Cooperative flight system based on magnetic attraction grabbing and collision docking of spherical unmanned aerial vehicle

Abstract

The application discloses a cooperative flight system based on magnetic attraction grabbing and collision butt joint of spherical unmanned aerial vehicles, and belongs to the technical field of unmanned aerial vehicles. The system comprises an unmanned aerial vehicle, a connecting arm arranged on the unmanned aerial vehicle, and a control module of the unmanned aerial vehicle. External force is perceived through collision detection, so that the attitude of the unmanned aerial vehicle is adjusted for butt joint of the connecting arm. A threaded connecting piece arranged in cooperation with another unmanned aerial vehicle is arranged on the connecting arm. The control module controls the unmanned aerial vehicle to rotate axially with the connecting arm as the axis, so that fixed connection between the unmanned aerial vehicles is completed. The unmanned aerial vehicle is provided with a spherical shell. A through hole is arranged on the shell. A magnetic attraction assembly is arranged on the spherical shell outside the through hole. The preliminary butt joint of the unmanned aerial vehicle is completed through magnetic attraction. The connecting arm is a telescopic and bendable L-shaped cantilever. After the threaded connection of the cantilever in the vertical state is completed, the cantilever is continuously stretched, so that the preliminary butt joint of the magnetic attraction assembly is peeled off, and another unmanned aerial vehicle is horizontally rolled to form two unmanned aerial vehicles in parallel, and the bottom is connected through the L-shaped cantilever.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicles, and particularly relates to a cooperative flight system based on magnetic attraction grabbing and collision docking of spherical unmanned aerial vehicles. BACKGROUND

[0002] In the existing unmanned aerial vehicle technology, although unmanned aerial vehicles have been widely applied in flight control and object grabbing, they still face many challenges in multi-unmanned aerial vehicle cooperation, accurate docking and autonomous operation. The traditional multi-unmanned aerial vehicle cooperative operation method relies on highly precise sensors and complex control algorithms, which makes it less adaptable in complex environments and has a high cost. Especially in precise operations such as unmanned aerial vehicle docking and carrying, the existing technology often needs to rely on complicated mechanical structures and complex perception systems, increasing the weight and energy consumption of the system. For example, when unmanned aerial vehicles dock, a visual perception system is usually used, but due to the complex air docking environment, the visual sensor has a certain blind area during docking, which affects the docking. At the same time, the visual sensor and complex mechanical docking structure increase the load of the unmanned aerial vehicle, which is not conducive to the balance and endurance of the unmanned aerial vehicle. Therefore, it is of great significance to develop a more simple and efficient unmanned aerial vehicle system that can stably perform tasks in complex environments. SUMMARY

[0003] To solve the problems of the prior art and improve the reliability of unmanned aerial vehicle docking, the application adopts the following technical solutions:

[0004] The cooperative flight system based on collision docking includes an unmanned aerial vehicle and a connecting arm arranged on the unmanned aerial vehicle, and a control module of the unmanned aerial vehicle. The control module adjusts the attitude of the unmanned aerial vehicle for the docking of the connecting arm by detecting and perceiving external force through collision detection. This perception capability can be approximately considered as a kind of touch perception, i.e. dynamic perception, which can perceive the size and direction of external interference force. A threaded connecting piece is arranged on the end of the connecting arm of the other unmanned aerial vehicle. The control module controls the unmanned aerial vehicle to rotate around the connecting arm as the axis to complete the fixed connection between the unmanned aerial vehicles. This docking method is more accurate and stable than the docking based on vision, and reduces the load of the unmanned aerial vehicle.

[0005] Further, the connecting arm is arranged transversely on one side of the unmanned aerial vehicle. The unmanned aerial vehicle is vertically flipped around the connecting arm as the axis, so that the threaded connecting piece on the connecting arm is threadedly connected with the threaded connecting piece on the connecting arm of the other unmanned aerial vehicle.

[0006] Further, the connecting arm is arranged transversely on one side of the unmanned aerial vehicle. The unmanned aerial vehicle is vertically flipped around the connecting arm as the axis, so that the threaded connecting piece on the connecting arm is threadedly connected with the threaded connecting piece on the connecting arm of the other unmanned aerial vehicle.

[0007] Furthermore, the connecting arm is a bendable L-shaped cantilever set at the bottom of the drone. When docking, the cantilever is in a vertically downward state, so as to be connected to the cantilever at the bottom of another drone flying inverted by means of an upward cantilever, through a thread. After docking, the other drone is horizontally rolled to form two drones running in parallel, and the bottom is connected by a cantilever bent into an L shape.

[0008] Furthermore, the drone is equipped with a spherical shell with through holes. The connecting arm is a telescopic connecting arm. The threaded connector of the connecting arm contacts a threaded connector on another drone through the through holes inside the spherical shell and rotates around the connecting arm as an axis to complete the fixed connection. The spherical shell can not only achieve stable balance control through the cooperation of sensors and flywheel system, but also protect the internal mechanical structure of the drone and the connecting arm. It is also suitable for tactile collision detection to better complete dynamic tactile perception.

[0009] Furthermore, a magnetic suction component is provided on the spherical shell outside the through hole. The magnetic suction component is configured to cooperate with the magnetic suction component outside the through hole of another drone. The initial docking of the drone is completed by magnetic attraction, and then the connecting arm is used for fixed connection.

[0010] Furthermore, the magnetic attraction component is a ring structure around the through hole, so that when another drone rotates relative to the current drone about the connecting arm as an axis, the initial docking between the current drone and the other drone can still be maintained, avoiding the initial docking separation caused by external forces received in the flight environment or changes in posture caused by the rotation of the drone.

[0011] Furthermore, during the separation process of the system, the UAV rotates in the opposite direction around the connecting arm to separate the fixed connection between the UAVs; the UAVs fly in opposite directions based on the magnetic connection to peel off the initial docking of the magnetic components. It is necessary to consider that the magnetic force is less than the lift of the UAVs or the driving force of the two UAVs flying in opposite directions.

[0012] Furthermore, during the connection process of the system, after the retractable connecting arm is fixedly connected to another drone, it continues to extend to allow the initial docking and separation of the magnetic components. During the separation process of the system, the drone first rotates in the opposite direction around the connecting arm as an axis to separate the fixed connection between the drones, and then the retractable connecting arm is retracted. Thus, during the separation process, there is no need to consider the separation problem of the magnetic components adsorption.

[0013] Furthermore, the connecting arm is a telescopic, bendable L-shaped cantilever mounted on the bottom of the drone;

[0014] During the connection process of the system, the drone and another drone flying inverted initially dock through their respective bottom magnetic attraction components. After being threaded together by the vertical cantilever, the cantilever continues to extend to disengage the initial docking of the magnetic attraction components and cause the other drone to roll horizontally to form two drones running in parallel, with the bottom connected by the cantilever bent into an L shape.

[0015] During the separation process of the system, another drone is made to roll vertically, directly separating the fixed connection between the L-shaped cantilever arms.

[0016] The advantages and beneficial effects of this invention are as follows:

[0017] (1) The system design is highly simplified: The combination of magnetic collision and robotic arm docking simplifies the overall structure of the UAV. The robotic arm only needs a few degrees of freedom to complete the docking and assembly tasks, which greatly reduces the complexity of the equipment and energy consumption compared with traditional complex docking mechanisms, and improves the reliability of the mission.

[0018] (2) The ease of operation is greatly improved: the innovative method of docking the spherical drone with magnetic collision eliminates the need for high-precision scanning equipment and complex visual recognition algorithms, greatly simplifying the operation process and significantly reducing technical requirements and costs, making it more suitable for scenarios that traditional drones cannot complete, such as high altitude and narrow space.

[0019] (3) High-efficiency collaboration and precise assembly: Employing dynamic control and multi-machine coordination algorithms, the spherical UAV can quickly achieve a relatively stationary state. After initial docking via magnetic collision, assembly is completed through precise adjustments by the robotic arm. The entire process is time-efficient and highly accurate, making it suitable for high-efficiency tasks such as emergency rescue or high-altitude construction.

[0020] (4) Adaptability to various complex environments: The spherical UAV shell has impact resistance and flexible attitude control capabilities, enabling it to work stably in complex environments such as strong winds, high temperatures, and low temperatures. In addition, the combined action of the magnetic suction device and the robotic arm can overcome the docking difficulties brought about by complex environments, making the invention highly applicable even under extreme conditions.

[0021] (5) Modular Expansion and Mission Diversification: The modular structure achieved through collision docking makes it possible to expand the mission capabilities of UAVs. For example, heavy-duty platforms and building components can be assembled through aerial docking, or a UAV headquarters can be established for mission management. Modular capability gives UAVs a wider range of mission adaptability and flexibility, and is also a necessary foundation for the realization of future UAV aerial headquarters.

[0022] (6) Remote Operation and Automation Capabilities: This invention supports remote operation. Combined with pre-programmed path planning and docking algorithms, complex docking or assembly tasks can be completed without human intervention. Even in unattended environments, it can efficiently complete tasks, making it particularly suitable for disaster relief and other missions. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the system structure according to an embodiment of the present invention.

[0024] Figure 2 This is a structural diagram of the UAV docking state in the system according to an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the parallel docking state in the system according to an embodiment of the present invention.

[0026] Figure 4 This is a flowchart of the docking process in an embodiment of the present invention. Detailed Implementation

[0027] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0028] like Figure 1 As shown, a collaborative flight system based on magnetic grasping and collision docking of a spherical UAV is used for aerial assembly tasks. The system includes a spherical UAV 1 and a robotic arm mounted on it. In this system, the UAV adjusts its attitude through collision, causing the magnetic components 4 below it to attract each other and perform initial docking. Once the initial magnetic docking is complete, the robotic arm (cantilever) performs precise docking through extension and retraction. The control module completes the entire collaborative process through a simple flight algorithm, reducing the complex perception and control systems required for traditional UAV docking.

[0029] The spherical unmanned aerial vehicle (UAV) 1 includes a magnetic attachment assembly 4, a control module, a power module, a communication module, and a sensing module. In the control module, the flight control unit (FCU) plans the flight path according to instructions and performs pitch and yaw movements to control the insertion, rotation, and docking actions of the robotic arm. The power module provides the UAV with flight power, employing a rotor system composed of an electric motor and an electronic speed controller to achieve flight and hovering operations. The communication module communicates bidirectionally with the control module to ensure real-time data exchange during flight.

[0030] The spherical drone 1 adopts a spherical shell 2, on which multiple strong magnetic components 4 are evenly distributed for docking and fixing between drones, so as to realize the adsorption and docking between drones.

[0031] The control module is responsible for the coordinated control of the entire UAV's flight control, attitude adjustment, and magnetic docking process. It includes a flight control module, a collision detection and control unit, a sensor fusion unit, a vision recognition unit, and a main control unit.

[0032] The flight control module includes a flight control unit (FCU), an inertial measurement unit (IMU), a GPS unit, and a barometer.

[0033] The Flight Control Unit (FCU) is responsible for attitude control, heading control, and position control. It combines data from the IMU, GPS, and barometer to execute flight control algorithms and adjust the UAV's flight status in real time to ensure accurate positioning and stable flight in three-dimensional space. The IMU monitors the UAV's attitude changes in real time, and the barometer measures altitude. The GPS unit provides accurate position data.

[0034] The collision detection and control unit uses devices such as ultrasonic sensors and lidar to perceive the environment in real time, monitor the relative positions between drones, and automatically adjust the flight path or stop flying to avoid a collision when a potential collision risk is detected.

[0035] The visual recognition unit, including a high-definition camera and an image processing unit, employs computer vision algorithms (such as convolutional neural networks, CNN) for environmental perception and target recognition. The visual recognition unit can identify ground targets, docking objects, and surrounding obstacles in real time, assisting the UAV in making precise flight adjustments.

[0036] The main control unit, consisting of an embedded processor (such as an ARM Cortex series processor) and a real-time operating system (RTOS), receives data from various sensors and coordinates functions such as flight control, robotic arm control, and target recognition based on preset control algorithms and mission requirements. Flight path planning generates the optimal path based on the A* algorithm.

[0037] f(n) = g(n) + h(n)

[0038] Where g(n) represents the cost from the starting point to node n, and h(n) represents the heuristic estimate from node n to the target.

[0039] The power module, comprising four independent electric motors and an electronic speed controller (ESC), achieves stable flight of the UAV through a PID control algorithm. Each motor controls the rotor speed, and attitude control is achieved by adjusting thrust. The UAV's dynamics can be described as follows:

[0040]

[0041] in, Let m represent the drone's position vector, g represent the drone's mass, and e represent the acceleration due to gravity. z R represents the motor speed, R represents the UAV's rotation matrix, and T represents the total thrust vector. This represents the magnitude of the UAV rotation matrix. This indicates the rotor angular velocity.

[0042] Attitude control uses a PID controller, and the formula is:

[0043]

[0044] Where, θ d Let θ represent the target attitude angle, t represent the current attitude angle, τ represent the unit time, and K represent the time delay. p ,K i ,K d These represent the proportional, integral, and differential coefficients, respectively.

[0045] The communication module, including a radio communication module and a high-gain antenna, ensures stable data transmission with the ground control station, supporting remote operation and real-time monitoring.

[0046] The sensing module, including ultrasonic sensors, infrared sensors, and lidar, monitors the surrounding environment in real time, providing obstacle detection and precise spatial positioning.

[0047] Each drone is equipped with an L-shaped telescopic robotic arm for precise docking and grasping operations during aerial assembly. Driven by a servo motor, the robotic arm can precisely bend, extend, and rotate. The robotic arm used in this invention is an L-shaped cantilever 3, which is also equipped with a magnetic attraction device. This allows the arm to adjust its posture through collision during docking, and the docking task is completed through the telescopic and rotating movements of the L-shaped cantilever 3.

[0048] The robotic arm consists of multi-degree-of-freedom joints and servo motors, with each joint equipped with a high-precision position sensor (such as a rotary encoder). Through closed-loop control, the robotic arm can precisely bend and straighten to adapt to different task requirements.

[0049] The multi-degree-of-freedom motion of a robotic arm can be described by DH parameters, and its end-effector pose matrix is:

[0050]

[0051] Where n represents the number of assumed joints, T i Let θ represent the transformation matrix. i α represents the joint angle. i d represents the length of the link. i This indicates the link offset.

[0052] likeFigure 2 , Figure 3 As shown, the magnetic attraction components 4 are distributed on the spherical shell 2. For example, the bottom of the spherical shell of the first drone has a lower through hole, and the lower magnetic attraction components are arranged around the lower through hole. The top of the spherical shell of the second drone has an upper through hole, and the upper magnetic attraction components are also arranged around the upper through hole. The magnetic poles of the lower magnetic attraction components correspond to those of the upper magnetic attraction components. When the two drones approach each other, they are initially docked by the attraction of the magnetic attraction components 4. However, the magnetic attraction force should not be too large, otherwise it will affect the separation of the drones. This results in the magnetic attraction connection between the two drones being unstable. Further fixation is required by the robotic arm. Therefore, after the magnetic attraction docking, the screw at the bottom of the retractable lower cantilever of the first drone extends downward through the lower through hole, and the screw hole at the top of the retractable upper cantilever of the second drone extends upward through the upper through hole, so that the screw and the screw hole are docked. The screw and the screw hole are tightened by the horizontal rotation of the second drone relative to the first drone.

[0053] Furthermore, the magnetic attraction component 4 can be a ring structure surrounding the through hole, so that when the second drone rotates horizontally relative to the first drone, the initial docking between the first drone and the second drone can still be maintained, avoiding the initial docking separation caused by the change in posture due to rotation.

[0054] Furthermore, the upper and lower cantilever arms are telescopic L-shaped cantilever arms 3. As the upper and lower cantilever arms continue to extend and the drone rotates relative to each other, while the screw holes are fixedly connected, the initial docking of the magnetic attraction component 4 is peeled off. The second drone flips vertically to one side, forming a state parallel to the first drone. At this time, the upper and lower cantilever arms change from the original vertical connection in the shape of 1 to the connection in the shape of L mirror symmetry.

[0055] In another embodiment, the two drones are equipped with left and right retractable arms respectively. They are initially connected through the magnetic attraction components 4 around the through holes on their respective sides. Then, by extending the left and right retractable arms and combining the vertical flip of the second drone relative to the first drone, the initial connection of the magnetic attraction components 4 is directly peeled off, the screws and screw holes are tightened, and the two drones are in parallel state.

[0056] Regarding the separation of the drones, the initial connection of the magnetic attraction component 4 can be restored first by retracting the cantilever. Then, the fixed connection between the cantilever arms can be separated by the relative rotation of the drones. Since the magnetic force is not set too high, for example, if the magnetic force is set less than the lift of the drones, the initial connection of the magnetic attraction component 4 can be separated by the upward lifting of the first drone, the descent of the second drone, or the counter-current flight of the two drones. Alternatively, the fixed connection of the cantilever arms can be separated directly by the relative rotation of the drones, without considering the magnetic attraction issue.

[0057] Magnetic component 4 can also use an electromagnet, but electromagnets are too bulky and consume too much energy, which would bring too much load and energy consumption to the drone.

[0058] The formula for magnetic attraction force is as follows:

[0059]

[0060] Where μ0 is the vacuum permeability, m1 and m2 are the magnetic moments of the UAV and the airborne terminal, respectively, and r is the distance between them.

[0061] like Figure 4 As shown, the aerial assembly operation based on UAV collision docking control includes the following steps:

[0062] Step 1: Preliminary task planning and path generation;

[0063] Before the mission begins, the ground control station uses the mission planning system to set the UAV's takeoff position, target position, and the objectives to be performed (such as object grasping or assembly positions). The ground control station then initiates the mission by transmitting control commands.

[0064] Path planning: Using classic path planning algorithms (such as A*, Dijkstra's algorithm, or RRT), the ground control station calculates the optimal flight path based on mission requirements and the UAV's current position. Path planning takes into account factors such as obstacles and airspace restrictions to ensure safety during flight.

[0065] Step 2: Drone takeoff and approach phase;

[0066] According to the mission requirements, the drone takes off and begins to fly to the target location. During the flight, the control module adjusts the attitude through the flight control unit to ensure stable flight.

[0067] Position adjustment and hovering: After reaching the target position, the UAV uses a PID control algorithm to adjust its attitude, maintain a hovering state, ensure stability in the air, and prepare for precise docking.

[0068] After takeoff, the drone adjusts to the target position, and hovering stability is ensured by the following formula:

[0069]

[0070] Where u represents the control variable, x d The x-coordinate of the link is represented by y. d The vertical coordinate of the link is represented by x, the horizontal coordinate of the drone is represented by y, the vertical coordinate of the drone is represented by t, and the unit time is represented by K. p ,K i ,K d These represent the proportional, integral, and differential coefficients, respectively.

[0071] Step 3: Collision docking and robotic arm operation, including the following steps:

[0072] Step 3.1: Initial docking (magnetic attraction stage);

[0073] As the two spherical drones approach each other, they detect each other's positions using radar or sensors and adjust their attitudes to bring their relative speed close to zero. At this point, the magnetic attraction device between the two drones activates, ensuring initial docking stability.

[0074] Magnetic attraction formula:

[0075]

[0076] Where μ0 is the vacuum permeability, m1 and m2 are the magnetic moments of the UAV and the airborne terminal, respectively, and r is the distance between them.

[0077] Step 3.2: Precise docking of the robotic arm;

[0078] After the initial magnetic connection is completed, the cantilever begins to extend and approaches the precise docking process. During this stage, the screw at one end of the cantilever aligns with the screw hole at the other end, achieving a more secure physical connection through precise robotic arm control.

[0079] Threaded joint mechanics: Assuming the screw's outer diameter is d, pitch is p, and torque is T, then the axial force F generated by the screw during rotation... axial The following formula can be used to estimate:

[0080]

[0081] Where r represents the radius of the screw rotation, and T represents the applied torque. To complete the docking, the cantilever needs to precisely adjust the torque through rotation to ensure that the screw is securely screwed into the threaded hole.

[0082] Robotic arm position control: using kinematic formulas:

[0083] q = q0 + J·Δx

[0084] Where q represents the joint coordinates of the robotic arm, q0 represents the joint coordinates of the UAV, J represents the Jacobian matrix, and Δx represents the displacement increment of the target position.

[0085] Attitude Adjustment: After the robotic arms achieve precise docking, the cantilever will revert from a straight shape to an L-shape, and the attitudes of the two drones will begin to adjust, bringing them to the same horizontal plane. This process ensures the relative attitude stability of the drones, resulting in the final cooperative flight attitude.

[0086] Attitude Adjustment Formula: The attitude adjustment process involves the application of a rotation matrix. Let the rotation matrix of the UAV be R, and its control input be angular velocity ω. Then, the attitude adjustment process of the UAV can be described as follows:

[0087]

[0088] in, Let be the antisymmetric matrix of angular velocity, representing the dynamic changes during rotation.

[0089] Stability control: Attitude stability can be achieved using a PID controller to ensure that the UAV quickly and stably adjusts to a uniform attitude. The control signal u is:

[0090]

[0091] Among them, K p ,K i ,K d These represent the proportional, integral, and differential coefficients, respectively, and e represents the deviation between the current attitude and the target attitude.

[0092] Implementation Case 1: Four drones collide and dock in mid-air for merging and heavy object transport.

[0093] Background: This embodiment details the process of four drones colliding, docking, and merging in mid-air to perform a heavy-load transport task. The process includes mathematical analysis and algorithmic details regarding flight path optimization, magnetic docking, precise docking by robotic arms, sensor fusion, relative position coordination, and collaborative control.

[0094] 1. Calculation of UAV takeoff and flight path

[0095] 1.1 Flight Path Optimization and Positioning

[0096] Four drones take off and fly to the designated target area to dock. To ensure optimized flight paths and avoid collisions, the flight path of each drone needs to be precisely calculated.

[0097] Displacement calculation:

[0098] The initial position of each drone is P. i =(x i ,y i ,z i The target location is T. i =(x i ′ ,y i ′ ,z i ′ The displacement of UAV i

[0099] Quantity ΔP i The calculation formula is:

[0100] ΔP i =T i -P i =(x i ′ -x i ,y i ′ -y i ,z i ′ -z i )

[0101] Each drone needs to adjust its flight path based on this displacement.

[0102] Flight path optimization:

[0103] The goal of flight path optimization is to ensure smooth flight of the UAV and avoid abrupt changes. Commonly used optimization algorithms include Bézier curves and spline curves. Assume the UAV's flight path is formed by several control points P0, P1, P2, ..., P... n The flight path P(t) is defined as follows:

[0104]

[0105] Where, α i (t) is the weight function for the control points, P i Let be the coordinates of the control points, and t be the path parameter. Weighting function α i (t) calculations typically rely on specific interpolation algorithms, such as B-spline interpolation.

[0106] 1.2 Flight Control System

[0107] The flight control of a drone relies on real-time adjustments to its attitude, speed, and deviation from the target position.

[0108] Acceleration control of drones:

[0109] The acceleration a of the drone i Determined by the difference between thrust and gravity:

[0110]

[0111] Among them, F thrust For thrust, m i Let g be the mass of the drone, and g be the acceleration due to gravity.

[0112] Equations of motion:

[0113] According to Newton's second law, the equation of motion is:

[0114]

[0115] Where, r i It is the location of the drone, F drag For air resistance, the formula for calculating air resistance is:

[0116]

[0117] Among them, C d Where ρ is the drag coefficient, A is the air density, and v is the frontal area. i The speed of the drone.

[0118] PID control:

[0119] Flight control of unmanned aerial vehicles (UAVs) is often achieved through PID control. The controller determines the flight path based on the current error e(t) = x. target -x current The flight control signal is adjusted using the derivative (velocity) and integral of the error. The output u(t) of the PID controller is:

[0120]

[0121] Among them, K p ,K i ,K d These are the proportional, integral, and differential gains, respectively.

[0122] 2. Collision docking and magnetic attraction calculation

[0123] 2.1 Magnetic attraction effect

[0124] During the collision docking process, each drone is equipped with a magnetic attraction device for initial docking. The magnitude of the magnetic attraction force is related to the distance between the two drones, and the magnetic attraction force F... mag Estimated using the following formula:

[0125]

[0126] Where μ0 is the vacuum permeability, m1 and m2 are the magnetic moments of the UAV and the airborne terminal, respectively, and r is the distance between them.

[0127] 2.2 Magnetic docking process

[0128] When the drone approaches, the magnetic attraction device draws it in using magnetic force. Let the relative velocity between drone i and drone j be v. ij Their relative motion follows the equations below:

[0129]

[0130] Where, Δrij Let i be the relative position of drone i and j. This refers to air resistance.

[0131] 2.3 Achieving docking through magnetic attraction

[0132] When the magnetic attraction force F m When a certain value is reached, it reduces the relative speed between the two drones, allowing them to approach steadily and complete the initial docking. At this point, the drone's robotic arm extends to perform a precise docking.

[0133] 3. Kinematic control and precise docking of robotic arms

[0134] 3.1 Kinematics of the Robotic Arm

[0135] Each drone is equipped with a multi-degree-of-freedom robotic arm. Inverse kinematics is used to precisely control the extension, retraction, and rotation of the robotic arm. Assume the end effector position of the robotic arm is P. end = (x, y, z), joint angles θ1, θ2, θ3, the relationship between the end effector position of the robotic arm and the joint angles is:

[0136] T = A1(θ1)A2(θ2)A3(θ3)

[0137] Among them, A i (θ i Let be the transformation matrix of the i-th joint. Each transformation matrix describes the spatial position transformation of the robotic arm from one joint to the next.

[0138] 3.2 Precise docking of robotic arms

[0139] During precise docking, the robotic arm needs to adjust its posture to ensure that its end effector perfectly aligns with the target docking point. Assume the target position of the robotic arm's end effector is P. target The formula for calculating the end position adjustment angle Δθ is:

[0140] Δθ=θ final -θ initial

[0141] During this process, the robotic arm solves for the change in position using inverse kinematics and adjusts the joint angles to reach the target position.

[0142] 4. Sensor fusion and relative position coordination

[0143] 4.1 Sensor Fusion

[0144] The drone acquires real-time attitude, position, and velocity information through sensors (such as IMU, GPS, and visual sensors). This information is fused using a Kalman filter to improve positioning accuracy. The state update formula for the Kalman filter is:

[0145] x k+1 =Ax k +Bu k +w k

[0146] Where A is the state transition matrix, B is the control matrix, and u k To control the input, w k This is process noise.

[0147] 4.2 Relative Position Adjustment

[0148] The drones need to adjust their relative positions to ensure a precise docking process. Let Δr be the relative position between drones i and j. ij The relative position is adjusted using the following governing equations:

[0149]

[0150] Where k is the control gain, which ensures the relative position between drones is stable and avoids excessive oscillation.

[0151] 5. Merging and Task Execution

[0152] 5.1 Consortium Control and Task Allocation

[0153] Once the four drones successfully merge into a single unit, they will execute subsequent tasks. Task scheduling is based on the state of each sub-drone, with the target task T being an item transport task. The unit control system coordinates flight and task execution through a distributed control algorithm.

[0154]

[0155] Among them, T i Sub-tasks performed by each sub-drone.

[0156] 5.2 Cooperative Flight and Precision Control

[0157] During the combined flight, a distributed PID control algorithm adjusts the speed and position of each sub-UAV in real time to ensure they coordinate and accurately complete their tasks. The control system needs to continuously monitor the status of each sub-UAV to ensure the stability of the coordinated flight. Through this series of mathematical models and control algorithms, the four UAVs can accurately perform aerial collision docking and merging, and complete complex tasks such as heavy object transport.

[0158] Implementation Case 2: Aerial Building Structure Assembly System

[0159] Background and Issues

[0160] In certain specialized construction scenarios, particularly during the construction of high-rise buildings or temporary facilities, traditional ground-based and climbing equipment cannot meet the demands for efficient and safe structural installation. For example, in confined areas of city centers or complex high-altitude environments, the use of traditional hoisting equipment may be limited by space constraints, or high-altitude operations may pose significant safety hazards. Utilizing the collision-and-dock technology of a spherical UAV1, rapid and precise structural assembly can be achieved in the air.

[0161] Core technologies

[0162] Collision docking: The spherical UAV1 docks in mid-air through collision and attitude adjustment, and completes the assembly of structural modules through a magnetic suction system or robotic arm for precise positioning.

[0163] Modular buildings: Building components are pre-designed as modules that can be quickly connected. Each module can independently bear a certain structural load and can be quickly installed in the air.

[0164] Robotic arm and automatic adjustment: Initial docking is completed by magnetic attraction, and further precise adjustment and fixation are performed by the robotic arm to complete the connection between components.

[0165] Detailed mathematical analysis and details

[0166] 1. Flight path and collision simulation

[0167] During aerial assembly, drones need to plan precise flight paths based on the location and altitude of the building components. Optimizing the flight path ensures that the drone can complete the docking in the shortest possible time while avoiding collisions or errors.

[0168] Flight trajectory optimization: To ensure that the drone can fly to the designated location efficiently and smoothly, we need to calculate the shortest time path of the flight trajectory, considering the following factors:

[0169] Aerodynamics: Adjusting the flight path by solving the influence of airflow during flight.

[0170] Rotational torque: The rotation angle of the drone needs to be controlled during flight to avoid attitude instability caused by angle errors.

[0171] Dynamic constraints: Calculate the maximum acceleration and turning angle during flight to avoid exceeding the physical limits of the drone during flight.

[0172] Shortest path algorithms (such as Dijkstra's or A*) are used to optimize the flight path of drones to ensure that multiple drones can reach the target location quickly and efficiently when working together.

[0173] Mathematical model:

[0174]

[0175] Here, r(t) represents the position of the UAV at time t, and the goal is to minimize the flight time from the starting point to the destination.

[0176] 2. Dynamics analysis of collision and magnetic docking

[0177] The collision and attitude adjustment of the spherical drone 1 are crucial steps in the entire assembly process. Whether it is the initial docking through magnetic attraction or the precise adjustment by a robotic arm, the dynamic characteristics of the drone must be considered during flight.

[0178] Collision Modeling: We can use a rigid collision model to describe the collision process between the drone and the building module. Let the mass of the drone be m, its velocity be v, and its angular velocity after the collision be ω. Assuming the collision is elastic, momentum and angular momentum are conserved.

[0179] Conservation of momentum: mv1 + Mv2 = mv ′ 1+Mv ′ 2

[0180] Conservation of angular momentum: I1ω1 + I2ω2 = I1ω ′ 1+I2ω ′ 2

[0181] Where m and M are the masses of the drone and the building module, respectively, v1 and v2 are the velocities before the collision, and ω1 and ω2 are the angular velocities before the collision.

[0182] Magnetic attraction model: During initial docking via magnetic attraction, the drone attracts the building module through a magnetic field. The magnitude and distribution of the magnetic attraction can be described by the following formula:

[0183]

[0184] Among them, F mag Let μ be the magnetic attraction force, μ0 be the permeability of free space, m1 and m2 be the magnetic moments of the two objects, and r be the distance between the two objects.

[0185] When calculating magnetic attraction force, the magnetic attraction layout on the surfaces of the drone and the building module needs to be considered to ensure that the magnetic force can be accurately applied to the contact surfaces of the drone and the building module.

[0186] 3. Ensure the accuracy of the docking error model and adjustment.

[0187] For high-precision assembly, especially in multi-drone collaborative processes, the accumulation of errors must be considered. Errors can come from multiple sources, such as navigation errors, deviations caused during collisions, and robotic arm operation errors.

[0188] Error propagation model: To improve the overall accuracy of the system, a Kalman filter is used to fuse data from multiple sensors, thereby reducing error propagation. Assume a multi-sensor system where the measurement value of each sensor i is z. i Its noise is v i The following filtering model can be established:

[0189] z i =H i x+v i

[0190] Where x is the system state vector, H i Let v be the observation matrix. i For measuring noise.

[0191] The Kalman filter reduces error by updating the estimate of the system state, as analyzed above.

[0192] Post-collision adjustment strategy: When using a robotic arm for precise adjustments after a collision, the kinematic limitations of the robotic arm and the docking accuracy with the building module must be considered. For example, if the drone experiences a slight deviation during docking, the robotic arm can be adjusted using inverse kinematics to achieve the required accuracy.

[0193] q final =q initial +Δq

[0194] Where, q initial and q final These are the initial and target joint angles of the robotic arm, respectively, and Δq is the fine-tuning amount.

[0195] 4. Structural stability analysis

[0196] Once the modules are docked, a structural stability analysis must be performed to ensure that the connections between the modules do not loosen or shift due to external forces (such as wind or vibration).

[0197] Static analysis: The finite element method (FEM) was used to analyze the joints to ensure that the module connections would not become unstable under external forces. The stiffness matrix of each connection point was constructed, the stress distribution of the system was calculated, and the connection strength between the modules was verified.

[0198] F = Kd

[0199] Where F is the external force, K is the stiffness matrix, and d is the displacement.

[0200] Vibration and Dynamic Stability: In the external environment, the modules of the docked UAV may be affected by vibration or other external forces. Therefore, vibration analysis of the connections between modules is required to ensure structural stability during long-term use.

[0201]

[0202] Where m is the mass matrix, c is the damping matrix, k is the stiffness matrix, and F ext External incentives.

[0203] 5. Specific steps

[0204] Similar to Case 1, I will not repeat myself.

[0205] Implementation Case 3: Aerial Unmanned Aerial Vehicle (UAV) Terminal: Detailed Analysis and Implementation

[0206] Background and Issues

[0207] With the rapid development of drone technology, more and more drones are being used for various high-altitude tasks, such as construction, environmental monitoring, and rescue missions. To improve the collaborative efficiency of drone swarms and their ability to handle complex scenarios, the concept of an Aerial Drone Hub (ADH) has been proposed. This hub not only provides real-time control and data support for drone swarms but also provides energy replenishment, maintenance, monitoring, and efficient scheduling management as needed.

[0208] The main objective of the aerial unmanned aerial vehicle (UAV) central station system is to improve the scheduling and coordination efficiency of UAV swarms to a higher level, while ensuring their stability and autonomy in complex environments.

[0209] Key technical points

[0210] Airborne Flight Control and Command System: The UAV central station uses real-time data analysis, communication and control systems to allocate flight missions, plan routes and coordinate management.

[0211] Energy replenishment and maintenance: By using wireless charging, energy management systems, or supply drones, the drone swarm can be ensured to continuously perform missions, especially during long-duration missions, where aerial drone hubs have a significant advantage.

[0212] Automated drone docking and repair: Utilizing collision docking technology and equipment such as robotic arms, the aerial drone hub can assist in receiving, repairing, loading, and redeploying drones, ensuring mission continuity.

[0213] Autonomous navigation and obstacle avoidance capabilities: The air terminal can autonomously adjust its flight path in complex urban airspace or other special environments to avoid collisions with other aerial objects.

[0214] Mathematical Analysis and Details of the Aerial Unmanned Aerial Vehicle Station

[0215] 1. Flight Management and Path Planning

[0216] A core function of an aerial drone control center is scheduling and flight path management. To ensure efficient coordination among multiple drones in the air, avoid collisions, and react quickly according to mission requirements, flight path optimization algorithms are necessary.

[0217] Path optimization model: Using reinforcement learning algorithms in **dynamic programming (DP) or artificial intelligence (AI)**, the optimal flight route for a group of multiple drones is calculated based on factors such as environmental information, target point, flight speed, and airspace restrictions.

[0218] Suppose the state of the drone swarm is x(t), where x(t) = [x1(t), x2(t), ..., x N (t)] T This represents the position of each drone in the drone swarm. The goal is to optimize the mission objective by controlling the path of each drone.

[0219] Objective function (e.g., shortest path problem):

[0220]

[0221] Among them, v i (t) represents the flight speed of the i-th UAV at time t.

[0222] Using A* or Dijkstra's algorithm, a path is designed for each UAV in the airspace to ensure that all UAVs can complete the task within the specified time and avoid collisions.

[0223] 2. Aerial docking and resupply

[0224] During missions, drones may require periodic battery replacements, data transmission, or mission updates. A key function of the aerial drone hub is to support these tasks. To enable autonomous docking, energy replenishment, and resupply for drones, we utilize the following technologies.

[0225] Collision docking and docking accuracy control:

[0226] When the drone approaches the airborne terminal, it uses a collision-docking and magnetic attraction system for positioning and docking. First, radar or visual sensors are used to monitor the position of the target drone and calculate its relative position to the airborne terminal. Then, the flight path is adjusted to ensure docking accuracy.

[0227] The dynamics of docking can be described using an elastic collision model and magnetic field interactions:

[0228] Elastic collision model:

[0229] At the time of the collision, assuming the contact between the drone and the airborne terminal is rigid and elastic, the momentum conservation equation is:

[0230] m1v1 + m2v2 = m1v ′ 1+m2v ′ 2

[0231] Where v1 and v2 are the velocities of the drone and the airborne terminal before the collision, respectively. ′ 1 and v ′ 2 is the velocity after the collision.

[0232] Magnetic docking:

[0233] The aerial terminal uses magnetic attraction to precisely adjust the docking. The effect of magnetic attraction is given by the following formula:

[0234]

[0235] Where μ0 is the vacuum permeability, m1 and m2 are the magnetic moments of the UAV and the airborne terminal, respectively, and r is the distance between them.

[0236] After the magnetic attraction provides initial docking power, a robotic arm is used for precise positioning to ensure the stability of the docking structure.

[0237] 3. In-flight energy management and automatic charging

[0238] An aerial drone hub not only needs to manage and connect flights, but also must provide charging and resupply services for drones. To achieve this, automatic charging equipment and resupply systems need to be deployed at the hub.

[0239] Wireless charging technology: This technology uses electromagnetic induction or laser charging to wirelessly charge drones in mid-air. By controlling the charging frequency to match the drone's receiving capabilities, it ensures that the drone always has sufficient power during flight.

[0240] Power model for wireless charging:

[0241]

[0242] Among them, P received For the received power, P transmitted For the transmitted power, A receiver Let r be the area of ​​the receiver and r be the transmission distance.

[0243] When the drone connects to the charging station, it adjusts its charging strategy based on real-time power demand and charging rate to ensure that charging is completed in a short time.

[0244] Supply system: The aerial drone terminal can also carry supply drones, which can carry necessary supplies (such as new sensors, batteries, tools, etc.) and dock and exchange supplies with the drones in operation. The supply operation can be completed through precise path planning and control systems, ensuring uninterrupted missions.

[0245] 4. Autonomous obstacle avoidance and flight stability

[0246] The airborne drone headquarters must avoid collisions with other objects (such as buildings, other drones, birds, etc.) during flight, especially in complex aerial environments.

[0247] Real-time obstacle avoidance algorithm: Employs deep learning-based obstacle avoidance algorithms (such as YOLO and PointNet) combined with sensors like LiDAR and cameras to analyze the surrounding environment in real time. When the system detects a potential obstacle, it adjusts its flight path to avoid collisions.

[0248] Attitude Control and Stability: The aerial drone control station must ensure that it maintains a stable attitude throughout flight, avoiding instability caused by external disturbances (such as wind and airflow). By using a PID controller (proportional-integral-derivative controller) or model-based control methods (such as LQR control), the aerial control station can maintain stable flight in dynamic environments.

[0249] Attitude control model:

[0250]

[0251] Where q is the attitude vector, M is the inertia matrix, C is the Coriolis matrix, K is the stiffness matrix, and τ is the input.

[0252] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cooperative flight system based on spherical UAV collision docking, comprising a UAV (1) and a connecting arm mounted thereon, characterized in that: The control module of the UAV (1) senses external force through collision detection to adjust the attitude of the UAV for docking with the connecting arm. The connecting arm is provided with a threaded connector that cooperates with another UAV. The control module controls the UAV to rotate axially around the connecting arm to complete the fixed connection between the UAVs. The connecting arm is a retractable and bendable L-shaped cantilever set at the bottom of the drone. When docking, the cantilever is in a vertically downward state and is connected to the vertically upward cantilever at the bottom of another drone flying inverted by threads. After docking, the other drone is horizontally rolled to form two drones running in parallel, and the bottom is connected by the cantilever bent into an L-shape. The drone (1) is provided with a spherical outer shell (2) and through holes on the outer shell; A magnetic suction component (4) is provided on the spherical shell (2) outside the through hole. The magnetic suction component (4) is set in conjunction with the magnetic suction component outside the through hole of another drone. The initial docking of the drone is completed by magnetic attraction, and then the cantilever is used for fixed connection.

2. The cooperative flight system based on spherical UAV collision docking according to claim 1, characterized in that: The cantilever is horizontally positioned on one side of the UAV (1). With the cantilever as the axis, the UAV is vertically flipped so that the threaded connector on the cantilever is threadedly connected to another UAV.

3. The cooperative flight system based on spherical UAV collision docking according to claim 2, characterized in that: The cantilever is equipped with a threaded connector so that the threaded connector can be matched with a threaded connector on the cantilever of another drone to form a threaded connection.

4. The cooperative flight system based on spherical UAV collision docking according to claim 3, characterized in that: The threaded connector of the cantilever contacts the threaded connector on another UAV through a through hole inside the spherical shell (2), and rotates around the cantilever as an axis to complete the fixed connection.

5. The cooperative flight system based on spherical UAV collision docking according to claim 4, characterized in that: The magnetic suction component (4) is a ring structure surrounding the through hole.

6. The cooperative flight system based on spherical UAV collision docking according to claim 4, characterized in that: During the separation process of the system, the UAV rotates in the opposite direction around the cantilever as the axis to separate the fixed connection between the UAVs; the UAVs fly in opposite directions based on the magnetic connection to peel off the initial docking of the magnetic component (4).

7. The cooperative flight system based on spherical UAV collision docking according to claim 4, characterized in that: During the connection process of the system, after the retractable cantilever is fixedly connected to another drone, it continues to extend to allow the initial docking of the magnetic component (4) to be separated. During the separation process of the system, the drone first rotates in the opposite direction around the cantilever to separate the fixed connection between the drones, and then the retractable cantilever is retracted.

8. The cooperative flight system based on spherical UAV collision docking according to claim 4, characterized in that: During the connection process of the system, the drone (1) and another drone flying in reverse are initially connected through their respective bottom magnetic components (4). After being connected by a threaded connection through a vertical cantilever, the cantilever continues to extend to peel off the initial connection of the magnetic components (4) and cause the other drone to roll horizontally to form two drones running in parallel, with the bottom connected by a cantilever bent into an L shape. During the separation process of the system, another drone is made to roll vertically, directly separating the fixed connection between the L-shaped cantilever arms.

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