Multi-unmanned aerial vehicle cooperative lifting method, equipment, platform and system

Abstract

The invention discloses a multi-unmanned aerial vehicle cooperative lifting method, device, platform and system. The method comprises the steps that a flight control computer obtains platform state information of a whole subsystem at the current moment and unmanned aerial vehicle state information of all unmanned aerial vehicles at the current moment; the flight control computer calculates the total control input of the overall subsystem at the current moment; the flight control computer calculates the total control input based on a set multi-unmanned aerial vehicle cooperative dynamics model and the state information of the unmanned aerial vehicle at the current moment to obtain sub-control input corresponding to each unmanned aerial vehicle at the current moment; and the flight control computer generates a control instruction based on the sub-control input and sends the control instruction to each unmanned aerial vehicle, so that the unmanned aerial vehicle responds to the control instruction and executes flight operation according to the sub-control input. According to the multi-unmanned-aerial-vehicle cooperative hoisting system, efficient coordination of the multi-unmanned-aerial-vehicle cooperative hoisting system on operation tasks of the multiple unmanned aerial vehicles is achieved, the limitation that the carrying capacity of a single unmanned aerial vehicle is insufficient is overcome, and the carrying capacity requirement of large-mass transportation is met.

Description

Technical Field

[0001] The present application relates to the field of drone transportation technology, and in particular to a multi-drone collaborative lifting method, equipment, platform and system. Background Art

[0002] With the rapid development of drone technology, drones, with their excellent maneuverability and flexibility, can perform efficient material transportation and delivery operations in areas that are difficult to reach with traditional transportation tools. Drones can not only break through the limitations of the geographical environment, but are also not subject to land traffic control. They are simple to operate and easy to use.

[0003] At present, when using drones for material transportation and delivery operations, a single drone is usually used.

[0004] However, the carrying capacity of a single drone is limited, especially when transporting large objects, the carrying capacity of a single drone is difficult to meet actual needs. Summary of the invention

[0005] The purpose of this application is to provide a multi-UAV collaborative lifting method, system and equipment to achieve collaborative transportation and delivery operations of multiple UAVs.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a multi-UAV collaborative lifting method, the method comprising:

[0008] The flight control computer obtains the platform status information of the overall subsystem at the current moment and the drone status information of each drone at the current moment, wherein the overall subsystem includes a control platform and hoisted cargo, and a plurality of the drones are detachably connected to the control platform through a connecting component, and the flight control computer and the navigation system are installed in the control platform;

[0009] The flight control computer calculates the total control input of the overall subsystem at the current moment based on the set flight trajectory and the platform status information at the current moment, wherein the total control input represents the total control force required for the overall subsystem to move according to the flight trajectory and reach the target position at the next moment;

[0010] The flight control computer solves the total control input based on the set multi-UAV cooperative dynamics model and the current state information of the UAVs to obtain the sub-control input corresponding to each UAV at the current moment, wherein the sub-control input represents the sub-control force required for each UAV when the overall subsystem moves according to the flight trajectory under the force of the multiple UAVs and reaches the target position at the next moment;

[0011] Based on the sub-control input, the flight control computer generates control instructions and sends them to each UAV, so that the UAV responds to the control instructions and performs flight operations according to the sub-control input.

[0012] Optionally, for the multi-UAV cooperative hoisting method provided by the present application, the flight control computer calculates the total control input of the overall subsystem at the current moment based on the set flight trajectory and the platform state information at the current moment, including:

[0013] The target state vector of the overall subsystem determined based on the state vector and flight trajectory at the current moment;

[0014] Determine the signal error between the state vector of the overall subsystem at the current moment and the expected target state vector; and the error between the derivative of the state vector at the current moment and the virtual signal quantity;

[0015] Determine the total control input at the current moment based on the determined error.

[0016] Optionally, for the multi-UAV cooperative hoisting method provided by the present application, the calculation model is expressed as:

[0017]

[0018] Wherein, u is the total control input of the overall subsystem at the current moment, f2, g1, and g2 are calculation model coefficients, e1 is the signal error between the state vector of the overall subsystem at the current moment and the expected state vector, k2 is the gain coefficient, is the approximation value of the disturbance term in the calculation model; e2 is the error between the derivative of the state vector of the overall subsystem at the current moment and the virtual signal quantity, is the virtual signal quantity of the derivative;

[0019]

[0020] Wherein, x1 is the state vector of the overall subsystem at the current moment, is the target state vector of the overall subsystem, x2 is the derivative of the state vector of the overall subsystem at the current moment, is the virtual signal quantity of the overall subsystem at the next moment, is the virtual signal quantity of the derivative.

[0021] Optionally, for the multi-UAV cooperative lifting method provided by this application, the UAV state information includes the pose information, speed information of the UAV, and the angle information of the connection component. Based on the set multi-UAV cooperative dynamics model and the state information of the UAV at the current moment, the flight control computer resolves the total control input to obtain the sub-control input of each UAV at the current moment, including:

[0022] Calculate the corresponding sub-control force of each UAV at the current moment based on the multi-UAV cooperative dynamics model, the total control input, and the pose information, speed information, and connection component angle information of each UAV;

[0023] Determine the sub-control input of each UAV at the current moment based on the sub-control force of each UAV and the current state information of each UAV. The sub-control input includes the sum of the control component and the vertical component along the rope direction.

[0024] Optionally, for the multi-UAV cooperative lifting method provided by this application, the multi-UAV cooperative dynamics distribution model is expressed as follows:

[0025]

[0026] where P is the relationship auxiliary matrix between the overall subsystem and the connection component, and R0 represents the attitude rotation matrix of the overall subsystem in the body coordinate system. is the expected tensile force applied by the i-th connecting piece to the overall subsystem. is the total control force corresponding to the total control input. is the total control torque corresponding to the total control input.

[0027] Optionally, for the multi-UAV cooperative lifting method provided by this application, the vertical component of the sub-control input is expressed as:

[0028]

[0029] The control component of the sub-control input is expressed as:

[0030]

[0031]

[0032] where e qi is the difference between the target angle of the i-th connecting piece at the next moment and the angle at the current moment. is the difference between the target angular velocity of the i-th connecting piece at the next moment and the angular velocity at the current moment, expressed as:

[0033]

[0034]

[0035] v i For The projection along the direction from the centroid of the $i$-th unmanned aerial vehicle (UAV) to the connection point of the $i$-th connecting member on the control platform,

[0036] m i Is the weight of the $i$-th UAV, and $l$ i Is the length of the $i$-th connecting member between the $i$-th UAV and the control platform, and $q$ i Is the unit vector from the centroid of the $i$-th UAV to the connection point of the $i$-th connecting member on the control platform, and $k$ q And $q$ i Are positive gain coefficients, Is the angular acceleration of the $i$-th connecting member, Is the expected angular velocity of the $i$-th connecting member, Is the angular acceleration of the overall subsystem at the current moment, and $a$ i Is an auxiliary variable.

[0037] Optionally, for the multi-UAV cooperative lifting method provided by the present application, the flight control computer generates control instructions based on the sub-control inputs, including:

[0038] The flight control computer generates control instructions according to the sub-control force, the sub-control torque, and the set cargo inclination threshold.

[0039] In a second aspect, the present application provides a flight control computer device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the multi-UAV cooperative lifting method as described in the first aspect.

[0040] In a third aspect, the present application provides a control platform, which integrates a navigation system and the flight control computer as described in the second aspect. The control platform is detachably connected to the lifted cargo, and the navigation system is used to collect the state information of the control platform.

[0041] In a fourth aspect, the present application provides a multi-UAV cooperative lifting system, which includes multiple UAVs and the control platform as described in the third aspect;

[0042] The multiple UAVs are detachably connected to the control platform through a connection component. The control platform is detachably connected to the lifted cargo, and the multiple UAVs are used to provide pulling force to the control platform.

[0043] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0044] The multi-UAV collaborative lifting method, device, platform and system provided by the embodiments of the present application, through the established multi-UAV collaborative lifting system and the algorithm model configured in the system platform, enable the flight control computer in the system to calculate the total control input required for the overall subsystem (control platform and goods) to reach the target position at the next moment based on the current state information of the overall subsystem (control platform and goods) collected by the navigation system and the pre-configured flight trajectory during actual goods lifting. Furthermore, based on the set multi-UAV collaborative dynamics model, according to the current state information of each UAV and the state of the connection components between the UAVs and the control platform, the total control input is resolved to obtain the sub-control inputs corresponding to each UAV. Finally, based on the obtained sub-control inputs, control instructions corresponding to each UAV are generated and sent to each UAV. After each UAV receives the control instructions, it can respond to the control instructions and perform corresponding flight operations to ensure that the lifted goods reach the target position on the flight trajectory at the next moment, realizing the efficient coordination of the multi-UAV collaborative lifting system for multiple UAV operation tasks, overcoming the limitation of the insufficient carrying capacity of a single UAV, meeting the carrying capacity requirements for large-mass transportation, and providing precise navigation and control support, significantly improving the stability and robustness of the system.

[0045] Compared with the prior art, the present application has significant advantages in the highly integrated design of the multi-UAV system lifting platform, the optimization of the connection method, and the improvement of the information interaction and control process, and can achieve more precise control and scheduling in complex environments, expanding the application scope of the multi-UAV collaborative lifting system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following described drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 It is a schematic structural diagram of a multi-UAV collaborative lifting system according to some embodiments of the present application;

[0048] Figure 2 It is a schematic flow diagram of a multi-UAV collaborative lifting method according to some embodiments of the present application;

[0049] Figure 3 It is a schematic structural diagram of a multi-UAV collaborative lifting device according to some embodiments of the present application;

[0050] Figure 4 It is a schematic structural diagram of a computer device provided by some embodiments of the present application. Detailed implementation manners

[0051] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0052] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0053] It can be understood that in the UAV transportation scenario, for the limitations existing in the transportation operation of a single UAV, the present application realizes the collaborative operation of multiple UAVs by building a multi-UAV collaborative hoisting system, so as to improve the carrying capacity of the system and enable it to adapt to more complex and diverse application scenarios.

[0054] When designing a multi-UAV collaborative hoisting system, the design and implementation of the control platform are crucial. The control platform is not only the hub for coordinating multiple UAVs, but also carries key devices and systems, such as a combined navigation system and a flight control computer, and provides precise positioning, navigation, and data processing support. The integration and collaborative effects of these systems are crucial for ensuring the efficiency and stability of the collaborative operation of UAVs.

[0055] Figure 1 The figure shows a schematic structural diagram of the built multi-UAV collaborative hoisting system.

[0056] As Figure 1 shown, the system can be used to hoist cargo 1, and the system can include:

[0057] A control platform 3 and multiple UAVs 8. The control platform 3 includes a frame and a combined navigation system 5, a flight computer 6, and a communication module installed in the frame.

[0058] Among them, the control platform 3 and the hoisted cargo 1 to be transported can form a subsystem, that is, the overall subsystem 11.

[0059] Among them, the frame can be made of an alloy material with high strength and light weight characteristics. The selection of this material is aimed at ensuring that the platform has sufficient strength to carry goods and equipment, while minimizing the overall weight as much as possible to improve the operating efficiency of the system. Additionally, according to the design requirements, precise machining is carried out on the central fixed point at the bottom of the control platform and multiple hanging points at the edge, strictly ensuring the position accuracy of each point, so as to achieve a stable connection between the lifted goods and the ropes. And at the top or side of the platform, multiple hanging points are installed according to the principle of uniform distribution. These hanging points have high strength and good wear resistance to ensure that they can withstand the corresponding tensile force during the lifting process by the unmanned aerial vehicle.

[0060] The navigation system 5 can be composed of an IMU and GNSS to form an integrated navigation system. It can be firmly fixed on the preset installation position on the platform with bolts by means of a supporting mounting bracket. The selection of the installation position needs to be away from strong electromagnetic interference sources to ensure that the integrated navigation system can accurately measure the overall state information of the control platform and the lifted goods. Additionally, the data cable of the integrated navigation system can be connected to the data interface of the flight control computer using an Ethernet cable to achieve high-speed and stable data transmission, ensuring that the state information is transmitted to the flight control computer 6 in a timely and accurate manner.

[0061] The flight control computer 6 can be installed in a shockproof and dustproof chassis designed on the control platform 3 and fixed with shock pads and screws to reduce the impact of vibrations during the lifting process. The flight control computer 6 is equipped with a high-performance processor and a large-capacity memory to meet the requirements of complex operations.

[0062] Additionally, a wireless communication module is installed in the control platform 3, and the position of its antenna is adjusted to ensure good signal reception and transmission. The wireless communication module is connected to the flight control computer to achieve data interaction.

[0063] As Figure 1 shown, the control platform 3 is connected to each unmanned aerial vehicle through ropes 10 and to the lifted goods through ropes 2. That is, ropes of appropriate specifications can be selected according to the shape, size, and weight of the lifted goods. The lifted goods are placed at the central position at the bottom of the platform, and the lifted goods are firmly tied to the central fixed point at the bottom of the platform with ropes. At the same time, ropes are led out from each hanging point at the bottom edge of the control platform 3, evenly distributed around the goods, and reliably connected to the goods to ensure that the lifted goods remain stable during the lifting process and form a rigid body structure approximately with the control platform.

[0064] It can be understood that in the system built as Figure 1 shown, the flight control computer 6 is configured with the flight trajectory of the lifted goods and the parameters of the lifted goods, such as the inclination threshold during the transportation of the lifted goods.

[0065] In addition, a control program runs in the flight control computer, that is, the flight control computer 6 is configured with a calculation model of the total control input of the overall subsystem and a multi-UAV collaborative dynamics allocation model for solving the total control input, etc., which can be specifically written in a high-level programming language.

[0066] The control program mainly includes the following functional modules:

[0067] Position information processing module: used to receive and process the overall position information of the platform 3 and the cargo 1 transmitted by the integrated navigation system 6.

[0068] Data transmission module: responsible for data exchange with the rope subsystem, drone subsystem and wireless communication module. In this process, the flight control computer 6 acts as an information hub to coordinate the data flow of each link to ensure that the information can be transmitted and executed in real time and accurately. The flight control computer 6 and the flight control system of the drone exchange data in real time through a wireless communication module or a wired communication line (such as a dedicated communication cable).

[0069] Communication parameter configuration: configure the parameters of the wireless communication module (such as 2.4G or 5G frequency band wireless communication equipment) between the flight control computer 6 and the flight control system of the drone 8, including communication frequency, transmission rate, encryption method, etc. Ensure that the communication link is stable and reliable, and can achieve real-time and accurate data interaction. At the same time, configure the parameters of the wired communication line (such as a dedicated communication cable) so that it can be used as a backup communication method when the wireless communication fails.

[0070] It can be understood that in the actual execution process of the multi-UAV cooperative lifting system provided by the present application, the flight control computer 6, as the central processor, collects the real-time status information of the overall subsystem and each UAV and the relative position relationship between each UAV 8 in real time, and then determines the total control input required by the system at the next moment according to the pre-set flight trajectory, and then through the constructed UAV cooperative dynamics allocation model, the determined total control input is intelligently split and reasonably allocated on demand to obtain the corresponding sub-control input. Finally, the control instructions of each UAV are generated according to the sub-control input, and sent to each UAV 8 through the wireless communication module to ensure that the UAV 8 performs the task according to the predetermined flight trajectory, and finally realizes the multi-UAV cooperative transportation of lifting goods.

[0071] In order to better understand the multi-UAV collaborative lifting method provided by this application, it is explained in detail through a flowchart below.

[0072] Figure 2 This is a flow chart of a multi-UAV collaborative lifting method provided in some embodiments of the present application. Figure 2 As shown, this method can be implemented as Figure 1It is executed by the flight control computer 6 shown, and the method specifically includes the following steps:

[0073] S110, the flight control computer obtains the platform state information of the overall subsystem at the current moment and the UAV state information of each UAV at the current moment. The overall subsystem includes a control platform and a hoisted cargo. Multiple UAVs are detachably connected to the control platform through a connection component, and the flight control computer and the navigation system are installed in the control platform.

[0074] S120, based on the set flight trajectory and the platform state information at the current moment, the flight control computer calculates the total control input of the overall subsystem at the current moment. The total control input represents the total control force required for the overall subsystem to move along the flight trajectory and reach the target position at the next moment.

[0075] S130, based on the set multi-UAV cooperative dynamics model and the state information of the UAV at the current moment, the flight control computer resolves the total control input to obtain the sub-control input of each UAV at the current moment. The sub-control input represents the sub-control force required for each UAV when the overall subsystem moves along the flight trajectory under the action of multiple UAVs and reaches the target position at the next moment.

[0076] S140, based on the sub-control input, the flight control computer generates a control command and sends it to each UAV, so that the UAV responds to the control command and performs a flight operation according to the sub-control input.

[0077] Specifically, in the embodiment of the present application, in the built multi-UAV cooperative hoisting system, when transporting hoisted cargo, the function modules in the flight control computer 6, the integrated navigation system 5 in the control platform 3, and the UAV 8 are started and run.

[0078] That is, when the flight control computer 6, as the central processing unit, controls the flight state of the UAV 8 to fly along the set flight trajectory, it can first obtain the platform state information of the overall subsystem 11 collected by the integrated navigation system 5 and the UAV state information of the UAV at the current moment collected by the UAV processor.

[0079] The platform state information may specifically include the pose information and speed information of the overall subsystem at the current moment. The pose information may include the current position and attitude (i.e., angle information) of the overall subsystem 11, and may specifically be represented as the state vector of the overall subsystem. The speed information may include the current linear velocity, angular velocity, and corresponding acceleration information of the overall subsystem 11.

[0080] Similarly, the UAV status information may include the current pose information and speed information of the UAV 8. The pose information may include the current position and attitude of the UAV 3. Moreover, the UAV status information may further include the status information of the connection components between each UAV 8 and the control platform 3.

[0081] The status information of the connection components may include the angular information of each connection component to represent the positional relationship between the UAVs.

[0082] Among them, as Figure 1 shown, the connection component may specifically be a rope structure, and the UAV 8 may be detachably connected to the control platform 3 through the rope 10. Correspondingly, the goods being lifted may be detachably connected to the control platform 3 through the rope 2.

[0083] Furthermore, after the flight control computer 6 obtains the platform status information and UAV status information at the current moment, it may set the flight trajectory and the platform status information at the current moment, and calculate the total control input required for the overall subsystem 11 to move from the current position to the target position corresponding to the next moment.

[0084] The total control input represents the total control force required for the overall subsystem to move to the target position at the next moment according to the flight trajectory.

[0085] That is, in S120, the flight control computer 6 may, based on the configured algorithm, determine the control input required for the overall subsystem at the current moment according to the platform status information at the current moment and the target position at the next moment.

[0086] Furthermore, after obtaining the total control input of the overall subsystem, based on the configured multi-UAV cooperative dynamics model, the total control input may be resolved according to the current UAV status information of each UAV, and the total control input may be allocated to each UAV, and finally the sub-control input required for each UAV at the current moment may be determined. The sub-control input represents the sub-control force required for each UAV when the overall subsystem moves to the target position at the next moment under the action of multiple UAVs according to the flight trajectory.

[0087] Finally, the flight control computer 6 determines each sub-control input, generates control commands for each UAV, and sends them to the processors of each UAV, so that each UAV responds to the received control commands and performs flight operations according to the sub-control input, enabling the overall subsystem to reach the target position at the next moment when multiple UAVs perform flight operations.

[0088] It can be understood that for the multi - UAV collaborative lifting method provided by the embodiments of the present application, through the established multi - UAV collaborative lifting system and the configured algorithm model, when actually collaborating to lift goods, the flight control computer in the system can calculate the total control input required for the overall subsystem (control platform and goods) at the next moment based on the current state information collected by the navigation system and the pre - configured flight trajectory. Then, based on the set distribution model, for the current flight states of each UAV, the total control input is resolved to obtain the sub - control inputs corresponding to each UAV. Finally, control instructions corresponding to each UAV are generated based on the obtained sub - control inputs and sent to each UAV. After each UAV receives the control instructions, it can respond to the control instructions and perform corresponding flight operations to reach the target position on the flight trajectory at the next moment, realizing the efficient coordination of the multi - UAV collaborative lifting system for multiple UAV operation tasks, providing precise navigation and control support, and significantly improving the stability and robustness of the system.

[0089] Compared with the prior art, the present application has significant advantages in the highly integrated design of the platform, the optimization of the connection method, and the improvement of the information interaction and control process, and can achieve more precise control and scheduling in complex environments, expanding the application scope of the multi - UAV collaborative lifting system.

[0090] It can be understood that for the system established in the embodiments of the present application, in order to calculate the total control input, a calculation model for the total control input of the overall subsystem is also configured in the flight control computer, enabling the flight control computer 6 to calculate the total control input required at the next moment based on the real - time collected data and the flight trajectory; and the configured multi - UAV collaborative dynamics distribution model is used to resolve the calculated total control input.

[0091] Then, in order to understand the calculation and resolution of the total control input in the embodiments of the present application, the following will be elaborated in detail through specific model representations.

[0092] Then corresponding to Figure 1 the system multi - UAV collaborative lifting system as shown, for the overall subsystem 11 therein, its inertial coordinate system I and n + 1 body coordinate systems B can be defined. i .

[0093]

[0094]

[0095] Among them, the 0th body coordinate system is established at the centroid of the overall subsystem 11, and the ith body coordinate system is established at the centroid of the UAV 8.

[0096] Then the kinematic equations of the overall subsystem 11, the UAV 8, and the rope 10 are expressed as:

[0097]

[0098]

[0099]

[0100] where \(q\) i is the unit vector from the centroid of the \(i\)-th UAV 8 to the connection point of the \(i\)-th cable 10 on the control platform 3 in the direction, and \(\omega\) i is the angular velocity of the \(i\)-th cable 10, satisfying \(q\) i ·\(\omega\) i = 0, \(R_0\) and \(R\) i represent the attitude rotation matrices of the overall subsystem 11 and the \(i\)-th UAV 8 in the body coordinate system respectively, and \(\Omega_0\) and \(\Omega\) i are the angular velocities of the overall subsystem 11 and the \(i\)-th UAV 8 in the body coordinate system respectively, represents the mapping of the skew-symmetric matrix.

[0101] Assume that the attitude change is very small during the collaborative operation of the UAVs 8, then the relationship between the overall subsystem 11 and the UAV 8 can be expressed as:

[0102] \(p\) i = \(p_0 + R_0\rho\) i - \(l\) i \(q\) i (4)

[0103] where \(p\) i is the position of the centroid of the \(i\)-th UAV 8 in the inertial coordinate system, \(p_0\) is the centroid position of the overall subsystem, and \(\rho\) i is the vector from the centroid of the overall subsystem 11 to the connection point of the \(i\)-th cable 10 on the control platform, and \(l\) i is the length of the \(i\)-th cable 10.

[0104] Then the velocity of the \(i\)-th UAV 8 can be expressed as:

[0105]

[0106] Then, according to the above velocity expression, the dynamic equation of the overall subsystem 11 is defined as:

[0107]

[0108]

[0109]

[0110]

[0111] where m0 = m l + m p is the total mass of the overall subsystem composed of the control platform 3 and the cargo 1, g is the local acceleration due to gravity, e3 represents the unit vector along the z-axis in three-dimensional space, J0 is the inertia matrix of the overall subsystem, M i is the control torque of the i-th drone.

[0112] u F represents the control forces of all drones, and u M represents the control torques of all drones, specifically expressed as:

[0113]

[0114] where v i is the virtual control quantity and can be expressed as:

[0115]

[0116]

[0117] where and are respectively the control component and the vertical component of the i-th drone 8's control input u i along the rope direction, and a i is an auxiliary variable.

[0118] It can be understood that due to the existence of external disturbances (such as air resistance and rope swing) and modeling errors, a neural network (such as an RBF neural network) can be used to simulate and approximate the uncertain terms and in the multi-drone hoisting system to compensate for the modeling errors.

[0119]

[0120] where H = [h1,…,h m is the radial basis vector of the neural network, which is a Gaussian function:

[0121]

[0122] where the center vector of the j-th node of the network is, is the basis width parameter of node j, and is a number greater than zero. W is the connection weight matrix between the hidden layer and the output layer of the neural network, and are the approximation values of the uncertain terms, and the whole can be expressed as

[0123] In practice, the flight control computer 6 can calculate the control input required for the overall subsystem 11 to move to the target position corresponding to the next moment according to the set neural network algorithm (such as the RBF-Backstepping control algorithm) in combination with the actual hoisting requirements, and based on the real-time platform state information such as the current position, attitude, speed, and acceleration of the overall subsystem 11 measured by the integrated navigation system 5 in real time.

[0124] It can be understood that in practice, for the convenience of calculation, that is, for the convenience of using the set control algorithm to deduce the virtual controller of the overall subsystem 11, the above dynamic equations (6) and (7) can be written in the form of a continuous-level system:

[0125]

[0126]

[0127] Among them, and express the system state of the overall subsystem, that is, x1 is the state vector of the overall subsystem at the current moment, which represents the position of the overall subsystem at the current moment, x2 is the derivative of the state vector of the overall subsystem at the current moment, f1, f2, g1, and g2 are collectively referred to as the influence coefficients of the inherent dynamics of the system and the control input on the system state, u represents the control input of the overall subsystem at the next moment, and Δ is the corresponding uncertain term in the neural network model.

[0128] It can be understood that through the above calculation model built based on the neural network model, in S120, specifically, the total control input u in the above formula can be calculated by collecting the platform state information at the current moment and the target position at the next moment in the flight trajectory, that is, it represents the total control force required for the overall subsystem to reach the target position at the next moment according to the set flight trajectory.

[0129] That is, in S120, it specifically includes the following steps:

[0130] S121, determining the target state vector of the overall subsystem based on the state vector at the current moment and the flight trajectory.

[0131] S122, determining the signal error between the state vector of the overall subsystem at the current moment and the expected target state vector; and the error between the derivative of the state vector at the current moment and the virtual signal quantity.

[0132] S123, determining the total control input at the current moment based on the determined error.

[0133] Specifically, the flight control computer can first determine the target state vector of the overall subsystem according to the state information of the overall subsystem at the current moment obtained and the pre-configured flight trajectory.

[0134] For example, based on the state vector representing the current position of the overall subsystem at the current moment and the set distance segment, the target state vector of the overall subsystem can be determined according to the flight trajectory.

[0135] Furthermore, based on the state vector at the current moment and the target state vector, determine the signal error between the current moment state vector of the overall subsystem and the expected target state vector; and the error between the derivative of the current moment state vector and the virtual signal quantity. Finally, based on the determined signal error and error, determine the total control input of the overall subsystem at the current moment.

[0136] For example, the signal error e1 between the current state vector of the overall subsystem and the expected state can be introduced, that is, the error between the feedback state signal (i.e., the currently collected position) and the control command signal (i.e., the target position at the next moment):

[0137]

[0138] Among them, x1 represents the state vector of the overall subsystem collected by the integrated navigation system at the current moment, which can be understood as the position at the current moment. is the target state vector that needs to be reached determined according to the flight trajectory, then the error vector e1 can represent the signal error between the state vector at the current moment and the expected state.

[0139] Then, taking the derivative of e1 gives:

[0140]

[0141] Among them, can represent the speed from the position at the current moment to the target position at the next moment.

[0142] Design a virtual signal quantity :

[0143]

[0144] Among them, k1 is a gain parameter.

[0145] Then introduce the error vector e2, representing the error between the state variable and the virtual signal quantity:

[0146]

[0147] Among them, x2 is the derivative of the state vector of the overall subsystem at the current moment. Furthermore, simplifying e1 gives:

[0148]

[0149] Then construct the Lyapunov function And its derivative is:

[0150]

[0151] When e2→0, Therefore, the state equation of the overall subsystem is positive definite after substitution.

[0152] Furthermore, construct the outer-loop equation. Taking the derivative of e2 gives:

[0153]

[0154] In summary, the virtual controller u can be calculated by the above formula as:

[0155]

[0156] where k2 is the gain parameter, representing the predicted value of the RBF. This virtual control u is the total control input required for the overall subsystem to reach the target position at the next moment.

[0157] It can be understood that the above error vectors e1 and e2 are used to represent the position signal error and the angle error of the overall subsystem.

[0158] It can be understood that this total control input can specifically include the total control force and the total control torque

[0159] Substitute u into Let Then It can be expressed as:

[0160]

[0161] where W is the connection weight matrix between the hidden layer and the output layer, and W * is the optimal value of the connection weight matrix between the hidden layer and the output layer, is the difference between the connection weight matrix and the optimal value.

[0162] Then the weight update probability is:

[0163]

[0164] where γ is the step size, H is the output value of the neural network hidden layer, n is the inertia factor, K is the outer-loop error vector, and K = [e1, e2], ||*|| represents taking the modulus. The updated inertia term W i+1 at the next moment is:

[0165]

[0166] where, Δt represents the time differential.

[0167] Construct the Lyapunov function Taking the derivative of it, we can get At this time, the outer loop of the system state equation is positive definite.

[0168] It can be understood that the model deduced through the above process, that is, the mathematical expression of the above control input, can take the pose information of the current moment actually collected as the input, and combine with the set flight trajectory, that is, the state expected to reach at the next moment, to calculate the total control input required by the overall subsystem at the current moment.

[0169] Furthermore, after obtaining the total control input of the overall subsystem, since the overall subsystem (control platform and goods) in the multi-UAV cooperative hoisting system is connected to each UAV through ropes, and during the goods hoisting process, each UAV needs to cooperate through the ropes to work, that is, the control input required by the overall subsystem is provided by multiple UAVs.

[0170] Then, after determining the total control input required at the current moment, the total control input can be solved through the configured multi-UAV cooperative dynamics distribution model to obtain the sub-control input that each UAV needs to bear.

[0171] Correspondingly, the expected control force (i.e., the total control force) of the overall subsystem calculated above and the expected torque (i.e., the total control torque) can be expressed as:

[0172]

[0173]

[0174] where, can be expressed as the sub-control force assigned to the i-th UAV, that is, the tension of the i-th rope on the overall subsystem.

[0175] That is, in some embodiments of the present application, in S130, that is, the sub-control input is determined through the multi-UAV cooperative dynamics distribution model.

[0176] That is, S130 specifically includes the following steps:

[0177] S131, based on the multi-UAV cooperative dynamics model, the total control input, and the pose information, speed information of each UAV, and the angle information of the connection components, calculate the corresponding sub-control forces of each UAV at the current moment.

[0178] S132. Based on the partial control forces of each UAV and the current state information of each UAV, determine the partial control input of each UAV at the current moment. The partial control input includes the sum of the control component along the rope direction and the vertical component.

[0179] Specifically, the flight control computer 6 can, according to the expected control force and expected control torque of the overall subsystem 11, and in accordance with information such as the real-time position coordinates, flight speed, acceleration, body attitude angle of each UAV at present, and the relative position relationship between each UAV 8, through the constructed multi-UAV cooperative dynamics distribution model, perform intelligent, on-demand splitting and reasonable distribution on the above-mentioned total control force and total control torque, and obtain the corresponding partial control forces of each UAV.

[0180] Finally, determine the current partial control input of the UAV (that is, the partial control input required for each UAV to fly from the current position to the target position at the next moment) according to the partial control force and the partial control torque corresponding to the partial control force, and generate a control instruction according to the partial control input, and send it to each UAV 8 through the wireless communication module, so that when the UAV 8 receives the control instruction, it responds to the control instruction and executes the flight operation, and finally ensures that the UAV 8 executes the task according to the predetermined trajectory.

[0181] Then, in some embodiments, the solution of the total control input by the multi-UAV cooperative dynamics distribution model is specifically analyzed as follows:

[0182] First, an auxiliary matrix can be defined as:

[0183]

[0184] That is, according to the total control force and the total control torque of the overall subsystem, the relationship auxiliary matrix between the overall subsystem 11 and the rope 10 can be determined. and can be converted to:

[0185]

[0186] Furthermore, by solving and the expected value of the virtual control quantity can be obtained as:

[0187]

[0188] wherein, R0 represents the attitude rotation matrix of the overall subsystem in the body coordinate system, is the expected tension applied by the i-th connecting member to the overall subsystem, The total control force corresponding to the total control input The total control torque corresponding to the total control input

[0189] Then the virtual control quantity v i Is the expected value of the virtual control quantity The projection along q i Direction:

[0190]

[0191] Furthermore, according to the above formula (10), the expression can be obtained as:

[0192]

[0193] If the expected direction of the i-th rope 10 is set As:

[0194]

[0195] Then by designing the normal vector So that within a finite time, make Where Is expressed as:

[0196]

[0197] That is, it represents the angular acceleration of the overall subsystem at the current moment

[0198] Then, given the expected direction of the rope 10, its corresponding expected angular velocity can be expressed as:

[0199]

[0200] Furthermore, assume that the direction error of the i-th rope 10 is defined And the angular velocity error of the rope 10 As:

[0201]

[0202]

[0203] Design the control vector of the rope 10 And As follows:

[0204]

[0205]

[0206] Where, k q And k ω Are positive gain coefficients. mi is the weight of the i-th drone, l i is the length of the i-th connecting member between the i-th drone and the control platform, q i is the unit vector from the centroid of the i-th drone to the connection point of the i-th connecting member on the control platform, is the angular acceleration of the i-th connecting member, is the expected angular velocity of the i-th connecting member.

[0207] It can be understood that through the configured multi-drone collaborative dynamics allocation model, the total control input calculated above can be resolved to obtain sub-control inputs, specifically as shown in the above formulas (30) and (35).

[0208] Furthermore, after obtaining the sub-control inputs, the sub-control forces and sub-control torques of each drone can be determined.

[0209] Specifically, according to the calculated control vector and of the i-th drone, determine the sub-control input u i as:

[0210]

[0211] For the expected direction of the third axis of the rigid body fixed coordinate system of the i-th drone, denoted as is defined as:

[0212]

[0213] Introduce the expected direction of the first axis and define the expected attitude of the i-th drone as:

[0214]

[0215] The required attitude angles can be obtained from the attitude equation, i.e.:

[0216]

[0217] Define the attitude tracking error and the angular velocity tracking error of the i-th drone as:

[0218]

[0219] Design the thrust of the drone controller, i.e., the sub-control force u i and the sub-control torque M i with the expressions as:

[0220] fi =-u i ·R i e3

[0221]

[0222] Among them, K R and K Ω It is a normal number. At the same time, based on the real-time posture data and mission requirements, the upper limit threshold of the platform and cargo inclination angle is set to ensure that it operates within a small angle range to avoid flight instability and cargo sway caused by large inclination angles.

[0223] When generating the pulling force and torque instructions for each UAV 8, the flight control computer 6 incorporates the above-mentioned upper limit threshold of the inclination angle into the control algorithm. By correcting the control output, each UAV 8 can maintain the stability of the posture of the platform and the cargo while meeting the overall lifting requirements.

[0224] It can be understood that in the UAV flight system, the flight control system of each UAV 8 is parameterized, including flight mode, attitude control parameters, communication protocol, etc., to ensure that the UAV can accurately receive control instructions from the flight control computer 6 and adjust its own flight attitude according to the control instructions.

[0225] Set the data collection frequency of the flight control navigation system so that it can collect real-time actual flight data such as position information measured by the high-precision GPS module, measured speed and angular velocity, etc.

[0226] The UAV 8 receives the transmission control input and the control torque of the flight control computer 6. According to these control instructions, it adjusts the flight attitude by adjusting the motor speed, blade angle, etc. to ensure that the lifting task is performed according to the predetermined trajectory. At the same time, it collects the actual flight data such as its current posture state and speed, and transmits the results to the flight control computer 6 through the wireless communication module to form a closed-loop control.

[0227] Among them, in the closed-loop control process, when it is detected that the posture of the platform and the cargo is close to or exceeds the set inclination angle threshold, the thrust and attitude adjustment amount of each drone 8 are automatically corrected, so that the control platform and the cargo can quickly return to the safe inclination angle range.

[0228] By means of the flight control navigation system of each drone 8 and the two-way wireless communication between it and the flight control computer 6, the inclination changes of the platform and cargo can be monitored and quickly compensated in real time, thereby achieving stable control of the platform and cargo during the lifting process.

[0229] It can be understood that before the multi-UAV collaborative lifting system provided by the embodiments of the present application is actually put into use, or after it has been in use for a period of time, commissioning operations can be performed, such as no-load commissioning, load commissioning, environmental adaptability testing, and long-term operation testing, etc.

[0230] Specifically, for no-load commissioning, that is, with the goods mounted, start the multi-UAV collaborative lifting system. First, check whether devices such as the integrated navigation system, flight control computer 6, and wireless communication module are working properly and whether data transmission is smooth. Then, send control commands through the flight control computer 6 to test whether the UAVs can perform basic actions such as taking off, hovering, and moving according to the commands, and check whether the connection between the UAVs and the platform is stable and whether there is any abnormal stress on the ropes.

[0231] Load commissioning: After the no-load commissioning is normal, gradually increase the weight of the goods for load commissioning. During the lifting process, real-time monitor the pose information measured by the integrated navigation system, the control commands calculated by the flight control computer 6, and the actual flight data fed back by the UAVs. The UAV 8 measures the actual flight data in real time by means of its own flight control and navigation system, such as measuring the position information with a high-precision GPS module and measuring the speed and angular velocity with an inertial measurement unit. The UAV 8 transmits these feedback messages back to the flight control computer 6 in a timely manner through the wireless communication module. The flight control computer 6 uses a specific error calculation algorithm to calculate the error between the actual pose and the desired pose based on the feedback data. Based on the error calculation results, the flight control computer 6 dynamically adjusts the desired control amount, and gradually converges the error by continuously optimizing the control commands. At the same time, observe the stress situation of the ropes to ensure that the rope control system can accurately calculate the desired attitude vector of the ropes and ensure the stability of the system.

[0232] Environmental adaptability testing: Place the multi-UAV collaborative lifting system under different environmental conditions for testing, such as different wind speeds, temperatures, and humidity environments. Under harsh environments, test the communication stability, positioning accuracy, and heavy-load control performance of the system. According to the test results, further consider optimizing the control strategy of the system, combining multi-level control strategies, error compensation and correction techniques, intelligent algorithms, disturbance rejection control and other technologies to improve the adaptability and robustness of the system in complex environments.

[0233] Long-term operation testing: Conduct long-term continuous lifting tests to simulate actual operation scenarios and test the reliability and durability of the system. During the test, record the faults and abnormal situations that occur in the system, analyze the reasons and make targeted improvements. Through long-term operation testing, continuously optimize the design and performance of the system to ensure that the multi-UAV collaborative lifting system can meet the requirements of actual applications.

[0234] It can be understood that in the embodiments of the present application, through the provided multi-UAV collaborative lifting system and the algorithm model configured in the system platform, when actually lifting goods, the flight control computer in the system can collect the current state information of the overall subsystem (control platform and goods) collected by the navigation system and the pre-configured flight trajectory, calculate the total control input required for the overall subsystem to reach the target position at the next moment, and then based on the set multi-UAV collaborative dynamics model, according to the current state information of each UAV and the state of the connection components between the UAVs and the control platform, resolve the total control input to obtain the sub-control input corresponding to each UAV. Finally, based on the obtained sub-control input, generate control instructions corresponding to each UAV, send them to each UAV, so that after each UAV receives the control instructions, it can respond to the control instructions and execute the corresponding flight operations to ensure that the lifted goods reach the target position on the flight trajectory at the next moment, realizing the efficient coordination of the multi-UAV collaborative lifting system for multiple UAV operation tasks, overcoming the limitation of the insufficient carrying capacity of a single UAV, meeting the carrying capacity requirements of large-mass transportation, and providing precise navigation and control support, significantly improving the stability and robustness of the system.

[0235] Compared with the prior art, the present application has significant advantages in the highly integrated design of the multi-UAV system lifting platform, the optimization of the connection method, and the improvement of the information interaction and control process, and can achieve more precise control and scheduling in complex environments, expanding the application scope of the multi-UAV collaborative lifting system.

[0236] On the other hand, as Figure 3 shown, the present application provides a multi-UAV lifting device, which includes:

[0237] An acquisition module 210, configured to enable the flight control computer to acquire the platform state information of the overall subsystem at the current moment and the UAV state information of each UAV at the current moment. The overall subsystem includes a control platform and a lifted cargo. Multiple such UAVs are detachably connected to the control platform through connection components, and the flight control computer and the navigation system are installed in the control platform;

[0238] A calculation module 220, configured to enable the flight control computer to calculate the total control input of the overall subsystem at the current moment based on the set flight trajectory and the platform state information at the current moment. The total control input represents the total control force required for the overall subsystem to move along the flight trajectory and reach the target position at the next moment;

[0239] A solution module 230, configured to calculate, by the flight control computer, the total control input based on a set multi-UAV cooperative dynamics model and state information of the UAV at the current moment, so as to obtain a sub-control input corresponding to each UAV at the current moment, where the sub-control input represents the sub-control force required for each UAV when the overall subsystem moves along the flight trajectory under the action of multiple UAVs and reaches the target position at the next moment;

[0240] A control module 240, configured to generate, by the flight control computer, a control instruction based on the sub-control input and send the control instruction to each UAV, so that the UAV responds to the control instruction and performs a flight operation according to the sub-control input.

[0241] Optionally, for the multi-UAV cooperative hoisting device provided in an embodiment of the present application, the calculation module is specifically configured to:

[0242] Determine a target state vector of the overall subsystem based on the state vector and flight trajectory at the current moment;

[0243] Determine a signal error between the state vector of the overall subsystem at the current moment and the expected target state vector; and an error between the derivative of the state vector at the current moment and a virtual signal;

[0244] Determine the total control input at the current moment based on the determined error.

[0245] Optionally, for the multi-UAV cooperative hoisting device provided in an embodiment of the present application, the calculation model is expressed as:

[0246]

[0247] where u is the total control input of the overall subsystem at the current moment, f2, g1, and g2 are calculation model coefficients, e1 is the signal error between the state vector of the overall subsystem at the current moment and the expected state vector, k2 is a gain coefficient, is an approximation value of the disturbance term in the calculation model; e2 is the error between the derivative of the state vector of the overall subsystem at the current moment and the virtual signal, is the derivative of the virtual signal;

[0248]

[0249] where x1 is the state vector of the overall subsystem at the current moment, is the target state vector of the overall subsystem, x2 is the derivative of the state vector of the overall subsystem at the current moment, is the virtual signal of the overall subsystem at the next moment, is the virtual signal of the derivative.

[0250] Optionally, for the multi-UAV collaborative lifting device provided by the embodiments of the present application, the UAV state information includes the pose information, speed information of the UAV, and the angle information of the connection components. The resolution module is specifically configured to:

[0251] Based on the multi-UAV collaborative dynamics model, the total control input, and the pose information, speed information of each UAV, and the angle information of the connection components, calculate the corresponding sub-control forces of each UAV at the current moment;

[0252] Based on the sub-control forces of each UAV and the current state information of each UAV, determine the sub-control input of each UAV at the current moment. The sub-control input includes the sum of the control component and the vertical component along the rope direction.

[0253] Optionally, for the multi-UAV collaborative lifting device provided by the embodiments of the present application, the multi-UAV collaborative dynamics distribution model is expressed as follows:

[0254]

[0255] where P is the relationship auxiliary matrix between the overall subsystem and the connection components, and R0 represents the attitude rotation matrix of the overall subsystem in the body coordinate system. is the expected tensile force exerted by the i-th connecting piece on the overall subsystem. is the total control force corresponding to the total control input. is the total control torque corresponding to the total control input.

[0256] Optionally, for the multi-UAV collaborative lifting device provided by the embodiments of the present application, the vertical component of the sub-control input is expressed as:

[0257]

[0258] The control component of the sub-control input is expressed as:

[0259]

[0260]

[0261] where e qi is the difference between the target angle of the i-th connecting piece at the next moment and the angle at the current moment. is the difference between the target angular velocity of the i-th connecting piece at the next moment and the angular velocity at the current moment, expressed as:

[0262] e qi = qi d × qi;

[0263]

[0264] vi is the projection along the direction from the centroid of the i-th unmanned aerial vehicle (UAV) to the connection point of the i-th connecting member on the control platform,

[0265] m i is the weight of the i-th UAV, and l i is the length of the i-th connecting member between the i-th UAV and the control platform, and q i is the unit vector from the centroid of the i-th UAV to the connection point of the i-th connecting member on the control platform, and k q and q i are positive gain coefficients, is the angular acceleration of the i-th connecting member, is the expected angular velocity of the i-th connecting member, is the angular acceleration of the overall subsystem at the current moment, and a i is an auxiliary variable.

[0266] Optionally, for the multi-UAV cooperative lifting device provided in the embodiments of the present application, the control module is specifically configured to:

[0267] The flight control computer generates a control instruction according to the partial control force, the partial control torque, and the set cargo inclination threshold.

[0268] In an exemplary embodiment, a flight control computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 4 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store video tag processing data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements the multi-UAV cooperative lifting method.

[0269] Those skilled in the art can understand, Figure 4The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0270] In an exemplary embodiment, a flight control computer device is further provided. The computer device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the multi-unmanned aerial vehicle collaborative lifting method as described in the above embodiments.

[0271] In some embodiments, a control platform is further provided. A navigation system and the flight control computer in the above embodiments are integrated in the control platform. The control platform is detachably connected to the lifted goods, and the navigation system is used to collect the status information of the control platform.

[0272] In some embodiments, a multi-unmanned aerial vehicle collaborative lifting system is further provided. The multi-unmanned aerial vehicle collaborative lifting system includes multiple unmanned aerial vehicles and the control platform as described in the above embodiments.

[0273] Multiple of these unmanned aerial vehicles are detachably connected to the control platform through a connection component. The control platform is detachably connected to the lifted goods. Multiple of these unmanned aerial vehicles are used to provide pulling force to the control platform.

[0274] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0275] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0276] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0277] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0278] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, computer devices, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0279] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0280] Specific examples are used in this article to elaborate on the principles and implementation methods of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be construed as a limitation of the present application.

Claims

1. A multi-UAV collaborative lifting method, characterized in that The method for collaborative hoisting of unmanned aerial vehicles includes: The flight control computer obtains the platform state information of the overall subsystem at the current moment and the unmanned aerial vehicle state information of each unmanned aerial vehicle at the current moment. The overall subsystem includes a control platform and a hoisted cargo. Multiple unmanned aerial vehicles are detachably connected to the control platform through a connection component. The flight control computer and a navigation system are installed in the control platform; Based on the set flight trajectory and the platform state information at the current moment, the flight control computer calculates the total control input of the overall subsystem at the current moment. The total control input represents the total control force required for the overall subsystem to move to the target position at the next moment according to the flight trajectory; Based on the set multi-unmanned aerial vehicle collaborative dynamics model and the state information of the unmanned aerial vehicle at the current moment, the flight control computer resolves the total control input to obtain the sub-control input of each unmanned aerial vehicle at the current moment. The sub-control input represents the sub-control force required for each unmanned aerial vehicle when the overall subsystem moves to the target position at the next moment under the action of multiple unmanned aerial vehicles according to the flight trajectory; Based on the sub-control input, the flight control computer generates a control instruction and sends it to each unmanned aerial vehicle, so that the unmanned aerial vehicle responds to the control instruction and executes a flight operation according to the sub-control input.

2. The multi-UAV collaborative lifting method according to claim 1, wherein The flight control computer calculates the total control input of the overall subsystem at the current moment based on the set flight trajectory and the platform state information at the current moment, including: The target state vector of the overall subsystem determined based on the state vector and flight trajectory at the current moment; Determining the signal error between the state vector of the overall subsystem at the current moment and the expected target state vector; and the error between the derivative of the state vector at the current moment and the virtual signal quantity; Determining the total control input at the current moment based on the determined error.

3. The multi-UAV collaborative lifting method according to claim 2, wherein The calculation model is expressed as: where \(u\) is the total control input of the overall subsystem at the current moment, \(f_2\), \(g_1\), and \(g_2\) are calculation model coefficients, \(e_1\) is the signal error between the state vector and the expected state vector of the overall subsystem at the current moment, \(k_2\) is the gain coefficient, which is the approximation value of the disturbance term in the calculation model; \(e_2\) is the error between the derivative of the state vector and the virtual signal quantity of the overall subsystem at the current moment, which is the derivative of the virtual signal quantity; where x1 is the state vector of the overall subsystem at the current moment, is the target state vector of the overall subsystem, x2 is the derivative of the state vector of the overall subsystem at the current moment, is the virtual signal quantity of the overall subsystem at the next moment, is the virtual signal quantity derivative.

4. The multi-UAV collaborative lifting method according to claim 3, wherein The unmanned aerial vehicle state information includes the pose information, speed information of the unmanned aerial vehicle, and the angle information of the connection component. Based on the set multi-unmanned aerial vehicle collaborative dynamics model and the state information of the unmanned aerial vehicle at the current moment, the flight control computer resolves the total control input to obtain the sub-control input of each unmanned aerial vehicle at the current moment, including: Calculating the corresponding sub-control force of each unmanned aerial vehicle at the current moment based on the multi-unmanned aerial vehicle collaborative dynamics model, the total control input, and the pose information, speed information of each unmanned aerial vehicle, and the angle information of the connection component; Based on the sub-control force of each unmanned aerial vehicle and the current state information of each unmanned aerial vehicle, determining the sub-control input of each unmanned aerial vehicle at the current moment. The sub-control input includes the sum of the control component and the vertical component along the rope direction.

5. The multi-UAV collaborative lifting method according to claim 4, characterized in that, The multi-unmanned aerial vehicle collaborative dynamics distribution model is expressed as follows: where P is the relationship auxiliary matrix between the overall subsystem and the connection component, and R0 represents the attitude rotation matrix of the overall subsystem in the body coordinate system. is the expected tensile force applied by the i-th connecting piece to the overall subsystem. is the total control force corresponding to the total control input. is the total control torque corresponding to the total control input.

6. The multi-UAV collaborative lifting method according to claim 5, wherein, The vertical component of the sub-control input is expressed as: The control component of the sub-control input is expressed as: wherein, is the difference between the target angle of the i-th connecting member at the next moment and the angle at the current moment, is the difference between the target angular velocity of the i-th connecting member at the next moment and the angular velocity at the current moment, expressed as: v i is the projection along the direction from the centroid of the i-th UAV to the connection point of the i-th connecting member on the control platform m i is the weight of the i-th drone, l i is the length of the i-th connecting member between the i-th drone and the control platform, q i is the unit vector from the centroid of the i-th drone to the connection point of the i-th connecting member on the control platform, k q and q i are positive gain coefficients, is the angular acceleration of the i-th connecting member, is the expected angular velocity of the i-th connecting member, is the angular acceleration of the overall subsystem at the current moment, a i is an auxiliary variable.

7. The multi-UAV collaborative lifting method according to any one of claims 1-6, characterized in that The flight control computer generates a control instruction based on the sub-control input, including: The flight control computer generates a control instruction according to the sub-control force, the sub-control torque, and the set cargo inclination threshold.

8. A flight control computer device, characterized in that, The computer device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the multi-UAV collaborative lifting method according to any one of claims 1-7.

9. A control platform, characterized in that, The control platform is integrated with a navigation system and a flight control computer according to claim 8, the control platform is detachably connected to the lifted goods, and the navigation system is used to collect the status information of the control platform.

10. A multi-UAV collaborative lifting system, characterized in that, The multi-UAV collaborative lifting system includes multiple UAVs and a control platform according to claim 9; Multiple UAVs are detachably connected to the control platform through a connection component, the control platform is detachably connected to the lifted goods, and multiple UAVs are used to provide pulling force to the control platform.

Images