A large-load unmanned aerial vehicle cooperative hoisting method, device, equipment and medium

Abstract

The application provides a large-load unmanned aerial vehicle cooperative hoisting method, device, equipment and medium, and relates to the technical field of edge computing. The method comprises the following steps: a virtual leading unmanned aerial vehicle in an unmanned aerial vehicle cluster calculates total load data of hoisted loads of a plurality of edge nodes based on load distribution data of each edge node; based on the total load data and node positions, an overall motion trajectory of the unmanned aerial vehicle cluster is determined, and the overall motion trajectory is sent to the edge nodes, so that the edge nodes find the required reference tension value and node motion trajectory based on an ant colony algorithm; if it is determined that a first unmanned aerial vehicle in the plurality of unmanned aerial vehicles has a hoisting rope breakage during flight, the load is redistributed according to a predetermined load redistribution scheme, and the tension is synchronously distributed to each second unmanned aerial vehicle; the first unmanned aerial vehicle is set as a proxy center node, and an updated motion trajectory of the unmanned aerial vehicle cluster of the proxy center node is received. The application can quickly process sudden failures in the process of multi-unmanned aerial vehicle cooperative hoisting.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of edge computing, and in particular to a large-load unmanned aerial vehicle cooperative hoisting method, device, equipment and medium. BACKGROUND

[0002] The large-load unmanned aerial vehicle is a large unmanned aerial vehicle. The Unmanned Aircraft Flight Management Provisional Regulations divides unmanned aerial vehicles into five categories, i.e., micro, light, small, medium and large, according to weight and other performance indicators. Among them, the unmanned aerial vehicle with an ascending mass greater than 150 kg belongs to the large unmanned aerial vehicle.

[0003] Multi-large-load unmanned aerial vehicle cooperative hoisting is an air transportation technology based on multi-rotor unmanned aerial vehicle cluster control and load distribution. The core is that multiple unmanned aerial vehicles jointly bear a large-size or single-machine load capacity load through flexible cables or rigid connection structures, complete the cooperative task of complex path planning, accurate attitude control and load dynamic balance under the premise of ensuring flight stability. Its implementation relies on high-precision position perception systems, multi-source information fusion algorithms and distributed or centralized control architectures. By introducing graph modeling, cooperative control law and robust control strategy, the system stability and safety can be further improved, and the application prospect in emergency rescue, large component assembly and urban air logistics fields can be expanded.

[0004] The existing unmanned aerial vehicle hoisting system has weak fault tolerance capability in the face of sudden failures, especially rope breakage and other non-structural failure conditions. The main problem is that the system lacks real-time perception and dynamic response mechanism for single-point hanging failure. Once a rope breaks, the remaining unmanned aerial vehicles often cannot quickly reconstruct the overall tension distribution strategy based on their local state, resulting in load imbalance or rotation, increasing the risk of goods falling. Traditional solutions rely on remote operators for manual intervention, which lags in response time and cannot meet the timeliness requirements of high-dynamic environments in the air. In addition, in the case of communication interruption or multi-machine cooperative link failure, control island effect is more likely to occur, making it difficult to ensure the stability and safety of the hoisting task. Therefore, there is an urgent need for a cooperative control method to quickly handle sudden failures in hoisting. SUMMARY

[0005] The present application provides a large-load unmanned aerial vehicle cooperative hoisting method, device, equipment and medium, which can quickly handle sudden failures in the process of multi-unmanned aerial vehicle cooperative hoisting.

[0006] In a first aspect of the present application, a large-load unmanned aerial vehicle cooperative hoisting method is provided, which is applied to a virtual leader unmanned aerial vehicle in a cluster of unmanned aerial vehicles. The method comprises:

[0007] acquire node positions of each edge node and load distribution data, wherein the virtual leading UAV is a center node of the UAV cluster, and the UAVs other than the virtual leading UAV in the UAV cluster are edge nodes;

[0008] calculate total load data of loads hoisted by the plurality of edge nodes based on the load distribution data;

[0009] determine an overall motion trajectory of the UAV cluster based on the total load data and the node positions, and send the overall motion trajectory to the edge nodes, so that the edge nodes find required reference tension values and node motion trajectories based on an ant colony algorithm;

[0010] if it is determined that a first UAV in the plurality of UAVs has a hoisting rope break during flight, synchronously distribute tension to each second UAV according to a predetermined load redistribution scheme, the second UAV being a UAV other than the first UAV in the UAV cluster;

[0011] set the first UAV as a proxy center node, and receive an updated motion trajectory of the proxy center node for the UAV cluster.

[0012] Based on the above technical solutions, preferably, the calculation of the total load data of the loads hoisted by the plurality of edge nodes based on the load distribution data specifically includes:

[0013] acquire load distribution data calculated by each edge node, wherein the edge node acquires hoisting rope tension, its own vertical acceleration and pitch and roll angles in real time through a mounted tension sensor, an accelerometer and an attitude sensor, calculates its own shared vertical component of force, and then corrects hoisting point inertia change based on its own measured inertial acceleration, calculates an effective gravity acceleration correction factor under unit tension, and obtains corrected load distribution data;

[0014] calculate a cargo center of gravity position according to the plurality of node positions with all load distribution data as weights;

[0015] determine a tension action vector of the edge node according to a direction vector formed between the node position and the cargo center of gravity position;

[0016] project the tension action vector in the direction of gravity to obtain an action component in the vertical direction of each load distribution data;

[0017] inertially correct the action component according to acceleration data of the UAV cluster to obtain a corrected action component;

[0018] The correction component is weighted and fused according to the relative distance between the node position and the center of gravity position of the cargo, to obtain the total load data.

[0019] On the basis of the above technical solutions, preferably, the total load data and the node position are used to determine the overall motion trajectory of the UAV cluster, specifically including:

[0020] The total load data and the node position are combined to construct a suspension point spatial distribution model of the UAV cluster.

[0021] Based on the suspension point spatial distribution model, the rate of change of the center of gravity of the cargo and the instantaneous acceleration data are combined to establish a cargo centroid state vector, and the motion trend of the cargo centroid is predicted.

[0022] Based on the motion trend of the cargo centroid, a mass distribution constraint, a tension balance constraint and an attitude consistency constraint are introduced to construct a load-node structure coupling model, thereby establishing a dynamic mapping relationship between the motion trend of the cargo centroid and the flight path of the UAV cluster.

[0023] A multi-body system dynamics modeling method is introduced, the cargo is regarded as a concentrated mass body, each edge node is connected by a flexible cable to form a spatial tension network structure, and the dynamics control equation of the overall UAV cluster is established based on the tension balance constraint and the angular momentum conservation constraint.

[0024] According to the task flight path input and the motion trend of the cargo centroid, a trajectory optimal control algorithm is used to solve the dynamics control equation to form an expected three-dimensional path of the cargo centroid.

[0025] Based on the load-node structure coupling model, the expected three-dimensional path is mapped to the flight path of the UAV cluster to obtain the overall motion trajectory.

[0026] On the basis of the above technical solutions, preferably, the overall motion trajectory is sent to the edge nodes, so that the edge nodes find the required reference tension value and node motion trajectory based on an ant colony algorithm, specifically including:

[0027] acquire the reference tension value and the node motion trajectory calculated by the edge node based on the overall motion trajectory, wherein the edge node constructs a local path search graph, the path search graph is composed of a node current position, an adjacent path state, a dynamic attitude limit and a cable geometry boundary, a feasible solution graph of a path space is formed, a heuristic search model is constructed based on the path search graph, a current expected path point in the overall motion trajectory is taken as a target guide, and a transfer probability is controlled through pheromone intensity and a heuristic function; in each iteration, a satisfaction degree of a path tension combination solution generated by an ant colony to a tension balance degree and an attitude consistency index of the system is obtained, and a target function is defined; after multiple iterations, the edge node selects the node motion trajectory and the reference tension value corresponding to an optimal ant colony path as an execution command of a current control period, and sends the node motion trajectory and the reference tension value to the center node.

[0028] Based on the above technical scheme, preferably, in the flying process, if it is determined that the first unmanned aerial vehicle of the plurality of unmanned aerial vehicles has a rope breakage, according to a predetermined load redistribution scheme, the synchronous tension distribution to each second unmanned aerial vehicle further comprises:

[0029] Construct a sling point space distribution model of the unmanned aerial vehicle cluster in combination with the total load data and the node position;

[0030] Construct a virtual failure model of the rope breakage situation for each edge node, under the premise of not changing the total mass of the goods, respectively eliminate the load distribution data corresponding to each failure node in the plurality of edge nodes, solve a new set of tension distribution for the remaining nodes, so that the disturbance cost function compared with the sling point space distribution model is minimized while satisfying the tension resultant force conservation and angular momentum constraint;

[0031] The disturbance cost function is calculated offline by using a quadratic programming solver, and an optimal redistribution solution corresponding to the rope breakage of each failure node is generated, and each optimal redistribution solution corresponds to a redistribution configuration of a failure situation;

[0032] The optimal redistribution solution set corresponding to all the failure nodes is obtained as the load redistribution scheme.

[0033] Based on the above technical scheme, preferably, in the flying process, if it is determined that the first unmanned aerial vehicle of the plurality of unmanned aerial vehicles has a rope breakage, according to a predetermined load redistribution scheme, the synchronous tension distribution to each second unmanned aerial vehicle comprises:

[0034] Determine the failure node corresponding to the first unmanned aerial vehicle;

[0035] calling an optimal re-distribution solution corresponding to the failed node corresponding to the first UAV;

[0036] determining a residual node corresponding to each of the second UAVs in the optimal re-distribution solution, thereby determining an updated tension value and a cable direction unit vector to be borne by each of the second UAVs;

[0037] respectively broadcasting the updated tension value and the cable direction unit vector to each of the second UAVs.

[0038] Based on the above technical solutions, preferably, before obtaining the node position and the load distribution data of each edge node, the method further comprises:

[0039] obtaining real-time state parameters broadcasted by each UAV in the UAV cluster in a take-off preparation stage, the real-time state parameters including position information, residual computing resources, communication signal quality, historical trajectory stability indicators, and node reliability levels;

[0040] calculating a centrality score of the virtual leader UAV according to the real-time state parameters, and broadcasting the centrality score of the virtual leader UAV;

[0041] After completing the omnidirectional score synchronization, each UAV respectively and independently compares the sizes of the centrality scores of a plurality of the UAVs, and if it is determined that the centrality score of the virtual leader UAV is the highest, the virtual leader UAV is determined as the center node.

[0042] A second aspect of the present application provides a large-load-carrying UAV cooperative hoisting device, which is used to perform a large-load-carrying UAV cooperative hoisting method according to any one of the above aspects, and is arranged on a virtual leader UAV in a UAV cluster. The device comprises an obtaining module, a processing module, and an output module, wherein:

[0043] The obtaining module is configured to obtain the node position and the load distribution data of each edge node, wherein the virtual leader UAV is a center node of the UAV cluster, and the UAVs other than the virtual leader UAV in the UAV cluster are edge nodes.

[0044] The processing module is configured to calculate total load data of the hoisted loads of a plurality of the edge nodes based on the load distribution data.

[0045] The processing module is configured to determine an overall motion trajectory of the UAV cluster based on the total load data and the node position, and send the overall motion trajectory to the edge nodes, so that the edge nodes find the required reference tension value and node motion trajectory based on an ant colony algorithm.

[0046] The output module is configured to, if it is determined that the first unmanned aerial vehicle in the plurality of unmanned aerial vehicles has a rope break during flight, distribute the tension to each second unmanned aerial vehicle according to a predetermined load redistribution scheme, the second unmanned aerial vehicle being an unmanned aerial vehicle in the unmanned aerial vehicle cluster other than the first unmanned aerial vehicle.

[0047] The output module is configured to set the first unmanned aerial vehicle as a proxy center node, and receive an updated motion trajectory of the proxy center node for the unmanned aerial vehicle cluster.

[0048] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method according to any one of the preceding aspects.

[0049] In a fourth aspect of the present application, a computer readable storage medium is provided, which stores instructions, when the instructions are executed, perform the method according to any one of the preceding aspects.

[0050] In summary, the one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0051] 1. In the embodiments of the present application, a virtual leading unmanned aerial vehicle is introduced as a center node in the unmanned aerial vehicle cluster, a global force model is constructed by combining real-time load data and spatial positions of edge nodes, a pre-calculated load redistribution scheme and a local path optimization mechanism based on an ant colony algorithm are adopted, and efficient control of cluster collaborative hoisting is achieved. Once a rope break of an unmanned aerial vehicle is detected during flight, the corresponding optimal redistribution solution can be immediately called, and the updated tension value and direction information are synchronized to all remaining edge nodes through the center node, so that the edge nodes quickly complete tension adjustment and attitude correction. At the same time, the failed node is converted into a proxy center node, which undertakes trajectory updating and control tasks without participating in physical hoisting, forming a fault-tolerant mechanism of logical control redundancy and physical load decoupling, thereby achieving millisecond-level response to sudden failures and immediate recovery of full stability.

[0052] 2. The application constructs a multi-node load perception model based on the fusion calculation of tension sensors, accelerometers and attitude sensors, combines spatial position and center of gravity offset relationship, comprehensively introduces the projection of tension direction and dynamic inertia correction mechanism, realizes the high-precision real-time estimation of the total load in the cooperative hoisting of unmanned aerial vehicle cluster, effectively overcomes the measurement error caused by attitude disturbance, non-vertical tension or dynamic acceleration in the traditional method, improves the robustness and reliability of the total load estimation under asymmetric force and flight dynamic change, and provides accurate mechanical basis and dynamic reference for subsequent path planning, tension coordination and fault adaptive control.

[0053] 3. The application fuses total load data and node spatial distribution, constructs an accurate hoisting point spatial distribution model, introduces multiple physical constraints such as mass distribution, tension balance and attitude consistency combined with real-time predicted cargo centroid motion trend, establishes the dynamic mapping relationship between load and node. Through multi-body modeling method and trajectory optimal control algorithm, the dynamic coordination and optimal matching between the expected path of goods and the flight path of unmanned aerial vehicle cluster are realized, so as to generate the overall motion trajectory conforming to the dynamic constraint and having high cooperativity. The path accuracy, attitude consistency and tension coordination of the cluster in the cooperative hoisting process are improved, and the dynamic stability and task execution ability in complex space path are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 is a flowchart of a large-load unmanned aerial vehicle cooperative hoisting method disclosed by an embodiment of the application;

[0055] Figure 2 is a module schematic diagram of a large-load unmanned aerial vehicle cooperative hoisting device disclosed by an embodiment of the application;

[0056] Figure 3 is a structural schematic diagram of an electronic device disclosed by an embodiment of the application.

[0057] Explanation of reference signs: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0058] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the embodiments of the specification will be described clearly and completely in conjunction with the drawings in the embodiments of the specification. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments.

[0059] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to represent an example, an illustration, or a description. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words such as "for example" or "for instance" are intended to present the relevant concept in a specific manner.

[0060] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly indicating the technical features indicated. Therefore, the features defined with "first" and "second" can explicitly or implicitly include one or more features. The terms "include", "contain", "have" and their variants mean "include but are not limited to", unless otherwise specifically emphasized.

[0061] For large unmanned aerial vehicles with a take-off mass greater than 150 kg, that is, large-load unmanned aerial vehicles, the multi-large-load unmanned aerial vehicle cooperative hoisting technology realizes the air transportation of large-size or heavy-load goods through cluster control and load distribution, relies on high-precision perception, multi-source fusion and distributed control to complete stable flight and load balancing, although progress has been made in path planning and disturbance control, but in the face of rope breakage and other non-structural sudden failures, the existing system generally lacks real-time perception and adaptive response mechanism, it is difficult to reconstruct the tension distribution strategy in time, and it is easy to cause load imbalance and control failure, so an intelligent cooperative control method with fast fault tolerance capability is needed to improve the system stability and task safety.

[0062] The embodiment discloses a large-load unmanned aerial vehicle cooperative hoisting method, referring to Figure 1 , comprising the following steps S110-S150:

[0063] S110, acquiring the node position and load distribution data of each edge node.

[0064] The large-load unmanned aerial vehicle cooperative hoisting method disclosed by the embodiment of the present application is applied to a virtual leader unmanned aerial vehicle in a unmanned aerial vehicle cluster. The virtual leader unmanned aerial vehicle serves as the center node of the cluster in the multi-unmanned aerial vehicle cooperative hoisting system, is responsible for fusing the position information and load distribution data uploaded by each edge node, uniformly plans the overall motion trajectory and generates target control instructions, simultaneously schedules the load distribution strategy and quickly reconstructs the control architecture when sudden failures such as rope breakage occur, and ensures that the system maintains flight stability and task continuity in a high dynamic environment, and is the core execution unit of cluster cooperative control and global task coordination.

[0065] In a possible implementation, before acquiring the node position of each edge node and the real-time measurement of the load distribution data of the sensor, the method further comprises: acquiring real-time state parameters broadcast by each UAV of the UAV cluster in the take-off preparation stage, the real-time state parameters including position information, remaining computing resources, communication signal quality, historical trajectory stability indicators, and node reliability level; calculating the centrality score of the virtual leader UAV according to the real-time state parameters, and broadcasting the centrality score of the virtual leader UAV; after completing the omnidirectional score synchronization, each UAV independently compares the centrality scores of the plurality of UAVs, and if it is determined that the centrality score of the virtual leader UAV is the highest, the virtual leader UAV is determined as the central node.

[0066] In implementing the technical solution, first, in the take-off preparation stage, each UAV in the UAV cluster actively broadcasts its current real-time state parameters, which include its position information in three-dimensional space, current remaining computing resource proportion, such as CPU or GPU utilization, communication signal quality with other nodes, such as RSSI value or packet loss rate, historical trajectory stability indicators calculated from position fluctuation amplitude and speed mutation frequency in a short time, and node reliability level based on past task records, such as failure rate or response success rate. These parameters are periodically broadcast by each node and synchronized to all members in the cluster through a low-latency self-organizing network.

[0067] Subsequently, each UAV independently calculates the centrality score of the virtual leader UAV candidate based on the preset weighting model using all the acquired node state parameters locally. The scoring function combines multiple dimensions to reflect the resource scheduling capability, communication hub potential, and running stability of the node in the cluster. The specific form is to add up the normalized parameters according to the weighting coefficients, ensuring the comparability between different dimensions and adjusting the weight distribution according to the task needs. For example, when the communication quality and computing resource weights are higher, the node with fast response and strong communication ability is preferentially selected as the control core. Each node broadcasts the centrality score of the virtual leader candidate node after completing the scoring calculation.

[0068] After all the score data synchronization is completed in the cluster, each unmanned aerial vehicle node independently compares the centrality score values of all candidate nodes based on the broadcasted score results, and selects the highest scorer using a deterministic selection mechanism. To avoid conflicts caused by parallel scoring, the same score result can be further set as a priority arbitration rule with node ID or timestamp, ensuring that the cluster always has a unique and consistent control center at any time. When the final score of the virtual leader unmanned aerial vehicle is confirmed as the global highest, and the centrality constraints such as the central position or the strongest communication reachability are met, it is automatically set as the center node of the current task, and is given the right to dominate path planning, task scheduling and control broadcasting. The remaining nodes act as edge nodes to accept the instructions of the center node for collaborative work, forming a hierarchical multi-machine control architecture to ensure stable and efficient operation of subsequent collaborative control.

[0069] In the collaborative hoisting method, the virtual leader unmanned aerial vehicle serves as the center node of the unmanned aerial vehicle cluster, responsible for obtaining real-time position information and load distribution data measured by the tension sensor, accelerometer and attitude module from all edge nodes. Specifically, each edge node determines its relative pose with the center of gravity of the load based on its three-dimensional spatial position, measures the tension of the hoisting rope and its attitude angle, extracts the vertical component of the tension after local preprocessing to form an estimated data packet of the load sharing amount, and sends its current accurate position to the center node through low-latency wireless communication. After receiving the data of all edge nodes, the center node reconstructs the spatial distribution structure of the cluster and perceives the overall tension field, providing accurate data basis for subsequent motion trajectory generation, tension redistribution and emergency response.

[0070] S120, calculating total load data of the loads hoisted by the plurality of edge nodes based on the load distribution data.

[0071] In a possible implementation, the total load data of the loads hoisted by the plurality of edge nodes is calculated based on the load distribution data, specifically comprising: obtaining the load distribution data calculated by each edge node, wherein the edge node obtains the hoisting rope tension, the vertical acceleration and the pitch and roll angles of the edge node by the tension sensor, the accelerometer and the attitude sensor carried by the edge node, calculates the vertical component of the tension, and then corrects the inertia change at the hoisting point based on the measured inertial acceleration, calculates the effective gravity acceleration correction factor under unit tension, and obtains the corrected load distribution data; taking all the load distribution data as the weight, the center of gravity of the goods is calculated according to the positions of the plurality of nodes; the tension action vector of the edge node is determined according to the direction vector formed between the node position and the center of gravity of the goods; the projection of the tension action vector in the vertical direction is obtained as the action component of each load distribution data in the vertical direction; the inertial correction is performed on the action component according to the acceleration data of the unmanned aerial vehicle cluster to obtain the corrected action component; the corrected action component is weighted and fused according to the relative distance between the node position and the center of gravity of the goods to obtain the total load data.

[0072] Specifically, first, the hoisting rope tension data is measured by each edge node based on the tension sensor carried by the edge node in real time, and the vertical acceleration information of the edge node along the gravity direction is obtained by using the accelerometer, and the pitch angle and roll angle provided by the attitude sensor are combined to calculate the vertical force component of the hoisting rope tension in the vertical direction of the edge node by the attitude compensation algorithm. For each ith edge node, the tension measurement value is T i , the pitch angle is θ i , the roll angle is φ i , and the vertical component of the tension in the vertical direction is represented as:

[0073] F zi =T i ·cos(θ i )·cos(φ i )

[0074] Subsequently, the edge node further corrects the inertia change at the hoisting point due to dynamic disturbance by using the inertial acceleration data obtained by the inertial measurement unit, calculates the corrected gravity acceleration factor corresponding to the unit tension, and sets the vertical direction acceleration measured by the ith edge node as a zi , and the corrected unit gravity acceleration factor of the edge node is:

[0075]

[0076] Where g is the standard gravity acceleration constant.

[0077] With the physical meaning of the corrected vertical component, the effective load distribution data of the current edge node after inertia compensation under dynamic conditions is finally obtained, and the load sharing estimation value of the i-th edge node is:

[0078]

[0079] The effective load distribution data is used to participate in global load estimation. Then, the center node receives the inertia-corrected effective load distribution data uploaded by all edge nodes as the center node of the UAV cluster, and uses the effective load distribution data as a weighted factor. The weighted factor is combined with the actual coordinate position of each edge node in the three-dimensional space, and the spatial position of the center of gravity of the goods at the current time is calculated based on the load weighted geometric centroid model. Let the spatial position vector of the i-th edge node be r i =(x i ,y i ,z i ), then the center of gravity position vector r c is expressed as:

[0080]

[0081] The spatial position of the center of gravity is regarded as the point of action of the resultant force of the pulling force of each edge node in space. Then, the center node constructs a direction vector for each edge node from its spatial position to the spatial position of the center of gravity of the goods. The direction vector represents the direction of the pulling force of the edge node, that is, the directional characteristics of the actual force path in space. Let the direction vector d i from the i-th edge node to the center of gravity r c , then:

[0082]

[0083] Subsequently, the center node performs vector projection operation on the pulling force direction vector of each edge node described above, and projects the pulling force direction vector onto the unit vector defined by the gravity direction to obtain the projection component of the pulling force vector of the edge node in the gravity direction, that is, the actual support force in the vertical direction. Let the gravity direction unit vector be e z =(0,0,-1), then the projection component of the pulling force direction of the i-th edge node is:

[0084]

[0085] Since the whole UAV cluster and the cargo being transported may have changes in the overall motion state during flight, the center node further acquires reference acceleration data of the whole cluster, makes inertia correction to the above-mentioned projection component, eliminates inertia interference caused by system acceleration motion, and thus obtains an inertia correction action component of each edge node, which reflects the actual mechanical contribution of the edge node against gravity. The measurement value a c of the overall centroid acceleration of the cargo in the vertical direction is introduced, and the corrected effective action component is:

[0086]

[0087] Finally, the center node calculates a spatial penalty weight factor according to the relative Euclidean distance between each edge node and the spatial position of the center of gravity of the cargo, and the spatial penalty weight factor is used to adjust the action credibility of the edge node. Let R i =||r i -r c ||, and the maximum relative distance is R max , and the distance weight of the ith edge node is:

[0088]

[0089] The inertia correction action component of the edge node far from the center of gravity will be given a lower fusion weight to avoid the influence of the asymmetric moment interference caused by the long tension arm on the total load estimation. After the weighted fusion of the inertia correction action components of all edge nodes, the total load data W total of the current UAV cluster to the target object being transported is obtained.

[0090]

[0091] The total load data serves as an input variable in the global system control model, supporting the implementation of subsequent control tasks such as cooperative trajectory generation, reference tension distribution, and adaptive reconstruction of sudden faults.

[0092] S130, based on the total load data and the node position, determining the overall motion trajectory of the UAV cluster and sending the overall motion trajectory to the edge nodes.

[0093] In a possible implementation, based on the total load data and the node positions, the overall motion trajectory of the UAV cluster is determined, specifically including: constructing a sling point space distribution model of the UAV cluster in combination with the total load data and the node positions; based on the sling point space distribution model, in combination with the change rate of the center of gravity of the goods and the instantaneous acceleration data, a center of gravity state vector of the goods is established, and then a motion trend of the center of gravity of the goods is predicted; based on the motion trend of the center of gravity of the goods, a mass distribution constraint, a tension balance constraint and an attitude consistency constraint are introduced, a load-node structure coupling model is constructed, so as to establish a dynamic mapping relationship between the motion trend of the center of gravity of the goods and the flight path of the UAV cluster; a multi-body system dynamics modeling method is introduced, the goods are regarded as a concentrated mass body, each edge node is connected through a flexible cable to form a spatial tension network structure, and a dynamic control equation of the overall UAV cluster is established relying on the tension balance constraint and the angular momentum conservation constraint; according to the task track input and the motion trend of the center of gravity of the goods, a trajectory optimal control algorithm is used to solve the dynamic control equation, to form an expected three-dimensional path of the center of gravity of the goods; the expected three-dimensional path is mapped to the flight path of the UAV cluster based on the load-node structure coupling model, to obtain the overall motion trajectory.

[0094] Specifically, first, the virtual leader UAV constructs a sling point space distribution model according to the calculated total load data and the three-dimensional space positions of each edge node. Let the three-dimensional position vector of the i-th edge node be

[0095]

[0096] The sling point space distribution model takes the position vector of the edge node as the tension application point, and defines the cable connection configuration in combination with the spatial vector difference between each edge node and the center of gravity of the goods. The total load data is W total , the tension distribution data is T i , and the spatial position of the center of gravity of the goods is calculated according to the tension weighting:

[0097]

[0098] The cable vector is defined as:

[0099] l i =r c -r i

[0100] The set {l i} constitutes the sling point space distribution model.

[0101] Thus, the spatial mechanical connection topology of the UAV cluster to the goods is established, providing a geometric basis for subsequent coupling modeling.

[0102] Next, a velocity vector is formed based on the rate of change of the cargo centroid position over time, and combined with an acceleration vector provided by the inertial measurement unit, a centroid state vector is constructed that includes position, velocity, and acceleration. Let the position of the cargo centroid be r c (t), the velocity be v c (t), and the acceleration be a c (t), and the state vector be:

[0103]

[0104] By continuous sampling and estimation of the centroid state vector, a state prediction algorithm is used to predict the centroid motion trend, which represents the future displacement trajectory and dynamic response trend of the cargo. The centroid motion trend is represented by the following state update formula:

[0105] x c (t+Δt)=A·x c (t)+B·u(t)

[0106] where A is the state transition matrix, B is the control input matrix, u(t) is the input disturbance vector, and the state vector can be predicted and estimated by extended Kalman filtering.

[0107] On the basis of this centroid motion trend, mass distribution constraints, tension balance constraints, and attitude consistency constraints are introduced to limit the upper limit of the thrust output of the edge nodes, the balanced and coordinated relationship of the internal cable tension, and the synchronization of the attitudes of all edge nodes, respectively. The mass distribution constraint is represented as follows:

[0108]

[0109] The tension balance constraint is represented as follows:

[0110]

[0111] The attitude consistency constraint is represented as follows:

[0112]

[0113] where R i is the attitude matrix of the i-th edge node, and ∈ sync is the consistency threshold.

[0114] Under these three constraints, a load-node structure coupling model is established, which describes the spatial mechanical coupling relationship between the centroid state change of the cargo and the flight state of the unmanned aerial vehicle, and establishes the mapping logic between the cluster power output and the cargo response.

[0115] Subsequently, a multi-body dynamics modeling method is introduced, modeling the cargo as a lumped mass, modeling all the cables as flexible tension constraints, and modeling all the edge nodes as spatially moving rigid bodies. In this multi-body dynamics model, tension balance constraints are established to ensure that all the cable tension vectors cancel out the gravitational and inertial loads, while angular momentum conservation constraints are introduced to maintain rotational stability in three-dimensional space, forming the cluster dynamics control equations, with the cargo mass m c The tension action is introduced to build the force balance equation:

[0116]

[0117] The angular momentum conservation equation is expressed as:

[0118]

[0119] where I c is the cargo moment of inertia tensor, and ω c is the angular velocity vector. This dynamics control equation couples the node tension, pose, spatial position, and cargo mass center response.

[0120] According to the task trajectory input information and the predicted mass center motion trend, a trajectory optimal control algorithm is used to solve the above dynamics control equation. The desired path is defined as The objective is to minimize the following performance index function:

[0121]

[0122] where p i is the tension change cost coefficient, is the desired tension curve, and the tension trajectory and mass center path are obtained by solving the optimal control algorithm such as LQR, NMPC, or Pontryagin's minimum principle. This trajectory optimal control algorithm is based on constrained optimization and nonlinear trajectory iterative correction mechanism, and under the premise of considering tension constraints, dynamic limits, and response time, the mass center desired path is output, which is the optimal three-dimensional trajectory prediction of the cargo under the current dynamic model.

[0123] Finally, based on the load-node structure coupling model, the mass center desired path is converted into the overall motion trajectory of the UAV cluster through spatial mechanics mapping. The mass center path r c (t) is mapped to the overall flight path of the UAV cluster:

[0124] r i (t) = r c (t) - l i · u i (t)

[0125] where is the cable direction unit vector, l i is the cable length constant. The global motion trajectory ensures the cluster global behavior highly consistent with the cargo centroid path, thus realizing stable cooperative control of hoisting in complex dynamic environment.

[0126] Further, after completing the global motion trajectory generation, the virtual leader drone broadcasts it to all edge nodes as a set of three-dimensional expected path vectors through the intra-cluster communication link. The global motion trajectory consists of a sequence of expected centroid spatial positions in continuous time steps, cargo dynamic state vectors, and attitude evolution information, serving as a path reference framework for global cooperative flight of the cluster. Upon receiving the global motion trajectory, each edge node does not directly execute it, but uses it as a path search constraint and tension control optimization target input, and initiates the trajectory-tension cooperative solving process based on the ant colony algorithm.

[0127] Specifically, each edge node constructs a local path search graph, which consists of the current position of the node, adjacent path states, dynamic attitude constraints, and cable geometric boundaries, forming a feasible solution graph of the path space. The ant colony algorithm constructs a heuristic search model based on the path search graph, where each "ant" represents a feasible combination of node motion trajectory and tension output solution. The path search process is guided by the current expected path point in the global motion trajectory as the target, and the transition probability is controlled by pheromone intensity τ and heuristic function η:

[0128]

[0129] where, is the set of available actions at the current state, η ij (t) is composed of the distance between the current position and the global expected path, the attitude change rate, and the tension balance degree, reflecting the degree of fit of the path to the global target.

[0130] In each iteration, the edge node evaluates the tension combination solution generated by the ant colony for the system's tension balance and attitude consistency indicators, and defines the objective function:

[0131]

[0132] where, derived from the global motion trajectory mapping, is the local tension target, ΔR i (t) is the attitude change amount. The higher the quality of the path tension pair, the higher the intensity it obtains in the ant colony pheromone update, enhancing its probability advantage in subsequent search.

[0133] After multiple iterations, the edge node selects the flight trajectory and tension output corresponding to the optimal ant colony path as the execution command of the current control period, and shares the results with the adjacent edge node for synchronization, ensuring the overall coordination of tension distribution within the cluster. The distributed path optimization mechanism uses the global search capability and local feedback structure of the ant colony algorithm, so that each edge node can independently find the optimal reference tension value and adaptive node motion trajectory while following the overall motion trajectory, thereby achieving adaptive tension control and structural stability maintenance of the collaborative hoisting system.

[0134] S140, during flight, if it is determined that the first unmanned aerial vehicle in the plurality of unmanned aerial vehicles has a hoisting rope breakage, according to a predetermined load redistribution scheme, synchronously distribute the tension to each second unmanned aerial vehicle.

[0135] In one possible implementation, before synchronously distributing the tension to each second unmanned aerial vehicle according to the predetermined load redistribution scheme, if it is determined that the first unmanned aerial vehicle in the plurality of unmanned aerial vehicles has a hoisting rope breakage during flight, the method further comprises: constructing a hoisting point spatial distribution model of the unmanned aerial vehicle cluster in combination with the total load data and the node positions; for each edge node, constructing a virtual failure model of the hoisting rope breakage scenario, under the premise of not changing the total mass of the goods, respectively excluding the load distribution data corresponding to each failed node in the plurality of edge nodes, and solving a new set of tension distribution for the remaining nodes, so that the tension resultant force conservation and angular momentum constraint are satisfied, and at the same time, the disturbance cost function compared with the hoisting point spatial distribution model is minimized; using a quadratic programming solver to perform offline enumeration calculation on the disturbance cost function, and generating an optimal redistribution solution corresponding to the hoisting rope breakage for each failed node, each optimal redistribution solution corresponding to a redistribution configuration of the failure scenario; obtaining the load redistribution scheme from the optimal redistribution solution set corresponding to all failed nodes.

[0136] Specifically, first, the center node constructs a hoisting point spatial distribution model of the unmanned aerial vehicle cluster according to the total load data obtained at present and the spatial position vectors of each edge node. The hoisting point spatial distribution model takes each edge node as a tension application point, takes the center of gravity of the goods as a center point, defines each cable connection vector, forms a tension action direction set, and constructs a cable mechanics topology structure based thereon, which is used to represent the current tension transmission path, direction distribution and node configuration as a steady-state force reference.

[0137] Subsequently, for each edge node, its corresponding virtual failure model of broken sling is constructed. The virtual failure model is based on the "single node breakage assumption", which eliminates the tension data and connection vector of the node from the original load distribution model under the condition of keeping the total mass of the goods and external disturbance force unchanged, retains the effective spatial configuration and historical tension information of the remaining nodes, and re-establishes the force equation of the remaining cluster according to the new node configuration, including the tension force balance equation and the angular momentum conservation equation, as the boundary constraint for solving the redistribution strategy.

[0138] With the original sling point spatial distribution model as the reference, the disturbance cost function is defined to measure the deviation degree of the state after failure from the original state. The disturbance cost function includes the node position disturbance term, the tension reconstruction offset term and the force direction offset term, which is mathematically expressed as:

[0139]

[0140] Wherein, J is the disturbance cost function, which is the optimization objective function this time, indicating the overall disturbance degree introduced by the tension redistribution state after the sling breakage compared with the original state. The optimization goal is to make the function value as small as possible while meeting the mechanical constraint. i∈S ′ is the index set of the edge node, indicating the number of all edge nodes except the failed node in the current assumed breakage scenario. is the tension distribution value recalculated for the edge node i under the condition of node failure, that is, the updated pulling force that the node needs to bear. is the original tension distribution value before the node failure, indicating the historical pulling force task of the node under the steady state working condition. is the change amplitude of the node tension value, which measures the deviation degree of each edge node from its original tension after the tension is redistributed, and reflects the disturbance degree to the original mechanical state. u i is the pulling force direction unit vector of the edge node i, indicating the direction from the node position to the center of gravity of the goods, which is used to project the tension vector into space for force calculation. is the resultant force vector of all remaining node tension vectors in three-dimensional space after the current tension is redistributed. W total is the estimated total weight vector of the goods, that is, the direction and size of the resultant force generated by the ideal sling, which is usually set as a constant vector with the gravity direction downward. γ is the weight coefficient of the force constraint term, which is used to adjust the attention degree to the overall force balance in tension reconstruction. The larger the value is, the more the optimization result tends to preferentially meet the force conservation condition. is the deviation between the current redistribution tension resultant force and the target total load, which constrains the synthetic tension provided by the unmanned vehicle cluster to be consistent with the actual force of the goods, so as to ensure that there is no overall imbalance. τ newThe angular momentum vector after redistribution of tension is defined as the sum of the moments of each remaining node relative to the center of gravity of the cargo. τ orig The angular momentum vector in the original stable state is τ0, which should be close to zero in the ideal case, indicating no spin and no twist. ||τ new -τ orig || 2 To represent the degree of disturbance in rotational balance after tension reconstruction, it reflects whether it will cause the cargo to rotate or attitude instability. δ is the weight coefficient of the angular momentum constraint term, used to control the importance of attitude stability in the optimization algorithm. The larger the weight, the more inclined to keep the original angular momentum unchanged, ensuring the minimum of rotational disturbance.

[0141] For the above disturbance cost function, a quadratic programming solver is used for constrained optimization calculation, and non-negative tension and maximum thrust boundary conditions are set to ensure the feasibility and physical validity of the solution. To improve the timeliness of fault response, this solving process is performed as an offline calculation task before flight, and for each edge node, an independent failure scenario is constructed, and the corresponding optimal tension redistribution solution is solved in turn to form a set of redistribution configuration sets covering all edge nodes.

[0142] Finally, the optimal tension redistribution solutions corresponding to the failure scenarios of all edge nodes are integrated to form a load redistribution scheme. This load redistribution scheme is stored as a mapping table in the control module of the central node. Once a rope breakage occurs in a certain edge node during actual flight, the pre-calculated redistribution solution can be called immediately, skipping the real-time solving process, to achieve high responsiveness and robustness of the mechanical adaptive control.

[0143] In one possible implementation, if it is determined that a first unmanned aerial vehicle of a plurality of unmanned aerial vehicles has a rope breakage during flight, according to a predetermined load redistribution scheme, the tension is distributed to each second unmanned aerial vehicle synchronously, specifically including: determining a failure node corresponding to the first unmanned aerial vehicle; calling an optimal redistribution solution corresponding to the failure node corresponding to the first unmanned aerial vehicle; determining a remaining node corresponding to each second unmanned aerial vehicle in the optimal redistribution solution, thereby determining an updated tension value and a cable direction unit vector that each second unmanned aerial vehicle should bear; and respectively broadcasting the updated tension value and the cable direction unit vector to each second unmanned aerial vehicle.

[0144] Specifically, first, the central node or each edge node confirms that the first unmanned aerial vehicle has a rope breakage by monitoring the tension sensor data, vertical acceleration, and flight attitude information of each unmanned aerial vehicle in the unmanned aerial vehicle cluster in real time, in combination with indicators such as a sudden drop in tension value to near zero, a sudden change in acceleration direction, and attitude instability. Then, the first unmanned aerial vehicle is designated as a failure node in the cluster control architecture, and the time of breakage and the last stable state parameters are recorded as the event source triggering the load redistribution mechanism.

[0145] After determining the failed node, the center node immediately calls the optimal reassignment solution corresponding to the failed node from the locally stored offline reassignment solution library. The optimal reassignment solution is the minimum disturbance configuration generated by the quadratic programming solver based on the suspension point spatial distribution model, the tension-coupled structure and the disturbance cost function in the flight mission initialization stage, which includes the new tension target value that each second unmanned aerial vehicle should bear under the current broken situation and the updated cable direction unit vector.

[0146] According to the optimal reassignment solution, the system identifies all the remaining edge nodes as the current second unmanned aerial vehicle set, and extracts the tension instruction parameters required for each second unmanned aerial vehicle to update, including the target tension value and the cable direction unit vector. The unit vector is calculated based on the newly constructed mechanical equilibrium point after the rope is broken and the spatial position of the remaining nodes, and is used to guide the attitude adjustment and direction control of each edge node, so that the tension direction is consistent with the new force center.

[0147] Subsequently, the center node synchronously sends the above-mentioned node-by-node data packet containing tension and direction instructions to each second unmanned aerial vehicle in a broadcast manner, and each edge node receives and starts the local controller after receiving the data packet. The local controller adjusts the propeller speed through a feedback gain adjustment control algorithm according to the error between the current tension measurement value and the target tension value, and then adjusts the output vertical force vector dynamically, so that the actual tension gradually approaches the set value.

[0148] At the same time, each second unmanned aerial vehicle uses the attitude control module to fine-tune the flight attitude, and adjusts the spatial position and orientation in real time according to the received cable direction unit vector, so that the cable direction vector is consistent with the expected direction calculated by the system in the three-dimensional space, thereby restoring the tension balance and suppressing the rotation trend of the system in the physical space, and finally realizing the rapid recovery of system stability and the seamless continuation of the hoisting task. The whole process has the characteristics of distributed control architecture combined with center guidance, ensuring the unity of response timeliness and control coordination.

[0149] S150, setting the first unmanned aerial vehicle as a proxy center node, and receiving an updated motion trajectory of the proxy center node for the unmanned aerial vehicle cluster.

[0150] After confirming that the first unmanned aerial vehicle has broken the hoisting rope and completing the failure identification, the first unmanned aerial vehicle is switched to a proxy center node, and its role is changed from a physical load participant to a logical control coordinator. In the specific implementation process, the virtual leader unmanned aerial vehicle determines whether the communication link quality, remaining computing resources and spatial position stability meet the requirements of the center node according to the state parameters recorded before the rope is broken, and if they meet the requirements, the virtual leader unmanned aerial vehicle immediately issues a role switching instruction and broadcasts a state announcement that the node is switched to a proxy center node in the cluster.

[0151] After the first UAV takes over the center node function, based on the resource redundancy of not bearing actual load, using its inertial measurement unit and the tension and trajectory data uploaded by surrounding edge nodes, it reconstructs the current cargo barycenter state and the spatial structure of the lifting point in real time, combines the task input and the dynamic mapping model to regenerate the updated motion trajectory of the UAV cluster. The updated motion trajectory contains the expected path of the cargo barycenter, the node attitude constraint and the tension scheduling reference. After generation, it is broadcast by the proxy center node to each edge node through the wide area communication link, ensuring the continuity of path control and the stability of cooperation after the structure mutation.

[0152] The embodiment also discloses a large-load UAV cooperative lifting device, which is used for executing the large-load UAV cooperative lifting method according to any one of the above. Figure 2 The device is arranged in a virtual leader UAV in a UAV cluster, and comprises an acquisition module 201, a processing module 202 and an output module 203.

[0153] The acquisition module 201 is used for acquiring the node position and the load allocation data of each edge node.

[0154] The processing module 202 is used for calculating total load data of the loads lifted by the plurality of edge nodes based on the load allocation data.

[0155] The processing module 202 is used for determining the overall motion trajectory of the UAV cluster based on the total load data and the node position, and sending the overall motion trajectory to the edge nodes, so that the edge nodes find the required reference tension value and the node motion trajectory based on the ant colony algorithm.

[0156] The output module 203 is used for, if it is determined that the first UAV in the plurality of UAVs occurs rope breakage during flight, synchronously allocating the tension to each second UAV according to a predetermined load redistribution scheme, the second UAV being a UAV in the UAV cluster except the first UAV.

[0157] The output module 203 is used for setting the first UAV as a proxy center node, and receiving the updated motion trajectory of the UAV cluster from the proxy center node.

[0158] In a possible implementation, the acquisition module 201 is configured to acquire load distribution data calculated by each edge node. The edge node acquires, in real time, the sling tension, the vertical acceleration and the pitch and roll angles of the edge node by using the on-board tension sensor, the accelerometer and the attitude sensor, calculates the vertical component of force borne by the edge node, and then corrects the sling inertia change based on the measured inertial acceleration of the edge node, calculates the effective gravity acceleration correction factor under unit tension, and obtains the corrected load distribution data.

[0159] The processing module 202 is configured to calculate the cargo center of gravity position according to the positions of the plurality of nodes by using all the load distribution data as weights.

[0160] The processing module 202 is configured to determine the tension action vector of the edge node according to the direction vector formed between the node position and the cargo center of gravity position.

[0161] The processing module 202 is configured to obtain the action component of each load distribution data in the vertical direction by projecting the tension action vector in the direction of gravity.

[0162] The processing module 202 is configured to perform inertial correction on the action component according to the acceleration data of the unmanned aerial vehicle cluster to obtain a corrected action component.

[0163] The processing module 202 is configured to perform weighted fusion on the corrected action component according to the relative distance between the node position and the cargo center of gravity position to obtain total load data.

[0164] In a possible implementation, the processing module 202 is configured to construct a sling space distribution model of the unmanned aerial vehicle cluster in combination with the total load data and the node position.

[0165] The processing module 202 is configured to establish a cargo mass center state vector based on the sling space distribution model and in combination with the change rate of the cargo center of gravity position and the instantaneous acceleration data, and further predict the cargo mass center motion trend.

[0166] The processing module 202 is configured to construct a load-node structure coupling model by introducing a mass distribution constraint, a tension balance constraint and an attitude consistency constraint based on the cargo mass center motion trend, so as to establish a dynamic mapping relationship between the cargo mass center motion trend and the flight path of the unmanned aerial vehicle cluster.

[0167] The processing module 202 is configured to introduce a multi-body system dynamics modeling method, treat the cargo as a concentrated mass body, and establish the dynamic control equation of the whole unmanned aerial vehicle cluster by using the flexible cable to connect each edge node to form a spatial tension network structure and relying on the tension balance constraint and the angular momentum conservation constraint.

[0168] The processing module 202 is configured to solve a dynamic control equation by using a trajectory optimization control algorithm according to the task track input and the cargo centroid motion trend, and form a desired three-dimensional path of the cargo centroid.

[0169] The processing module 202 is configured to map the desired three-dimensional path to a flight path of the unmanned aerial vehicle cluster based on a load-node structure coupling model, and obtain an overall motion trajectory.

[0170] In a possible implementation, the acquisition module 201 is configured to acquire a reference tension value calculated by an edge node based on the overall motion trajectory and a node motion trajectory, where the edge node constructs a local path search graph, the path search graph is composed of a current position of the node, an adjacent path state, a dynamic attitude limit and a cable geometry boundary, a feasible solution graph of a path space is formed, a heuristic search model is constructed based on the path search graph, a current desired path point in the overall motion trajectory is taken as a target guide, and a transfer probability is controlled by using pheromone intensity and a heuristic function. In each iteration, a satisfaction degree of a path tension combination solution generated by the ant colony to a tension balance degree and an attitude consistency index of the system is obtained, and a target function is defined. After multiple iterations, the edge node selects a node motion trajectory and the reference tension value corresponding to an optimal ant colony path as an execution command of a current control period, and sends the node motion trajectory and the reference tension value to the center node.

[0171] In a possible implementation, the processing module 202 is configured to construct a sling point space distribution model of the unmanned aerial vehicle cluster in combination with total load data and node positions.

[0172] The processing module 202 is configured to construct a virtual failure model of a sling rope breakage situation for each edge node, and under the premise of not changing a total mass of the cargo, respectively remove load allocation data corresponding to each failure node in the plurality of edge nodes, and solve a new tension allocation for the remaining nodes, so that a disturbance cost function compared with the sling point space distribution model is minimized while a tension resultant force conservation and an angular momentum constraint are satisfied.

[0173] The processing module 202 is configured to perform offline enumeration calculation on the disturbance cost function by using a quadratic programming solver, and generate an optimal re-allocation solution corresponding to the sling rope breakage for each failure node, where each optimal re-allocation solution corresponds to a re-allocation configuration of a failure situation.

[0174] The processing module 202 is configured to obtain a load re-allocation scheme from a set of optimal re-allocation solutions corresponding to all failure nodes.

[0175] In a possible implementation, the processing module 202 is configured to determine a failure node corresponding to the first unmanned aerial vehicle.

[0176] The processing module 202 is configured to call the optimal re-distribution solution corresponding to the failed node of the first UAV.

[0177] The processing module 202 is configured to determine the residual node corresponding to each second UAV in the optimal re-distribution solution, so as to determine the updated tension value and the cable direction unit vector that should be borne by each second UAV.

[0178] The output module 203 is configured to broadcast the updated tension value and the cable direction unit vector to each second UAV respectively.

[0179] In a possible implementation, the acquisition module 201 is configured to acquire real-time state parameters broadcast by each UAV in the UAV cluster in a take-off preparation stage, and the real-time state parameters include position information, residual computing resources, communication signal quality, historical trajectory stability indicators, and node reliability levels.

[0180] The output module 203 is configured to calculate the centrality score of the virtual leader UAV according to the real-time state parameters, and broadcast the centrality score of the virtual leader UAV.

[0181] The processing module 202 is configured to compare the centrality scores of the plurality of UAVs independently after completing the omnidirectional score synchronization, and if it is determined that the centrality score of the virtual leader UAV is the highest, the virtual leader UAV is determined as the central node.

[0182] It should be noted that the apparatus provided in the above embodiments is only used as an example for dividing the above functional modules to achieve the functions thereof, and in actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above-described functions. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be repeated here.

[0183] The embodiment further discloses an electronic device, which refers to Figure 3 The electronic device can include at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0184] The communication bus 302 is configured to realize the connection and communication between the components.

[0185] The user interface 303 can include a display screen (Display) and a camera (Camera), and the optional user interface 303 can further include a standard wired interface and a wireless interface.

[0186] The network interface 304 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).

[0187] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the server through various interfaces and lines, performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. The processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU is mainly used to process the operating system, user interface, and application programs; the GPU is used to render and draw the content to be displayed on the display screen; and the modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0188] The memory 305 can include a random access memory (RAM) and a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 305 can optionally be at least one storage device located away from the above-mentioned processor 301. As a computer storage medium, the memory 305 can include an operating system, a network communication module, a user interface 303 module, and an application program of a large-load unmanned aerial vehicle cooperative hoisting method.

[0189] In Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an interface for user input, and obtain data input by the user; and the processor 301 can be used to invoke an application program of a large-load unmanned aerial vehicle cooperative hoisting method stored in the memory 305, and when executed by one or more processors 301, the electronic device executes the method of one or more of the above embodiments.

[0190] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0191] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0192] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different parts can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical or other forms.

[0193] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0194] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0195] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory 305 and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory 305 includes: a U disk, a mobile hard disk, a magnetic or optical disk and various program code storage media.

[0196] The present application also discloses a computer readable storage medium, which stores instructions. When executed by one or more processors 301, the instructions cause an electronic device to perform the method of one or more of the above embodiments.

[0197] The above is only exemplary embodiments of the present disclosure, which cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practice of the present disclosure. The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional techniques in the art that are not described in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A large-load unmanned aerial vehicle cooperative hoisting method, characterized in that, The method is applied to a virtual leader unmanned aerial vehicle in a cluster of unmanned aerial vehicles, and the method comprises: acquiring node positions of each edge node and load allocation data, wherein the virtual leader unmanned aerial vehicle is a center node of the cluster of unmanned aerial vehicles, and unmanned aerial vehicles other than the virtual leader unmanned aerial vehicle in the cluster of unmanned aerial vehicles are edge nodes; calculating total load data of loads hoisted by the plurality of edge nodes based on the load allocation data; determining an overall motion trajectory of the cluster of unmanned aerial vehicles based on the total load data and the node positions, and sending the overall motion trajectory to the edge nodes so that the edge nodes find required reference tension values and node motion trajectories based on an ant colony algorithm; if it is determined that a first unmanned aerial vehicle in the plurality of unmanned aerial vehicles has a hoisting rope breakage during flight, synchronously distributing tension to each second unmanned aerial vehicle according to a predetermined load redistribution scheme, the second unmanned aerial vehicle being an unmanned aerial vehicle other than the first unmanned aerial vehicle in the cluster of unmanned aerial vehicles; setting the first unmanned aerial vehicle as a proxy center node, and receiving an updated motion trajectory of the proxy center node for the cluster of unmanned aerial vehicles; before the synchronously distributing tension to each second unmanned aerial vehicle according to the predetermined load redistribution scheme if it is determined that a first unmanned aerial vehicle in the plurality of unmanned aerial vehicles has a hoisting rope breakage during flight, the method further comprises: constructing a hoisting point spatial distribution model of the cluster of unmanned aerial vehicles in combination with the total load data and the node positions; constructing a virtual failure model of a hoisting rope breakage situation for each edge node, and under the premise of not changing the total mass of goods, respectively removing load allocation data corresponding to each failure node in the plurality of edge nodes, and solving a new set of tension distribution for the remaining nodes so that a disturbance cost function compared with the hoisting point spatial distribution model is minimized while satisfying tension resultant force conservation and angular momentum constraints; performing offline enumeration calculation on the disturbance cost function using a quadratic programming solver, generating an optimal redistribution solution corresponding to the hoisting rope breakage for each failure node, each optimal redistribution solution corresponding to a redistribution configuration of a failure situation; obtaining the load redistribution scheme from a set of optimal redistribution solutions corresponding to all the failure nodes.

2. The method of claim 1, wherein, The calculation of the total load data of the loads hoisted by the plurality of edge nodes based on the load allocation data specifically comprises: acquiring load allocation data calculated by each edge node, wherein the edge node acquires hoisting rope tension, its own vertical acceleration and pitch and roll angles in real time through a tension sensor, an accelerometer and an attitude sensor carried by the edge node, calculates a vertical component force borne by the edge node, and then calculates an effective gravity acceleration correction factor under unit tension based on inertial acceleration measured by the edge node to obtain corrected load allocation data; calculating a center of gravity position of goods according to a plurality of node positions with all load allocation data as weights; determining a tension action vector of the edge node according to a direction vector formed between the node positions and the center of gravity position of the goods; and Projecting the tension acting vector on the direction of gravity, the projection of each load distribution data in the vertical direction is obtained; According to the acceleration data of the UAV cluster, the acting component is inertially corrected to obtain a corrected acting component; According to the relative distance between the node position and the center of gravity position of the cargo, the corrected acting component is weighted and fused to obtain the total load data.

3. The method of claim 1, wherein, The total load data and the node position are used to determine the overall motion trajectory of the UAV cluster, specifically including: The total load data and the node position are combined to construct a sling point space distribution model of the UAV cluster; Based on the sling point space distribution model, the change rate of the center of gravity position of the cargo and the instantaneous acceleration data are combined to establish a cargo centroid state vector, and the motion trend of the cargo centroid is predicted; Based on the motion trend of the cargo centroid, mass distribution constraints, tension balance constraints and attitude consistency constraints are introduced to construct a load-node structure coupling model, thereby establishing a dynamic mapping relationship between the motion trend of the cargo centroid and the flight path of the UAV cluster; A multi-body system dynamics modeling method is introduced, the cargo is regarded as a concentrated mass body, each edge node is connected by a flexible cable to form a spatial tension network structure, and the dynamics control equation of the overall UAV cluster is established based on the tension balance constraint and the angular momentum conservation constraint; According to the task flight path input and the motion trend of the cargo centroid, a trajectory optimal control algorithm is used to solve the dynamics control equation to form an expected three-dimensional path of the cargo centroid; Based on the load-node structure coupling model, the expected three-dimensional path is mapped to the flight path of the UAV cluster to obtain the overall motion trajectory.

4. The method of claim 1, wherein, The overall motion trajectory is sent to the edge node, so that the edge node finds the required reference tension value and node motion trajectory based on the ant colony algorithm, specifically including: The edge node calculates the reference tension value and the node motion trajectory based on the overall motion trajectory, wherein the edge node constructs a local path search graph, the path search graph is composed of the current position of the node, the adjacent path state, the dynamic attitude limit and the cable geometric boundary, forms a feasible solution graph of the path space, and constructs a heuristic search model based on the path search graph. The current expected path point in the overall motion trajectory is taken as the target guide, and the transfer probability is controlled through pheromone intensity and heuristic function; in each iteration, the satisfaction degree of the path tension combination solution generated by the edge node evaluation ant colony to the tension balance degree and attitude consistency index of the system is obtained, and the objective function is defined; after multiple iterations, the edge node selects the node motion trajectory and the reference tension value corresponding to the optimal ant colony path as the execution command of the current control period, and sends the node motion trajectory and the reference tension value to the center node.

5. The method of claim 1, wherein, If it is determined that a first UAV in the plurality of UAVs has a sling rope break during flight, according to a predetermined load redistribution scheme, the tension is distributed to each second UAV synchronously, specifically including: determining a failure node corresponding to the first UAV; calling an optimal re-distribution solution corresponding to the failure node corresponding to the first UAV; determining a residual node corresponding to each of the second UAVs in the optimal re-distribution solution, thereby determining an updated tension value and a cable direction unit vector to be borne by each of the second UAVs; broadcasting the updated tension value and the cable direction unit vector to each of the second UAVs, respectively.

6. The method of claim 1, wherein, Before obtaining the node position and the load distribution data of each edge node, the method further comprises: obtaining real-time state parameters broadcasted by each UAV in the UAV cluster in a take-off preparation stage, the real-time state parameters including position information, residual computing resources, communication signal quality, historical trajectory stability indicators, and node reliability levels; calculating a centrality score of the virtual leader UAV according to the real-time state parameters, and broadcasting the centrality score of the virtual leader UAV; after completing the omnidirectional score synchronization and comparing the centrality scores of a plurality of the UAVs independently by each UAV, if it is determined that the centrality score of the virtual leader UAV is the highest, the virtual leader UAV is determined as the center node.

7. A large-load unmanned aerial vehicle cooperative hoisting device, characterized in that, The device is used to perform a large-load UAV cooperative hoisting method according to any one of claims 1-6, and is arranged in a virtual leader UAV in a UAV cluster. The device comprises an obtaining module (201), a processing module (202), and an output module (203), wherein: The obtaining module (201) is configured to obtain the node position and the load distribution data of each edge node, wherein the virtual leader UAV is a center node of the UAV cluster, and the UAVs other than the virtual leader UAV in the UAV cluster are edge nodes. The processing module (202) is configured to calculate total load data of the loads hoisted by a plurality of the edge nodes based on the load distribution data. The processing module (202) is configured to determine an overall motion trajectory of the UAV cluster based on the total load data and the node position, and send the overall motion trajectory to the edge nodes, so that the edge nodes find a required reference tension value and a node motion trajectory based on an ant colony algorithm. The output module (203) is configured to, if it is determined that a first UAV in a plurality of UAVs has a hoisting rope breakage during flight, synchronously distribute tension to each second UAV according to a predetermined load re-distribution scheme, the second UAV being a UAV other than the first UAV in the UAV cluster. The output module (203) is configured to set the first UAV as a proxy center node, and receive an updated motion trajectory of the proxy center node for the UAV cluster.

8. An electronic device, comprising: The electronic device comprises a processor (301), a communication bus (302), a user interface (303), a network interface (304), and a memory (305) for storing instructions, the user interface (303) and the network interface (304) are both used for communicating with other devices, the communication bus (302) is used for realizing the connection communication between components in the electronic device, and the processor (301) is used for executing the instructions stored in the memory (305) to make the electronic device execute the method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions, when the instructions are executed, the method in any one of claims 1-6 is executed.

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