Micro-miniature multi-rotor unmanned aerial vehicle cluster cooperative control system

Abstract

The invention discloses a micro-miniature multi-rotor unmanned aerial vehicle cluster cooperative control system, and belongs to the technical field of micro-miniature unmanned aerial vehicle cluster cooperative control. The problems that a control system based on an existing platform cannot provide a reliable multi-machine communication function and cannot realize stable large-scale multi-machine cooperative control are solved. According to the invention, the micro-miniature multi-rotor unmanned aerial vehicle cooperation module is utilized to acquire sensor data, the unmanned aerial vehicle pose acquisition unit is utilized to acquire pose data, state data including a sensor and a pose are sent to the expected instruction acquisition unit and the interaction unit, and the interaction unit is utilized to output a control instruction. The expected instruction obtaining unit is used for outputting an expected instruction, the communication module sends a control instruction and the expected instruction to the micro-miniature multi-rotor unmanned aerial vehicle cooperation module, and the micro-miniature multi-rotor unmanned aerial vehicle cooperation module carries out task planning and trajectory planning. The method can be applied to cooperative control of the unmanned aerial vehicle cluster.

Description

Technical Field

[0001] The present invention belongs to the technical field of micro-UAV cluster cooperative control, and specifically relates to a micro-multi-rotor UAV cluster cooperative control system. Background Art

[0002] A drone is an unmanned aircraft that is completely and intermittently autonomously operated by a radio remote control device, a self-contained program control device, or an onboard computer. With the continuous development of drone functions, its advantages such as unmanned, intelligent, and low-cost have enabled drones to show their prowess in many fields. At the same time, the tasks of drones have gradually become more diverse and complex, gradually evolving from low-level use to high-level use, and from single-machine to multi-machine formation collaborative tasks, increasingly reflecting its advantages such as high task completion rate, fast task completion speed, and large amount of information perception. For these collaborative tasks, the autonomous trajectory planning capability and collaborative flight capability of drones are necessary conditions for the drone collaborative control system to complete flight missions.

[0003] The Crazyflie quadcopter platform designed in the literature (Crazyswarm: A large nano-quadcopter swarm [C] / / 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 3299-3304.) is a widely used quadcopter multi-machine collaborative control platform. The platform uses a small and light ultra-light quadcopter drone, and uses a self-developed flight control and communication system to provide a host computer interface for various drone control experiments, which can better meet the experimental needs. However, due to the small size of the drone, the problems such as insufficient power reserve, short endurance, limited large maneuver flight actions, and poor platform ductility have also greatly restricted related experiments, resulting in poor scalability of the platform in collaborative control.

[0004] The FLA-Quad platform designed in the literature (Fast, autonomous flight in GPS-denied and cluttered environments[J].Journal of Field Robotics, 2018, 35(1):101-120.) has rich sensor resources and high computing power, good scalability, and high load capacity. However, the platform is too large in size and mass, with a low thrust-to-weight ratio, and cannot complete large maneuvering flight missions. Indoor flight experiments are also more dangerous. In addition, the platform does not provide reliable multi-machine communication functions, making it difficult to achieve stable large-scale multi-machine collaborative control.

[0005] The Agilicious platform proposed in the literature (Agilicious: Open-source and open-hardware agile quadrotor for vision-based flight [J]. Science robotics, 2022, 7 (67): eabl6259.) adopts a flying drone solution, using a medium-sized frame and combining self-developed flight control and four-in-one electric speed controller to form a flying tower to reduce space occupancy. The platform has high flight maneuverability, and its onboard computer also provides high computing power resources, but the cost of a single machine is too high, which is not suitable for collaborative control experiments that require large-scale deployment, and there is no reliable multi-machine communication function to provide communication guarantee. Summary of the invention

[0006] The purpose of the present invention is to solve the problem that the control system based on the existing platform cannot provide reliable multi-machine communication function and cannot achieve stable large-scale multi-machine collaborative control, and proposes a micro-multi-rotor UAV cluster collaborative control system.

[0007] The technical solution adopted by the present invention to solve the above technical problems is: a micro-multi-rotor UAV cluster collaborative control system, the system includes a micro-multi-rotor UAV collaborative module, a communication module and a multi-functional ground module, and the multi-functional ground module includes a second-generation robot operation unit, a UAV posture acquisition unit, a desired instruction acquisition unit and an interaction unit, wherein:

[0008] The micro-multi-rotor UAV cooperation module is used to obtain sensor data of each micro-multi-rotor UAV and send the obtained sensor data to the communication module;

[0009] The communication module sends the sensor data to the second generation robot operation unit;

[0010] The UAV posture acquisition unit is used to acquire the posture data of each micro-multi-rotor UAV and send the acquired posture data to the second-generation robot operation unit;

[0011] The second generation robot operation unit sends the status data including the drone sensor and posture data to the desired instruction acquisition unit and the interaction unit;

[0012] The interaction unit is used to output control instructions according to the received drone status data, and send the control instructions to the second-generation robot operation unit;

[0013] The expected instruction acquisition unit is used to output the expected instruction according to the received drone status data, and send the expected instruction to the second-generation robot operation unit;

[0014] The second-generation robot operation unit sends the control instructions and expected instructions to the communication module, and the communication module sends the control instructions and expected instructions to the micro-multi-rotor drone cooperation module;

[0015] The micro multi-rotor UAV collaborative module is used to perform mission planning and trajectory planning based on the received control instructions, expected instructions and the communication relationship between each micro multi-rotor UAV.

[0016] Furthermore, the micro multi-rotor UAV is designed with a PCB integrated fuselage, a composite PLA material is added to the preparation material of the fuselage, a soft rubber is arranged between the motor and the sensor, and the driving system of the motor is based on a single-channel lower bridge arm design.

[0017] Furthermore, the micro-multi-rotor drone is provided with an IMU, a compass, an air pressure sensor, a laser altitude sensor and an optical flow sensor.

[0018] Furthermore, the micro-multi-rotor UAV collaborative module performs mission planning and trajectory planning based on the UAV flight control core, and the UAV flight control core uses STM32F405 as a processor.

[0019] Furthermore, the micro-multi-rotor UAV is also provided with a memory, which includes an FM25V01FRAM memory and a MKDV4GCL SD NAND memory, and the FM25V01 FRAM memory and the MKDV4GCL SD NAND memory are used to store UAV flight parameters, flight logs and custom script files;

[0020] The drone flight control core, memory and sensor data communicate with the communication module using I2C or SPI bus.

[0021] Furthermore, the power supply part of the micro-multi-rotor UAV adopts a multi-level management solution, that is, it supports wide voltage input and staggered separation of power domains in different intervals.

[0022] Furthermore, the communication module includes a sky terminal, a ground terminal and a routing terminal;

[0023] The air terminal is used to receive the sensor data of the multi-rotor drone and broadcast the sensor data to the specified port through UDP, and then complete the communication between the air terminal and the ground terminal through the IoT device;

[0024] The sensor data is sent to the second-generation robot operation unit through the ground terminal;

[0025] The router is used to manage the LAN connection of the multi-rotor drone and the multi-function ground module.

[0026] Furthermore, the working process of the drone posture acquisition unit is as follows:

[0027] Take any miniature multi-rotor drone as an example

[0028] Step 1: The initial position of the micro-multi-rotor UAV is recorded as t0, and the initial arrangement of the identification points of the micro-multi-rotor UAV is recorded as P = {p1, p2, p3, ...., p n}, where p1,p2,p3,....,p n is the position of each identification point on the micro-multi-rotor UAV, n is the number of identification points, and the iterative closest point algorithm is used to determine the initial position R0 of the micro-multi-rotor UAV in combination with t0 and P;

[0029] Step 2: Take the image at the initial position as the first frame image. After the micro-multirotor drone moves, the arrangement of the identification points in the second frame image is recorded as Q = {q1, q2, q3, ...., q n},q1,q2,q3,....,q n is the position of each mark point on the micro-multi-rotor drone in the second frame image, according to P = {p1, p2, p3, ...., p n} and Q={q1,q2,q3,....,q n} Calculate the rotation matrix R and translation matrix t of the micro-rotor drone in the second frame image, which is equivalent to the micro-rotor drone in the first frame image;

[0030] Step 3: Obtain the pose of the micro-multirotor drone in the second frame image according to the initial pose R0, the rotation matrix R and the translation matrix t.

[0031] Furthermore, the specific process of step 2 is as follows:

[0032] Step 21: Establish the objective function Among them, ||·|| is the L2 norm;

[0033] Step 22, calculate the centroid of P and Q respectively:

[0034]

[0035] Among them, μ p is the center of mass of P, μ q is the center of mass of Q;

[0036] Step 23: Calculate the relationship between each element in P and the centroid μ p The difference between each element in Q and the centroid μ is calculated separately. q The difference:

[0037] P'={p1-μ p ,p2-μp ,p3-μ p ,…,p n -μ p}={p1',p'2,p'3,…,p' n}

[0038] Q'={q1-μ q ,q2-μ q ,q3-μ q ,…,q n -μ q}={q1',q'2,q'3,…,q' n}

[0039] The objective function of step 21 is simplified according to P' and Q':

[0040]

[0041] Defining the Matrix U is the left singular matrix obtained by singular value decomposition of W, V is the right singular matrix obtained by singular value decomposition of W, Λ is a diagonal matrix composed of singular values, and the superscript T represents the transpose of the matrix;

[0042] Find:

[0043] R * =UV T

[0044] R * That is the calculated rotation matrix R, and then calculate t based on R * :

[0045] t * =μ q -Rμ p

[0046] t * This is the calculated translation matrix t.

[0047] Furthermore, the mission planning and trajectory planning are described as follows:

[0048] min f(p)

[0049]

[0050] Among them, f(p) is the objective function, A eq and b eq is an equality constraint, A ieq and b ieq is an inequality constraint, and p is the motion trajectory of the UAV.

[0051] The beneficial effects of the present invention are:

[0052] The present invention designs the structure and circuit of the multi-rotor unmanned aerial vehicle monomer, and proposes a design scheme of small size, low weight and long endurance to meet the requirements of cooperative control of multi-rotor unmanned aerial vehicles. The multifunctional ground module is used to realize the global positioning / attitude determination algorithm, provide data interface for centralized / distributed task planning, motion planning and cluster control, and has functions such as human-computer interaction and virtual reality interaction; the distributed cooperative control / planning method is the core of the system implementation, the planning method is generally embedded in the expected instruction acquisition module, and the control method can be deployed in the flight control software of the multi-rotor unmanned aerial vehicle cooperative module and the interactive unit of the multi-rotor ground module to realize the motion control of the multi-rotor unmanned aerial vehicle. The independently designed large-scale adaptive communication software is used for unmanned aerial vehicle cluster and communication work, completes the uplink and downlink data interaction between the multi-functional ground module and the multi-rotor unmanned aerial vehicle, and the transmission is stable and the delay is short; the multi-rotor unmanned aerial vehicle flight control software realizes the functions of multi-rotor unmanned aerial vehicle platform software communication, multi-sensor fusion navigation, simple task execution, high-performance position / attitude control and data storage, and ensures that the unmanned aerial vehicle system can run stably and smoothly execute the instructions of the multi-functional ground module.

[0053] The micro-multi-rotor cooperative control system designed by the present invention has better and more stable control performance and lower cost, and is more convenient to use. The system has a rich cooperative control, navigation, and planning interface, and the system of the present invention can be used to quickly verify the deployed cooperative algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is an architectural diagram of a micro-multi-rotor UAV cluster collaborative control system of the present invention;

[0055] Figure 2 It is a task planning effect diagram;

[0056] Figure 3 It is the communication software development flow chart of the sky terminal. DETAILED DESCRIPTION

[0057] Specific implementation method 1: Combination Figure 1 The present embodiment is described as a micro-multi-rotor drone cluster collaborative control system. The traditional micro-multi-rotor drone refers to a multi-rotor drone with an empty weight of less than 250g, while the micro-multi-rotor drone designed by the present invention has a total weight of less than 70g. The system includes a micro-multi-rotor drone collaborative module, a communication module and a multi-functional ground module, and the multi-functional ground module includes a second-generation robot operation unit, a drone posture acquisition unit, a desired instruction acquisition unit and an interaction unit, wherein:

[0058] The micro-multi-rotor UAV cooperation module is used to obtain sensor data of each micro-multi-rotor UAV and send the obtained sensor data to the communication module;

[0059] The communication module sends the sensor data to the second generation robot operation unit;

[0060] The UAV posture acquisition unit is used to acquire the posture data of each micro-multi-rotor UAV and send the acquired posture data to the second-generation robot operation unit;

[0061] The second generation robot operation unit sends the status data including the drone sensor and posture data to the desired instruction acquisition unit and the interaction unit;

[0062] The interaction unit is used to output control instructions according to the received drone status data (the task / control instructions are given by the operator, or the task / control instructions are automatically generated according to the rules set by the task), and send the control instructions to the second-generation robot operation unit;

[0063] The expected instruction acquisition unit is used to output the expected instruction according to the received drone state data, and send the expected instruction to the second-generation robot operation unit, where the expected instruction includes the expected trajectory, the expected task, and the expected constraint (such as the distance from other drones);

[0064] The second-generation robot operation unit sends the control instructions and expected instructions to the communication module, and the communication module sends the control instructions and expected instructions to the micro-multi-rotor drone cooperation module;

[0065] The micro multi-rotor UAV collaborative module is used to perform mission planning and trajectory planning based on the received control instructions, expected instructions and the communication relationship between each micro multi-rotor UAV.

[0066] A modular communication method is designed between drones in the present invention, and the communication relationship between drones is expressed in the form of non-intersecting matrices. The positive elements in the matrix can obtain the status data of the corresponding ID individual to realize virtual drone interaction and provide guarantee for the implementation of centralized / distributed algorithms.

[0067] Taking 4 multi-rotor drones as an example, if a unified description method is used, the interaction matrix of the system is in the following form, indicating that drone 1 can obtain information from drones 2 and 3, drone 2 can obtain information from drones 1 and 3, drone 3 can obtain information from drones 1 and 4, and drone 4 can obtain information from drones 1 and 2.

[0068]

[0069] If a fully distributed description method is used, the interaction matrix of UAV 1 is: [0 1 1 0], which indicates that UAV 1 can obtain information about UAVs 2 and 3, but cannot obtain information about UAV 4. Similarly, other UAVs in the system also use this distributed representation.

[0070] Specific implementation method two: This implementation method is different from specific implementation method one in that the micro multi-rotor UAV is designed with a PCB integrated fuselage, which makes the fuselage structure have the characteristics of a small wheelbase. Composite PLA (polylactic acid) material is added to the preparation material of the fuselage, and soft rubber is arranged between the motor and the sensor. The drive system of the motor is based on a single-channel lower bridge arm design.

[0071] The other steps and parameters are the same as those in the first embodiment.

[0072] By designing a PCB integrated fuselage, the size and weight of the drone can be reduced, simplifying the difficulty of actuator installation. By adding composite PLA materials, the fuselage strength can be improved and the probability of circuit board damage can be reduced. By setting soft rubber, the impact of high-frequency oscillations generated by the motor running on various sensors can be reduced. The designed drive system can reduce the overall weight of the drone.

[0073] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that the micro multi-rotor drone is provided with an IMU (inertial measurement unit), a compass, an air pressure sensor, a laser altitude sensor and an optical flow sensor.

[0074] The other steps and parameters are the same as those in the first or second embodiment.

[0075] The IMU uses dual ICM42688 to increase the redundancy of the system, the compass uses QMC5883, and the air pressure sensor uses MS5611.

[0076] Specific implementation method 4: This implementation method is different from any one of specific implementation methods 1 to 3 in that the micro-multi-rotor UAV collaborative module performs mission planning and trajectory planning based on the UAV flight control core, and the UAV flight control core uses STM32F405 as the core processor.

[0077] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0078] The present invention performs multi-machine collaborative trajectory planning based on an unconstrained optimization algorithm, and generates a trajectory by converting trajectory planning into an unconstrained optimization problem and solving it. In view of the need for formation obstacle avoidance, the various conditions and indicators required for generating trajectories are converted into the idea of ​​solving the optimization algorithm. Combining the concept of differential flatness and actual flight requirements, an unconstrained optimization objective function for solving the problem of multi-UAV collaborative trajectory planning is established, and the multi-UAV collaborative trajectory planning program constructed by the objective function is applied to a micro-multi-rotor collaborative control platform to achieve collaborative formation obstacle avoidance flight of multiple UAVs. At the same time, the control interfaces such as UAV position, speed, acceleration, angle, angular velocity, motor speed, positioning and navigation interfaces such as EKF, CKF, UKF, and task interfaces such as path tracking, perimeter patrol, and regional exploration are standardized and expressed in the form of topics, nodes, and plug-ins of the second-generation robot operating system to meet the corresponding experimental requirements.

[0079] Specific implementation method five: This implementation method is different from any one of specific implementation methods one to four in that the micro-multi-rotor drone is also provided with a memory, which includes an FM25V01 FRAM memory and a MKDV4GCL SD NAND memory, and the FM25V01 FRAM memory and the MKDV4GCL SD NAND memory are used to store drone flight parameters, flight logs and custom script files;

[0080] The drone flight control core, memory and sensor data communicate with the communication module using I2C or SPI bus.

[0081] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0082] Specific implementation method six: This implementation method is different from any one of specific implementation methods one to five in that the power supply part of the micro-multi-rotor drone adopts a multi-level management solution, that is, it supports wide voltage input (voltage range is 6V-16.8V) and staggers and separates power domains in different intervals.

[0083] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0084] The solution of this implementation can achieve stable and reliable operation of the power supply system, and even if part of the power supply system is damaged, the system can still operate.

[0085] Specific implementation method 7: This implementation method is different from any one of specific implementation methods 1 to 6 in that the communication module includes a sky terminal, a ground terminal and a routing terminal;

[0086] The air terminal is used to receive the sensor data of the multi-rotor drone and broadcast the sensor data to the specified port through UDP, and then complete the communication between the air terminal and the ground terminal through the IoT device;

[0087] The sensor data is sent to the second-generation robot operation unit through the ground terminal;

[0088] The router is used to manage the LAN connection of the multi-rotor drone and the multi-function ground module.

[0089] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0090] The sky end uses the ESP32C6FH4 chip as the main control chip, and the RF circuit part uses π-type matching impedance, so that it can still maintain a stable connection to the WLAN network at a distance of more than 100 meters. The communication software of the sky end is developed based on ESP32-PICO-D4 and IoT Web Config. The communication software development process is as follows: Figure 3 shown.

[0091] The maximum transmission delay for data interaction between the air terminal and the ground terminal is less than 15ms, and the average time delay is less than 10ms, which is nearly 3 times faster than the current general WLAN network communication rate. The ground terminal communication software is developed based on DroneKit, and has the interactive function of transmitting necessary data packets according to the configuration, reducing the amount of data sent, further reducing the communication time delay, realizing the interaction of drone posture data, optimizing the control effect, and significantly reducing the communication load.

[0092] The integrated router on the routing end manages the LAN connection of the cluster multi-rotor drones and the multi-function ground module by modifying and adapting the compiled OpenWRT firmware, binds a fixed IP address to each drone according to its MAC address, and completes data exchange. The communication module can still maintain low time delay and very high stability in the state of large-scale multi-rotor drones, meeting the needs of large-scale multi-machine collaborative control experiments.

[0093] The communication module supports the forwarding of real-time images and high-frequency remote control signals. All data can be transmitted through the communication module. The maximum data transmission rate of a single machine can reach 30Mbps. Within 200 drones, 5Mbps data transmission can be guaranteed for each drone.

[0094] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that the working process of the drone posture acquisition unit is as follows:

[0095] Take any miniature multi-rotor drone as an example

[0096] Step 1: The initial position of the micro-multi-rotor UAV is recorded as t0, and the initial arrangement of the identification points of the micro-multi-rotor UAV is recorded as P = {p1, p2, p3, ...., p n}, where p1,p2,p3,....,p n is the position of each identification point on the micro-multi-rotor UAV, n is the number of identification points, and the iterative closest point (ICP) algorithm is used to determine the initial position R0 of the micro-multi-rotor UAV in combination with t0 and P;

[0097] Step 2: Take the image at the initial position as the first frame image. After the micro-multirotor drone moves, the arrangement of the identification points in the second frame image is recorded as Q = {q1, q2, q3, ...., q n},q1,q2,q3,....,q n is the position of each mark point on the micro-multi-rotor drone in the second frame image, according to P = {p1, p2, p3, ...., p n} and Q={q1,q2,q3,....,q n} Calculate the rotation matrix R and translation matrix t of the micro-multi-rotor drone in the second frame image, which is equivalent to the micro-multi-rotor drone in the first frame image; the specific calculation process is:

[0098] Step 21: Establish the objective function Among them, ||·|| is the L2 norm;

[0099] Step 22, calculate the centroid of P and Q respectively:

[0100]

[0101] Among them, μ p is the center of mass of P, μ q is the center of mass of Q;

[0102] Step 23: Calculate the relationship between each element in P and the centroid μ p The difference between each element in Q and the centroid μ is calculated separately. q The difference:

[0103] P'={p1-μ p ,p2-μ p ,p3-μ p ,…,p n -μ p}={p1',p'2,p'3,…,p' n}

[0104] Q'={q1-μ q ,q2-μ q ,q3-μ q ,…,q n -μ q}={q1',q'2,q'3,…,q'n}

[0105] The objective function of step 21 is simplified according to P' and Q':

[0106]

[0107] Defining the Matrix U is the left singular matrix obtained by singular value decomposition of W, V is the right singular matrix obtained by singular value decomposition of W, Λ is a diagonal matrix composed of singular values, and the superscript T represents the transpose of the matrix;

[0108] When the matrix W is full rank, we get:

[0109] R * =UV T

[0110] R * That is the calculated rotation matrix R, and then calculate t based on R * :

[0111] t * =μ q -Rμ p

[0112] t * That is the calculated translation matrix t;

[0113] Step 3: Obtain the pose of the micro-multirotor drone in the second frame image according to the initial pose R0, the rotation matrix R and the translation matrix t.

[0114] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0115] Similarly, the position and posture of the drone in the third frame can be obtained based on the second frame image, and so on. The method of this embodiment can be used to obtain the position and posture data of the drone in each frame image, and the method of this embodiment can be executed for each drone. Moreover, the method of this embodiment avoids the defect of the traditional motion capture system to identify different drone individuals by identifying different arrangements of identification points on different objects, and can achieve the effect of large-scale drone group positioning, while reducing the complexity of drone position and posture calculation.

[0116] Specific implementation method 9: This implementation method is different from any one of specific implementation methods 1 to 8 in that the task planning and trajectory planning are described as follows:

[0117] min f(p)

[0118]

[0119] Among them, f(p) is the objective function, Aeq and b eq is an equality constraint, A ieq and b ieq is an inequality constraint, and p is the motion trajectory of the UAV.

[0120] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0121] The objective function f(p) represents the planning trajectory consumption, which is generally the total time, the Euclidean distance from the specified location, the energy consumption, etc. By finding the parameters that minimize the objective function f(p), the optimal trajectory under this indicator can be calculated. However, for the planning problem of large-scale UAV systems, it is also necessary to consider the relative position constraints between UAVs, avoid collisions, and maintain the specified formation, etc. Therefore, A and B are set separately. eq , b eq and A ieq , b ieq The equality and inequality constraints represented by , the equality constraints are the intermediate path points, the inequality constraints are obstacles and kinematic constraints, taking the four-UAV formation task as an example, the planning effect is as follows Figure 2 shown.

[0122] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A micro-scale multi-rotor drone cluster collaborative control system, characterized in that: The system includes a micro-multi-rotor UAV cooperation module, a communication module and a multi-functional ground module, and the multi-functional ground module includes a second-generation robot operation unit, a UAV posture acquisition unit, a desired instruction acquisition unit and an interaction unit, wherein: The micro-multi-rotor UAV cooperation module is used to obtain sensor data of each micro-multi-rotor UAV and send the obtained sensor data to the communication module; The communication module sends the sensor data to the second generation robot operation unit; The UAV posture acquisition unit is used to acquire the posture data of each micro-multi-rotor UAV and send the acquired posture data to the second-generation robot operation unit; The second generation robot operation unit sends the status data including the drone sensor and posture data to the desired instruction acquisition unit and the interaction unit; The interaction unit is used to output control instructions according to the received drone status data, and send the control instructions to the second-generation robot operation unit; The expected instruction acquisition unit is used to output the expected instruction according to the received drone status data, and send the expected instruction to the second-generation robot operation unit; The second-generation robot operation unit sends the control instructions and expected instructions to the communication module, and the communication module sends the control instructions and expected instructions to the micro-multi-rotor drone cooperation module; The micro multi-rotor UAV collaborative module is used to perform mission planning and trajectory planning based on the received control instructions, expected instructions and the communication relationship between each micro multi-rotor UAV.

2. A micro-sized multi-rotor drone cluster collaborative control system according to claim 1, characterized in that: The micro multi-rotor UAV is designed with a PCB integrated fuselage, a composite PLA material is added to the preparation material of the fuselage, a soft rubber is arranged between the motor and the sensor, and the driving system of the motor is based on a single-channel lower bridge arm design.

3. A micro-multi-rotor UAV cluster collaborative control system according to claim 2, characterized in that: The micro-sized multi-rotor UAV is provided with an IMU, a compass, an air pressure sensor, a laser altitude sensor and an optical flow sensor.

4. A micro-multi-rotor UAV cluster collaborative control system according to claim 3, characterized in that: The micro multi-rotor UAV collaborative module performs mission planning and trajectory planning based on the UAV flight control core, and the UAV flight control core uses STM32F405 as a processor.

5. A micro-multi-rotor UAV cluster collaborative control system according to claim 4, characterized in that: The micro-multi-rotor UAV is also provided with a memory, which includes an FM25V01 FRAM memory and a MKDV4GCL SD NAND memory, and the FM25V01 FRAM memory and the MKDV4GCL SD NAND memory are used to store UAV flight parameters, flight logs and custom script files; The drone flight control core, memory and sensor data communicate with the communication module using I2C or SPI bus.

6. A micro-multi-rotor UAV cluster collaborative control system according to claim 5, characterized in that: The power supply part of the micro multi-rotor UAV adopts a multi-level management solution, that is, it supports wide voltage input and staggered separation of power domains in different intervals.

7. A micro-multi-rotor UAV cluster collaborative control system according to claim 6, characterized in that: The communication module includes a sky terminal, a ground terminal and a routing terminal; The air terminal is used to receive the sensor data of the multi-rotor drone and broadcast the sensor data to the specified port through UDP, and then complete the communication between the air terminal and the ground terminal through the IoT device; The sensor data is sent to the second-generation robot operation unit through the ground terminal; The router is used to manage the LAN connection of the multi-rotor drone and the multi-function ground module.

8. A micro-sized multi-rotor drone cluster collaborative control system according to claim 7, characterized in that: The working process of the UAV posture acquisition unit is as follows: Take any miniature multi-rotor drone as an example Step 1: The initial position of the micro-multi-rotor UAV is recorded as t0, and the initial arrangement of the identification points of the micro-multi-rotor UAV is recorded as P = {p1, p2, p3, ...., p n }, where p1,p2,p3,....,p n is the position of each identification point on the micro-multi-rotor UAV, n is the number of identification points, and the iterative closest point algorithm is used to determine the initial position R0 of the micro-multi-rotor UAV in combination with t0 and P; Step 2: Take the image at the initial position as the first frame image. After the micro-multirotor drone moves, the arrangement of the identification points in the second frame image is recorded as Q = {q1, q2, q3, ...., q n },q1,q2,q3,....,q n is the position of each mark point on the micro-multi-rotor drone in the second frame image, according to P = {p1, p2, p3, ...., p n } and Q={q1,q2,q3,....,q n } Calculate the rotation matrix R and translation matrix t of the micro-rotor drone in the second frame image, which is equivalent to the micro-rotor drone in the first frame image; Step 3: Obtain the pose of the micro-multirotor drone in the second frame image according to the initial pose R0, the rotation matrix R and the translation matrix t.

9. A micro-multi-rotor UAV cluster collaborative control system according to claim 8, characterized in that: The specific process of step 2 is: Step 21: Establish the objective function Among them, ||·|| is the L2 norm; Step 22, calculate the centroid of P and Q respectively: Among them, μ p is the center of mass of P, μ q is the center of mass of Q; Step 23: Calculate the relationship between each element in P and the centroid μ p The difference between each element in Q and the centroid μ is calculated separately. q The difference: P'={p1-μ p ,p2-m p ,p3-m p ,…,p n -m p }={p′1,p'2,p'3,…,p' n } Q'={q1-μ q ,q2-μ q ,q3-μ q ,…,q n -μ q }={q′1,q'2,q'3,…,q' n } The objective function of step 21 is simplified according to P' and Q': Defining the Matrix U is the left singular matrix obtained by singular value decomposition of W, V is the right singular matrix obtained by singular value decomposition of W, Λ is a diagonal matrix composed of singular values, and the superscript T represents the transpose of the matrix; Find: R*=UV T R* is the calculated rotation matrix R, and t* is calculated based on R: t * =μ q -Rμ p t * This is the calculated translation matrix t.

10. A micro-multi-rotor UAV cluster collaborative control system according to claim 9, characterized in that: The mission planning and trajectory planning are described as: min f(p) Among them, f(p) is the objective function, A eq and b eq is an equality constraint, A ieq and b ieq is an inequality constraint, and p is the motion trajectory of the UAV.

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