Multi-unmanned aerial vehicle cooperative hoisting system

Abstract

The invention relates to the technical field of unmanned aerial vehicle cooperative hoisting, and provides a multi-unmanned aerial vehicle cooperative hoisting system and an unmanned aerial vehicle module, the unmanned aerial vehicle module is composed of two or more unmanned aerial vehicles, and each unmanned aerial vehicle is equipped with an airborne computer, a flight control assembly, a power assembly, a space positioning sensor, a tension sensor and a communication unit; the spatial positioning sensor is used for acquiring spatial position data of the unmanned aerial vehicle; the tension sensor is used for acquiring tension data of goods hoisted by the unmanned aerial vehicles, and the airborne computer is in data connection with a communication unit of the unmanned aerial vehicles and is used for calculating real-time lift force and attitude adjustment amount of the unmanned aerial vehicles based on the tension data and spatial data acquired by the unmanned aerial vehicles and controlling flight parameters of the multiple unmanned aerial vehicles according to the real-time lift force and attitude adjustment amount; according to the invention, through a multi-source fusion positioning and sliding mode adaptive hybrid control algorithm, cooperative positioning and dynamic load balancing of the unmanned aerial vehicle group are realized, and the overall stability and anti-interference capability of the hoisting system under complex disturbance are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative lifting by unmanned aerial vehicles, and more specifically to a multi-unmanned aerial vehicle collaborative lifting system. Background Art

[0002] With the development of industry and intelligent logistics, the application scenarios of unmanned aerial vehicles in fields such as power inspection, emergency disaster relief, and logistics distribution continue to expand. Existing medium and small unmanned aerial vehicles are limited by the power and structural load of a single platform and are difficult to meet the transportation requirements of ultra-large objects. Although traditional multi-rotor unmanned aerial vehicles can increase the load capacity by increasing the number of rotors, the size and energy consumption of a single platform increase exponentially, and the mobility decreases significantly.

[0003] However, most existing unmanned aerial vehicles operate individually and cannot meet the transportation requirements of large goods. At the same time, they lack the ability to cooperate. For the transportation of large goods, traditional ground or air transportation tools are usually required, which may have limitations in certain environments.

[0004] There are still the following deficiencies in the existing background art:

[0005] 1. Lack of collaborative operation ability: The current mainstream multi-aircraft formation products on the market, but their control logic is limited to formation maintenance, lacking the ability to allocate collaborative forces for heavy-load transportation. The existing publicly disclosed multi-aircraft lifting systems only achieve simple position synchronization and do not solve the dynamic control problem under multi-physical field coupling, and cannot meet the accuracy requirements of engineering-level heavy-load transportation.

[0006] 2. Insufficient adaptability to complex environments: The control strategies of existing collaborative lifting systems are based on the assumption of ideal flight conditions and lack a real-time response mechanism to sudden meteorological disturbances. When encountering extreme working conditions such as lateral winds and vertical airflows, the differences in sling lengths and the lift deviations of each aircraft will cause torsional vibrations of the load, and the existing control algorithms cannot compensate for aerodynamic disturbances in time, resulting in an increased risk of system instability. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides a multi-unmanned aerial vehicle collaborative lifting system, which realizes collaborative positioning and dynamic load balancing of an unmanned aerial vehicle group through a multi-source fusion positioning and sliding mode adaptive hybrid control algorithm, ensures the overall stability and anti-interference ability of the lifting system under complex disturbances, and realizes the collaborative optimization of centralized decision-making and distributed execution through a hybrid computing architecture of a ground base station and a self-organizing network to solve the problems in the prior art.

[0008] The technical solution of the present invention is as follows:

[0009] A multi-unmanned aerial vehicle collaborative lifting system, comprising:

[0010] Drone module, which is composed of two or more drones. Each drone is equipped with an on-board computer, a flight control component, a power component, a spatial positioning sensor, a tension sensor, and a communication unit;

[0011] The spatial positioning sensor includes a differential GPS, an RTK, and a UWB positioning sensor, and a binocular camera or a lidar can also be used to measure the relative spatial position between drones or the absolute position based on the ground coordinate system, so as to obtain the position data of the drones;

[0012] The tension sensor is used to detect the change in the tension load of each drone during the hoisting process, obtain the tension data of the drones hoisting goods, and provide data support for balancing the power output of each drone;

[0013] The on-board computer is data-connected to the communication unit and is used to calculate the real-time lift and attitude adjustment amount of the drones based on the tension data and position data of the spatial positioning sensor and the tension sensor on one of the drones in the hoisting formation through self-organizing network technology, and control the flight parameters of multiple drones accordingly;

[0014] The communication unit is used for data transmission between drones.

[0015] Preferably, the on-board computer includes an error calculation unit and a drone control calculation unit;

[0016] The error calculation unit dynamically calculates the displacement adjustment amount required to adjust the attitude of the drone formation through the tension and spatial position data collected in real time, so as to achieve load balance, formation stability, and adaptation to external disturbances including wind force and load changes;

[0017] The drone control calculation unit is used to calculate the lift and attitude adjustment amount required for the drones in real time according to the displacement adjustment amount calculated by the error calculation unit, and transmit it to the drones through the communication unit to achieve the dynamic adjustment of the cooperative hoisting of the drones.

[0018] Preferably, the specific calculation process of the error calculation unit for calculating the vertical displacement of the drones is as follows:

[0019] Tension data: Each drone collects the real-time tension value F i (i = 1, 2, 3,..., n); i represents the index of the drone, and n represents the total number of drones in the drone module;

[0020] Positioning data: Obtain its own absolute position w through RTK / GPS / UWB i (x i , y i , z i) or obtain the relative formation position through a binocular camera / lidar, where x and y respectively represent two horizontal directions of the UAV in space, and z represents the vertical direction of the UAV in space;

[0021] Data transmission: Aggregate data to the ground base station or the main UAV through the ad hoc network module;

[0022] Global state calculation, calculate the average tension F avg :

[0023]

[0024] Tension balance control, vertical direction adjustment, first calculate the tension error:

[0025] ΔF i = F i -F avg

[0026] Combining the robustness of sliding mode surface control and the dynamic gain adjustment of adaptive control, suppressing chattering and improving the response speed, calculate the sliding mode surface s i :

[0027] s i = ΔF i +λ∫ΔF i dt

[0028] Calculate the adaptive gain based on the sliding mode surface:

[0029]

[0030] Calculate the vertical displacement Δz i :

[0031]

[0032] where s i is the sliding mode surface, used to characterize the cumulative dynamics of the tension error, is the adaptive gain, used to dynamically adjust the control intensity, t represents the real-time running time of the system, ensuring that the adaptive gain varies dynamically with the task process; λ, γ and β are design parameters, where λ > 0, used to adjust the convergence speed, γ > 0, used to control the gain growth rate, β > 0, used to suppress chattering;

[0033] sat(s i ) is the saturation function, used to smooth the control signal; sign(s i ) is the sign function, used to extract its sign from the numerical value, that is, to judge whether the numerical value is positive, negative or zero, and return 1, -1 or 0 accordingly.

[0034] Preferably, the specific calculation process of the error calculation unit for calculating the horizontal displacement of the UAV is as follows:

[0035] Position deviation: Calculate the target position w of each UAV desired,i and the actual position w i deviation Δw i ;

[0036] Δw i =|w desired,i -w i |

[0037] Calculate the feedforward disturbance compensation:

[0038]

[0039] Calculate the position correction amount:

[0040]

[0041] where, and are the disturbance calculation values in the x / y directions in the feedforward compensation respectively; α represents the feedforward gain coefficient, calibrated through experiments, K LQR is the optimal feedback matrix calculated by the LQR algorithm obtained through offline pre-calculation, θ i represents the yaw angle of the i-th UAV;

[0042] Output the control quantity, and send (Δx i , Δy i , Δz i ) to the UAV flight controller to adjust the attitude and power.

[0043] Preferably, the UAV control calculation unit calculates the lift and attitude adjustment amounts according to the adjustment amount calculated by the error calculation unit:

[0044] Lift adjustment amount, the basic lift P base,i the power required to maintain hovering, the load compensation lift:

[0045]

[0046] where, is the gain coefficient, and the total lift is:

[0047] P i =P base,i +ΔP i

[0048] Attitude adjustment amount: The pitch angle φ i and the roll angle ψ i are determined by the horizontal displacement command (Δx i , Δyi ) Calculation:

[0049]

[0050]

[0051] Where, φ i represents the pitch angle of the i-th drone, and a positive value indicates that the drone pitches forward to generate a forward horizontal thrust, ψ i represents the roll angle of the i-th drone, and a positive value indicates that the drone rolls to the right to generate a rightward horizontal thrust, Q φ and Q ψ represent the proportional gains of pitch and roll control, which are obtained through experimental calibration;

[0052] and represent the change rate of the horizontal displacement correction amount, that is, the adjustment speed of the drone in the horizontal direction, which is used to damp the oscillation. g represents the acceleration due to gravity, and h represents the current hovering height of the drone, which is used to dynamically adjust the damping term; the yaw angle is uniformly adjusted by the formation orientation.

[0053] Preferably, the flight control component is further configured to perform cooperative constraint and fault tolerance on the drones, specifically as follows;

[0054] Formation height locking, the sum of the height adjustments of each drone ∑Δz i = 0, to maintain the overall height stability;

[0055] Saturation processing, restricting the power output P i ∈[P min , P max , to prevent motor overload;

[0056] Abnormality detection, if a certain drone has a communication timeout, enable the local decision-making mode and give priority to maintaining position stability.

[0057] Preferably, the power component can adjust the power and attitude of the drone according to the power and attitude adjustment amounts transmitted by the drone control calculation unit.

[0058] Preferably, a multi-drone cooperative hoisting system further includes a ground base station, which includes a calculation unit and a communication unit. The power and state adjustment amounts between multiple drones can be calculated by the communication unit and sent from the communication unit to the communication unit of the drone.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. Through the collaborative operation of multiple drones, the present invention effectively solves the problem of limited transportation capacity of a single drone, improves the upper limit of the lifting load of the drone, and enhances the transportation efficiency. At the same time, through the precise control of the spatial positioning sensor and the tension sensor, the balance and safety of the goods during transportation are ensured.

[0061] 2. The present invention performs real-time calculation of the vertical / horizontal displacement through the load balancing algorithm of the on-board computer error calculation unit. When the ground base station signal is unstable, it realizes the autonomous collaborative control of distributed loads of multiple drones, achieves the collaborative transportation ability of large goods, and realizes rapid attitude adjustment under unexpected conditions through the combination of sliding mode control and adaptive gain algorithm with multi-modal sensor fusion positioning, achieving stable lifting under complex meteorological conditions, and solving the core defect of weak anti-interference ability of the existing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a schematic diagram of the working connection process between the various modules and units of the present invention;

[0063] Figure 2 is a schematic diagram of the working process of the unmanned lifting of goods in the embodiment of the present invention;

[0064] Figure 3 is a schematic diagram of the process of the ground base station calculating the real-time lift and attitude adjustment amount of the drone. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0066] The present invention provides a multi-drone collaborative lifting system, including:

[0067] A drone module, which consists of two or more drones, and each drone is equipped with an on-board computer, a flight control component, a power component, a spatial positioning sensor, a tension sensor, and a communication unit;

[0068] The spatial positioning sensor includes differential GPS, RTK, and UWB positioning sensors, and can also use binocular cameras and lidar to measure the relative spatial position between drones or the absolute position based on the ground coordinate system, so as to obtain the position data of the drones;

[0069] The tension sensor is used to detect the change of the tension load of each drone during the lifting process, obtain the tension data of the drone lifting the goods, and provide data support for balancing the power output of each drone;

[0070] The onboard computer is connected to the communication unit data, and is used to calculate the real-time lift and attitude adjustment of one of the UAVs in the lifting formation based on the tension data and position data of the spatial positioning sensor and the tension sensor through the self-organizing network technology, and control the flight parameters of the multiple UAVs accordingly;

[0071] The communication unit is used for data transmission between drones.

[0072] Example:

[0073] like Figures 1 - 3 As shown, in this embodiment, a mudslide disaster occurred in a mountainous area, and ground transportation was completely interrupted. Four drones need to cooperate to lift emergency medical supplies boxes across the canyon to reach the affected village.

[0074] There is strong turbulence in the canyon area, and the hoisted objects need to be accurately dropped at a narrow landing point. Traditional cranes cannot operate due to space limitations, and the existing drone formation system has problems such as positioning drift, uneven load, and poor wind resistance, which leads to the risk of falling materials. Therefore, a multi-drone collaborative lifting system was introduced;

[0075] After takeoff, the drone uses RTK-GPS and airborne laser radar to build a "global-local" dual coordinate system to obtain the drone's position data. At the same time, the drone calculates the tension data in real time and transmits it to the ground base station. The ground base station uses a sliding mode-adaptive algorithm to dynamically adjust the height of each drone.

[0076] The onboard computer includes an error calculation unit and a UAV control calculation unit;

[0077] The error calculation unit dynamically calculates the displacement adjustment required to adjust the UAV formation posture through the real-time collected tension and spatial position data to achieve load balancing, formation stability and adapt to external disturbances including wind and load changes;

[0078] The specific calculation process of the error calculation unit to calculate the vertical displacement of the drone is as follows:

[0079] Tension data: Each drone collects the tension value F in real time i (i=1,2,3,...,n); i represents the index of the drone, and n represents the total number of drones in the drone module;

[0080] Positioning data: Get its absolute position through RTK / GPS / UWB i (x i ,y i ,z i ), or obtain the relative formation position through binocular camera / lidar, where x and y represent the two horizontal directions of the drone in space, and z represents the vertical direction of the drone in space;

[0081] Data transmission: Aggregate data to the ground base station or the main UAV through the ad hoc network module;

[0082] Global state calculation, calculate the average tension F avg :

[0083]

[0084] Tension balance control, vertical direction adjustment, first calculate the tension error:

[0085] ΔF i = F i - F avg

[0086] Combining the robustness of sliding mode surface control and the dynamic gain adjustment of adaptive control, suppressing chattering and improving the response speed, calculate the sliding mode surface s i :

[0087] s i = Δ F i + λ∫ΔF i dt

[0088] Calculate the adaptive gain based on the sliding mode surface:

[0089]

[0090] Calculate the vertical displacement Δz i :

[0091]

[0092] Among them, s i is the sliding mode surface, used to characterize the cumulative dynamics of the tension error, is the adaptive gain, used to dynamically adjust the control intensity, t represents the real-time running time of the system, ensuring that the adaptive gain changes dynamically with the task progress; λ, γ and β are design parameters, where λ > 0, used to adjust the convergence speed, γ > 0, used to control the gain growth rate, β > 0, used to suppress chattering;

[0093] sat(s i ) is the saturation function, used to smooth the control signal; sign(s i ) is the sign function, used to extract its sign from the numerical value, that is, to judge whether the numerical value is positive, negative or zero, and return 1, -1 or 0 accordingly.

[0094] The specific calculation process of the error calculation unit for calculating the horizontal displacement of the UAV is as follows:

[0095] Position deviation: Calculate the target position w of each UAVdesired,i Deviation Δw from the actual position w i ; i ;

[0096] Δw i = |w desired,i - w i |

[0097] Calculate the feedforward disturbance compensation:

[0098]

[0099] Calculate the position correction amount:

[0100]

[0101] where and are the calculated values of the disturbances in the x / y directions in the feedforward compensation respectively; α represents the feedforward gain coefficient, which is calibrated through experiments, K LQR is the optimal feedback matrix calculated by the LQR algorithm obtained through offline pre - calculation, θ i represents the yaw angle of the i - th UAV;

[0102] Output the control quantity, and send (Δx i , Δy i , Δz i ) to the UAV flight controller to adjust the attitude and power.

[0103] The traditional equal - load - sharing strategy will cause the upwind UAV to be overloaded under the action of cross - wind; this solution makes the load difference among the four UAVs always small through vertical displacement fine - tuning, preventing a single UAV from overheating and shutting down.

[0104] At the same time, the traditional PID control needs to wait for the position deviation to appear before responding, resulting in a large swing of the lifted goods; this solution pre - compensates for the feedforward disturbance, adjusts the formation position in advance when the wind speed suddenly changes, and effectively controls the swing.

[0105] The UAV control calculation unit is used to calculate the displacement adjustment amount according to the error calculation unit, calculate in real - time the lift and attitude adjustment amounts required by the UAV, and transmit them to the UAV through the communication unit to achieve the dynamic adjustment of UAV cooperative lifting. The specific calculation process is as follows:

[0106] Lift adjustment amount, the basic lift P base,i Power required to maintain hovering, load compensation lift:

[0107]

[0108] where is the gain coefficient, and the total lift is:

[0109] P i = P base,i + ΔP i

[0110] Attitude adjustment amount: pitch angle φ i and roll angle ψ i are calculated from the horizontal displacement command (Δx i , Δy i ):

[0111]

[0112] where φ i represents the pitch angle of the i-th UAV, and a positive value indicates that the UAV pitches forward to generate a forward horizontal thrust. ψ i represents the roll angle of the i-th UAV, and a positive value indicates that the UAV rolls to the right to generate a rightward horizontal thrust. Q φ and Q ψ represent the proportional gains of pitch and roll control, which are obtained through experimental calibration;

[0113] and represent the change rate of the horizontal displacement correction amount, that is, the adjustment speed of the UAV in the horizontal direction, which is used to damp the oscillation. g represents the acceleration due to gravity, and h represents the current hovering height of the UAV, which is used to dynamically adjust the damping term; the yaw angle is uniformly adjusted by the formation orientation.

[0114] Through the load balancing algorithm of the error calculation unit of the on-board computer, the vertical / horizontal displacement is calculated in real time, realizing the collaborative control of the distributed loads of multiple UAVs and the collaborative transportation ability of large goods;

[0115] Through the combination of sliding mode control and adaptive gain algorithm with multi-modal sensor fusion positioning, rapid attitude adjustment under unexpected conditions is achieved, stable lifting under complex meteorological conditions is achieved, and the core defect of weak anti-interference ability of the existing system is solved.

[0116] At the same time, the flight control component is also used for collaborative constraint and fault tolerance of the UAVs, specifically as follows;

[0117] Formation height locking, the sum of the height adjustments of each UAV ∑Δz i = 0, maintaining the overall height stability;

[0118] Saturation processing, restricting the power output P i ∈ [P min , P max , preventing motor overload;

[0119] Abnormality detection, if a UAV communication times out, enable the local decision-making mode and give priority to maintaining position stability.

[0120] The power component can adjust the power and attitude of the drone according to the power and attitude adjustment amounts transmitted by the drone control calculation unit.

[0121] Finally, a multi-drone collaborative lifting system further includes a ground base station, which includes a calculation unit and a communication unit. The power and state adjustment amounts between multiple drones can be calculated by the communication unit and sent by the communication unit to the communication units of the drones.

[0122] Traditional centralized systems are completely paralyzed in case of communication interruption; while this solution ensures continuous output of instructions through the "ground base station - drone" dual decision-making nodes, and there is no perceptible fluctuation of the lifted object during the switching process.

[0123] The embodiments of the present invention are given for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A multi-UAV collaborative lifting system, characterized in that, include: UAV module, wherein the UAV module is composed of two or more UAVs, each of which is equipped with an onboard computer, a flight control component, a power component, a spatial positioning sensor, a tension sensor and a communication unit; The spatial positioning sensor includes differential GPS, RTK and UWB positioning sensors, and can also use binocular cameras and laser radars to measure the relative spatial positions between drones or the absolute positions based on the ground coordinate system, so as to obtain the position data of the drones; The tension sensor is used to detect the change in the tension load of each drone during the lifting process, obtain the tension data of the cargo lifted by the drone, and provide data support for balancing the power output of each drone; The onboard computer is connected to the communication unit data, and is used to calculate the real-time lift and attitude adjustment of one of the UAVs in the lifting formation based on the tension data and position data of the spatial positioning sensor and the tension sensor through the self-organizing network technology, and control the flight parameters of the multiple UAVs accordingly; The communication unit is used for data transmission between drones.

2. The multi-UAV collaborative hoisting system according to claim 1, characterized in that: The onboard computer includes an error calculation unit and a drone control calculation unit; The error calculation unit dynamically calculates the displacement adjustment amount required to adjust the UAV formation posture through the real-time collected tension and spatial position data, so as to achieve load balancing, formation stability and adapt to external disturbances including wind and load changes; The UAV control calculation unit is used to calculate the displacement adjustment amount according to the error calculation unit, calculate the lift and attitude adjustment amount required by the UAV in real time, and transmit it to the UAV through the communication unit to realize dynamic adjustment of UAV collaborative lifting.

3. The multi-UAV collaborative lifting system according to claim 2, wherein: The specific calculation process of the error calculation unit for calculating the vertical displacement of the drone is as follows: Tensile force data: The tensile force value F is collected in real time by each drone i (i = 1, 2, 3,..., n); i represents the index of the drone, and n represents the total number of drones in the drone module; Positioning data: Obtain its own absolute position w through RTK / GPS / UWB i (x i , y i , z i ), or obtain the relative formation position through binocular cameras / Lidar, where x and y respectively represent two horizontal directions of the UAV in space, and z represents the vertical direction of the UAV in space; Data transmission: Aggregate data to the ground base station or the main UAV through the self-organizing network module; Global state calculation, calculate the average tensile force F avg : Tension balance control, vertical adjustment, first calculate the tension error: ΔF i = F i - F avg Combining the robustness of sliding mode surface control with the dynamic gain adjustment of adaptive control to suppress chattering and improve the response speed, calculate the sliding mode surface s i : s i = ΔF i + λ∫ΔF i dt Calculate the adaptive gain based on the sliding surface: Calculate the vertical displacement Δz i : where s i is the sliding surface, which is used to characterize the cumulative dynamics of the tension error, is the adaptive gain, which is used to dynamically adjust the control strength, t represents the real-time running time of the system, ensuring that the adaptive gain varies dynamically with the task process; λ, γ, and β are design parameters, where λ > 0 is used to adjust the convergence speed, γ > 0 is used to control the gain growth rate, and β > 0 is used to suppress chattering; sat(s i ) is a saturation function used to smooth the control signal; sign(s i ) is a sign function used to extract the sign from a numerical value, that is, to determine whether the numerical value is positive, negative, or zero, and return 1, -1, or 0 accordingly.

4. The multi-UAV collaborative lifting system according to claim 3, wherein: The specific calculation process of the error calculation unit for calculating the horizontal displacement of the drone is as follows: Position deviation: Calculate the target position w of each UAV desired,i and the actual position w i for the deviation Δw i ; Δw i = |w desired,i - w i | Calculate the feedforward disturbance compensation: Calculate the position correction: Among them, and are the calculated disturbance values in the x / y directions in feedforward compensation respectively; α represents the feedforward gain coefficient, which is calibrated through experiments, and K LQR is the optimal feedback matrix calculated by the LQR algorithm obtained through offline pre-calculation, and θ i represents the yaw angle of the i-th UAV. The output control quantity, and send (Δx i , Δy i , Δz i ) to the UAV flight control to adjust the attitude and power.

5. The multi-UAV collaborative hoisting system according to claim 4, characterized in that: The UAV control calculation unit calculates the lift and attitude adjustment amount according to the adjustment amount calculated by the error calculation unit: Lift adjustment amount, basic lift P base,i Power required to maintain hover, load compensation lift: wherein, is the gain coefficient, and the total lift is: P i = P base,i + ΔP i Attitude adjustment amount: pitch angle φ i and roll angle ψ i Calculated from the horizontal displacement command (Δx i , Δy i ): Among them, φ i represents the pitch angle of the i-th drone. A positive value indicates that the drone pitches forward to generate a forward horizontal thrust. ψ i represents the roll angle of the i-th drone. A positive value indicates that the drone rolls to the right to generate a rightward horizontal thrust. Q φ and Q ψ represent the proportional gains of pitch and roll control, which are obtained through experimental calibration; and represents the change rate of the horizontal displacement correction amount, that is, the adjustment speed of the drone in the horizontal direction, which is used for damping oscillation. g represents the acceleration due to gravity, and h represents the current hovering height of the drone, which is used to dynamically adjust the damping term; the yaw angle is uniformly adjusted by the formation orientation.

6. The multi-UAV collaborative lifting system according to claim 1, wherein: The flight control component is also used to perform collaborative constraints and fault tolerance on the UAV, as follows; Formation altitude lock, sum of altitude adjustments of each UAV ∑Δz i = 0, maintaining overall altitude stability; Saturation processing, limiting the power output P i ∈ [P min , P max , to prevent the motor from overloading; Abnormal detection: If a drone’s communication times out, the local decision-making mode is enabled to prioritize maintaining position stability.

7. The multi-UAV collaborative hoisting system according to claim 2, wherein: The power component can adjust the power and attitude of the drone according to the power and attitude adjustment amount transmitted by the drone control calculation unit.

8. The multi-UAV collaborative lifting system according to claim 1, characterized in that: The system also includes a ground base station, which includes a computing unit and a communication unit. The power and state adjustment amounts between multiple UAVs can be calculated by the communication unit and sent by the communication unit to the communication units of the UAVs.

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