned aerial vehicle, E i,j = 1 indicates that there is a rope connection between the ith unmanned aerial vehicle and the jth unmanned aerial vehicle. θ i is the angle between the tension of the ith unmanned aerial vehicle and the gravity direction of the load, θ j is the angle between the tension of the jth unmanned aerial vehicle and the gravity direction of the load. G is the gravity of the load.

[0271] Wherein, according to the position and speed of each unmanned aerial vehicle, the speed command value of the virtual leader in the hoisting cluster is determined, comprising:

[0272] The speed command value of the virtual leader is determined by the formula

[0273] Wherein, is the speed command value of the virtual leader at the next moment, k p is the proportional gain, k d is the differential gain, x i ​is the position of the i-th UAV, x vir is the position of the virtual leader at the current time, v i is the speed of the i-th UAV, v vir is the speed of the virtual leader at the current time.

[0274] The adjacent constraint control quantity of each UAV is determined according to the speed instruction value, including:

[0275] The adjacent constraint control quantity of each UAV is determined by the formula

[0276] wherein, is the adjacent constraint control quantity of the i-th UAV, a is an adjacent weight coefficient, N i is the adjacent UAV set of the i-th UAV, the adjacent UAV set includes the virtual leader and the UAVs having physical constraints with the i-th UAV, m is the adjacent UAV identifier of the i-th UAV, x m is the position of the adjacent UAV m of the i-th UAV, x i is the position of the i-th UAV.

[0277] The adjacent constraint control quantity of each UAV is determined according to the speed instruction value, including:

[0278] The adjacent constraint control quantity of each UAV is determined by the formula

[0279] wherein, is the adjacent constraint control quantity of the i-th UAV, a is an adjacent weight coefficient, N i is the adjacent UAV set of the i-th UAV, the adjacent UAV set includes the virtual leader and the UAVs having physical constraints with the i-th UAV, m is the adjacent UAV identifier of the i-th UAV, x m is the position of the adjacent UAV m of the i-th UAV, x i is the position of the i-th UAV, is the expected distance difference between the i-th UAV and the m-th adjacent UAV of the i-th UAV.

[0280] The attitude error vector of each UAV is determined according to the adjacent constraint control quantity and the tension compensation control quantity ΔTC i , including:

[0281] The total expected force of each UAV is determined by the formula i is the total expected force of the i-th UAV, is the adjacent constraint control quantity of the i-th UAV, ΔTC i is the tension compensation control quantity of the i-th UAV.​​​

[0282] Based on Newton's second law, the acceleration, velocity, and displacement of each drone are determined according to the total expected force of each drone.

[0283] Based on the expected values ​​of acceleration, velocity, displacement, and angle of each UAV, the current attitude quaternion of each UAV is obtained.

[0284] Through formula Determine the attitude error vector for each UAV.

[0285] in, Let q be the attitude error vector of the i-th UAV. e Let q be the desired pose quaternion. i Let be the current attitude quaternion of the i-th UAV. `vec` is the quaternion multiplication operator, and `vec` is the operation for extracting the vector part.

[0286] Among them, based on the adjacent constraint control quantity and the tension compensation control quantity ΔTC i Determine the current attitude quaternions of each drone, including:

[0287] Through formula Determine the total expected power of each UAV. Among them, U i Let i be the total expected power of the i-th drone. Let ΔTC be the adjacency constraint control variable for the i-th UAV. i Let be the tension compensation control quantity for the i-th UAV.

[0288] Based on Newton's second law, the acceleration, velocity, and displacement of each drone are determined according to the total expected force of each drone.

[0289] Based on the expected values ​​of acceleration, velocity, displacement, and angle of each UAV, the current attitude quaternion of each UAV is obtained.

[0290] Among them, the control torque of each UAV is determined based on the attitude error vector, including:

[0291] Through formula Determine the control torque for each drone.

[0292] Where, τ i Let k be the control torque of the i-th UAV. a For attitude scaling gain, Let k be the attitude error vector of the i-th UAV. ω For angular velocity proportional gain, Let be the angular velocity error of the i-th UAV.

[0293] a sliding mode parameter of the i-th UAV, or, k s a sliding mode surface proportional gain, ω i a current angular velocity of the i-th UAV, and a sliding mode surface matrix of the i-th UAV, a position error vector of the i-th UAV, a derivative of , λ is a positive diagonal matrix; η is a sliding mode robust gain, and sgn() is a sign function,

[0294] The computer readable storage medium provided by the embodiment can automatically adjust the poses of the UAVs to realize stable hoisting flight, reduce control complexity, and enhance control robustness.

[0295] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product in the form of one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and direct interpretation script languages such as JavaScript.

[0296] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one or more flows and / or blocks. Figure 1 The device that implements the functions specified in one or more flows and / or blocks.

[0297] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0298] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0299] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the preferred embodiments by those of skill in the art once they have the benefit of the present disclosure. Therefore, the appended claims are intended to encompass within their scope all possible variations and modifications of the preferred embodiments.

[0300] It is apparent that a person skilled in the art can make a variety of changes and modifications to the application without departing from the spirit and scope of the application. Thus, if these modifications and variations of the application fall within the scope of the claims and their equivalents, it is intended to include them within the scope of the application.

Claims

1. A multi-UAV cooperative transportation control method, characterized in that, The method controls a hoisting cluster including multiple unmanned aerial vehicles and loads; each unmanned aerial vehicle is equipped with a hoisting interface; the multiple unmanned aerial vehicles and the loads are connected into a preset topological structure through the hoisting interfaces and high-strength flexible static ropes; Each rope is embedded with a pulley; The method includes: determining tension vectors borne by each unmanned aerial vehicle in the hoisting cluster; determining a speed instruction value of a virtual leader in the hoisting cluster according to positions and speeds of the unmanned aerial vehicles; determining an adjacency constraint control quantity of each unmanned aerial vehicle according to the speed instruction value; determine a tension compensation control amount of each unmanned aerial vehicle according to the tension force vector Wherein, i is the unmanned aerial vehicle identifier, n is the total number of unmanned aerial vehicles in the hoisting cluster, i = 1, 2, …, n, ΔTC i is the tension compensation control amount of the i th unmanned aerial vehicle, γ is the compensation gain coefficient, F ref is the target tension, F i is the tension force vector borne by the i th unmanned aerial vehicle, l i is the direction vector of the rope connected to the i th unmanned aerial vehicle, |l i is the length of the rope connected to the i th unmanned aerial vehicle; According to the abutment constraint control quantity and the tension compensation control quantity ΔTC i , determine the attitude error vector of each UAV; determining a control moment of each unmanned aerial vehicle according to the attitude error vector; adjusting a pose of each unmanned aerial vehicle based on the control moment of the unmanned aerial vehicle.

2. The method of claim 1, wherein, The determination of the tension vectors borne by each unmanned aerial vehicle in the hoisting cluster includes: determining the tension vectors borne by each unmanned aerial vehicle in the hoisting cluster according to the following equation group: wherein, i is the UAV identifier, n is the total number of UAVs in the cluster, i = 1, 2, …, n; j is the UAV identifier, j = 1, 2, …, n, and i≠j; F i is the tension vector of the ith UAV, |F i is the tension of the ith UAV; F j is the tension vector of the jth UAV, |F j is the tension of the jth UAV; E i,j is the connection between the ith UAV and the jth UAV, E i,j = 1 indicates that there is a connection between the ith UAV and the jth UAV; θ i is the angle between the tension of the ith UAV and the direction of the gravity of the load, θ j is the angle between the tension of the jth UAV and the direction of the gravity of the load; G is the gravity of the load.

3. The method of claim 1, wherein, The determination of the speed instruction value of the virtual leader in the hoisting cluster according to the positions and speeds of the unmanned aerial vehicles includes: determining a speed command value for the virtual pilot by the equation determining a speed command value for the virtual pilot; wherein, is the velocity command value for the virtual leader at the next time instant, k p is a proportional gain, k d is a derivative gain, x i is the position of the ith UAV, x vir is the position of the virtual leader at the current time instant, v i is the velocity of the ith UAV, v vir is the velocity of the virtual leader at the current time instant.

4. The method of claim 1, wherein, The determination of the adjacency constraint control quantity of each unmanned aerial vehicle according to the speed instruction value includes: The adjacent constraint control quantity of each drone is determined by the formula determines the adjacent constraint control quantity of each drone wherein, is the adjacency constraint control amount of the ith UAV, and a is an adjacency weight coefficient, N i is the adjacency UAV set of the ith UAV, the adjacency UAV set including a virtual leader and a UAV having a physical constraint with the ith UAV, m is the adjacency UAV identifier of the ith UAV, x m is the position of the adjacency UAV m of the ith UAV, x i is the position of the ith UAV.

5. The method of claim 1, wherein, The determination of the adjacency constraint control quantity of each unmanned aerial vehicle according to the speed instruction value includes: The adjacent constraint control quantity of each drone is determined by the formula determines the adjacent constraint control quantity of each drone wherein, is the adjacency constraint control amount of the ith UAV, and a is an adjacency weight coefficient, and N i is the adjacency UAV set of the ith UAV, the adjacency UAV set including a virtual leader and a UAV having a physical constraint with the ith UAV, m is the adjacency UAV identifier of the ith UAV, x m is the position of the adjacency UAV m of the ith UAV, x i is the position of the ith UAV, is the desired distance difference between the ith UAV and the mth adjacency UAV of the ith UAV.

6. The method of claim 1, wherein, The abutment constraint control quantity and the tension compensation control quantity ΔTC i Determine the attitude error vector of each UAV, including: The total expected force of each unmanned aerial vehicle is determined by the formula wherein U i is the total expected force of the i-th unmanned aerial vehicle, is the adjacent constraint control amount of the i-th unmanned aerial vehicle, and i is the tension compensation control amount of the i-th unmanned aerial vehicle. determining an acceleration, a speed and a displacement of each unmanned aerial vehicle based on a total expected force of the unmanned aerial vehicle according to Newton's second law; obtaining a current attitude quaternion of each unmanned aerial vehicle according to the acceleration, the speed, the displacement and an angle expected value of the unmanned aerial vehicle; The attitude error vector of each drone is determined by the formula ​ wherein, is the attitude error vector of the i-th UAV, q e is the desired attitude quaternion, q i is the current attitude quaternion of the i-th UAV, q is the quaternion multiplication operator, vec is the vector part extraction operator.

7. The method of claim 1, wherein, The determination of the control moment of each unmanned aerial vehicle according to the attitude error vector includes: The control moment of each drone is determined by the formula ​ where τ i is the control moment of the i-th UAV, k a is the attitude proportional gain, is the attitude error vector of the i-th UAV, k ω is the angular velocity proportional gain, is the angular velocity error of the i-th UAV; is the sliding mode parameter for the i-th UAV, or k s is the sliding mode surface proportional gain, ω i is the current angular velocity of the i-th UAV, and are the sliding mode surface matrices for the i-th UAV, is the position error vector of the i-th UAV, is the derivative of, λ is a positive definite diagonal matrix; η is the sliding mode robust gain, sgn() is the sign function, 8.A multi-UAV cooperative transportation control apparatus, characterized by, The device controls a hoisting cluster including multiple unmanned aerial vehicles and loads; each unmanned aerial vehicle is equipped with a hoisting interface; the multiple unmanned aerial vehicles and the loads are connected into a preset topological structure through the hoisting interfaces and high-strength flexible static ropes; Each rope is embedded with a pulley; The device includes: a first determination module configured to determine tension vectors borne by each unmanned aerial vehicle in the hoisting cluster; a second determination module configured to determine a speed instruction value of a virtual leader in the hoisting cluster according to positions and speeds of the unmanned aerial vehicles; a third determination module configured to determine an adjacency constraint control quantity of each unmanned aerial vehicle according to the speed instruction value; a fourth determining module configured to determine a tension compensation control amount of each unmanned aerial vehicle according to the tension vector wherein i is an unmanned aerial vehicle identifier, n is a total number of unmanned aerial vehicles in the hoisting cluster, i = 1, 2, …, n, ATC i is a tension compensation control amount of the i-th unmanned aerial vehicle, γ is a compensation gain coefficient, F ref is a target tension, F i is a tension vector borne by the i-th unmanned aerial vehicle, l i is a direction vector of the rope connected to the i-th unmanned aerial vehicle, |l i is a length of the rope connected to the i-th unmanned aerial vehicle; A fifth determining module is configured to determine a pose error vector of each UAV according to the abutment constraint control variable and the tension compensation control variable ΔTC i . a sixth determination module configured to determine a control moment of each unmanned aerial vehicle according to the attitude error vector; a control module configured to adjust a pose of each unmanned aerial vehicle based on the control moment of the unmanned aerial vehicle.

9. An electronic device, comprising: include: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, having a computer program stored thereon; the computer program is executed by a processor to implement the method of any one of claims 1-7.

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