441 results about "Flight formation" patented technology

The invention discloses a multi-unmanned aerial vehicle combined flight control system coping with wind power change, and relates to unmanned aerial vehicle flight control, and the system comprises a flight data collection module which is used for collecting unmanned aerial vehicle flight data in real time, and the unmanned aerial vehicle flight data comprises wind power data, attitude angle data and height data of each unmanned aerial vehicle; the data receiving and processing module is electrically connected with the flight data acquisition module, and the data receiving and processing module is used for receiving unmanned aerial vehicle flight data acquired in real time and performing noise removal on the acquired data. According to the multi-unmanned aerial vehicle combined flight control system provided by the invention, the wind field prediction model is constructed by combining the LSTM neural network with the time attention mechanism, wind power periodic change characteristics are accurately captured, real-time correction is performed in cooperation with the Kalman filter, the prediction precision and timeliness are both optimized, and the system is suitable for large-scale popularization and application. A multi-unmanned aerial vehicle cooperative kinetic model is constructed based on a prediction result, so that the formation trajectory tracking error is reduced.

The invention discloses a multi-aircraft cooperative formation flight path planning method based on a leader and follower model, and relates to the technical field of environment perception and unmanned aerial vehicle cluster cooperation. The method comprises the following steps of: firstly, designing a multi-agent double delay depth deterministic strategy gradient (LFMATD3) based on a leader-follower model, and converting an optimization problem model into a Markov decision process model by introducing an artificial potential field model and a reward function; and secondly, constructing an independent agent for each unmanned aerial vehicle, optimizing a behavior strategy of the unmanned aerial vehicle by combining a reward function based on an algorithm framework of deep reinforcement learning, and performing flight path planning and realizing dynamic formation control. The method provided by the invention can effectively improve the formation stability and collaboration of the unmanned aerial vehicles in a complex environment.

The invention discloses a multi-aircraft distributed decision-making swarm route avoidance cooperative control method and system, relates to the technical field of unmanned aerial vehicle formation control, and is used for multi-aircraft flight formation. Each unmanned aerial vehicle is provided with an intelligent decision-making module with environment sensing, communication and path planning capabilities; the method comprises the steps that each unmanned aerial vehicle generates a preliminary avoidance path by adopting an improved genetic algorithm based on acquired local environment information and received state information of neighborhood unmanned aerial vehicles in combination with a preset avoidance rule and a preset multi-objective optimization function; the optimization objectives of the preset multi-objective optimization function comprise an obstacle avoidance safety distance, an energy consumption coefficient, a formation retention degree and task timeliness; space-time conflict detection is executed based on the multiple preliminary avoidance paths, the preliminary avoidance paths are optimized based on conflict detection results, a final conflict-free optimal path is obtained and broadcasted to other unmanned aerial vehicles in the formation, and distributed collaborative decision making is achieved; according to the invention, the control efficiency of multi-unmanned aerial vehicle formation control is improved.

The invention provides an unmanned aerial vehicle group formation cooperation and task allocation optimization method and system, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: obtaining the flight state and task area information of an unmanned aerial vehicle, dividing subtasks, setting initial allocation parameters, building an information interaction mechanism based on communication weight, and carrying out the task allocation. Minimizing the total task execution cost to obtain an optimal allocation scheme; calculating an expected route to generate an initial formation position; collecting environmental parameters to calculate an optimal distance; dynamically adjusting a relative position and compensating pneumatic interference;

The invention discloses a multi-unmanned-aerial-vehicle high-speed dynamic flight formation control method, system and device, and relates to the technical field of multi-unmanned-aerial-vehicle cooperative formation control, and the method comprises the steps: analyzing a multi-unmanned-aerial-vehicle formation, setting a reasonable flight safety distance, and constructing a formation topological structure through an adjacent matrix and an offset; a distributed control model combining leader target driving and follower cooperation is designed, and global navigation and local formation keeping are achieved; setting a kinematics constraint function and a dynamic formation transformation mechanism to ensure that the unmanned aerial vehicle formation stably and safely advances and has an adaptive formation adjustment capability; according to the improved artificial potential field method, various resultant forces are considered, and virtual edge traction force is introduced, so that the local minimum problem of the traditional artificial potential field method is effectively solved, and the obstacle avoidance reliability of the system is enhanced.

The invention relates to the technical field of data processing, and discloses a multi-intelligence interactive cooperative unmanned aerial vehicle formation control method and system, and the method comprises the steps: obtaining real-time data through an unmanned aerial vehicle carrying a multimode sensor, extracting the multi-scale space correlation characteristics of the real-time data, carrying out the dynamic role distribution of the unmanned aerial vehicle, and obtaining an unmanned aerial vehicle formation; establishing a formation topology constraint relation, and planning a task path based on the formation topology constraint relation; predicting a flight path, constructing a state transition covariance matrix based on the predicted flight path and the real-time data, and performing path correction on the unmanned aerial vehicle formation to obtain a nonlinear obstacle avoidance path; establishing an anomaly reconstruction scheme, performing abnormal state confrontation training on the unmanned aerial vehicle formation to obtain an anomaly diagnosis confidence coefficient, and performing role reconstruction on the unmanned aerial vehicle formation when the anomaly diagnosis confidence coefficient exceeds a preset anomaly threshold value; and collecting a task path, a nonlinear obstacle avoidance path and an abnormal reconstruction scheme as a control protocol. The control efficiency of the unmanned aerial vehicle formation can be improved.

The invention discloses a multi-agent cooperative control system based on adaptive learning, and belongs to the technical field of multi-agent system control, and the system specifically comprises the steps: collecting and analyzing the wind field data of the position of each agent; distinguishing two typical working conditions of a global uniform wind field and a local disturbance wind field based on a consistency analysis result; for a global uniform wind field, a model prediction control method is adopted to generate a wind field compensation strategy for keeping formation stability; for a local disturbance wind field, an accurate airflow disturbance state is obtained through environment three-dimensional reconstruction and fluid simulation, then the expected track deviation of each agent is predicted, and a collaborative avoidance instruction set is generated in combination with a relative position relationship. According to the method, adaptive cooperative control of multiple agents under different wind field working conditions is realized, the hidden conflict problem caused by local wind field disturbance is effectively solved, and the flight safety and task reliability of an agent cluster in a complex environment are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle cooperative control, and discloses a multi-unmanned aerial vehicle cooperative control method based on data link networking. The method comprises the following steps of: constructing a cluster collaborative task data set comprising a flight path feature, a load state feature and a communication link feature; extracting dynamic cooperative nodes, including a cluster formation form switching node and a task target switching node, from the cluster formation form switching node and the task target switching node; generating a track cooperative trigger signal based on the dynamic cooperative node, and identifying a local topology adjustment node triggered by the formation form switching and a global topology reconstruction node triggered by the task target switching; generating a cooperative offset parameter containing a track offset and a communication delay offset according to the node classification; extracting cluster collaboration protocol parameters based on the collaboration offset parameters; and controlling the unmanned aerial vehicle cluster to execute a cooperative flight task according to the protocol parameters. The method can improve the cooperative efficiency and task adaptability of the unmanned aerial vehicle group in a dynamic environment, and is suitable for various multi-vehicle cooperative operation scenes.

The invention relates to an unmanned aerial vehicle control system and method for formation flight in a wind environment. The method comprises the following steps: generating a wind disturbance environment with a specified map scale and containing random obstacles; acquiring external image data and inertial measurement data of the unmanned aerial vehicle, and generating a grid map through point cloud extraction and conversion operation; the method comprises the following steps: modeling a spatial topological structure of an unmanned aerial vehicle cluster by adopting an undirected graph mode, and solving and generating a collision-free initial trajectory by adopting an unconstrained discrete optimization mode; optimizing the initial trajectory by adopting a task redistribution strategy, and re-planning a global target and a local target to obtain a re-planned optimized trajectory; and based on the optimized trajectory, solving by adopting a model prediction control mode to obtain an optimal control quantity for unmanned aerial vehicle bottom layer control so as to correspondingly control the motion state of each unmanned aerial vehicle and realize formation flight. Compared with the prior art, the unmanned aerial vehicle cluster can efficiently and accurately realize formation flight in an obstacle environment with wind influence.

The invention discloses a ground-to-ground heterogeneous cluster formation control method and system based on an adjustable funnel algorithm, and relates to the technical field of distributed formation maneuvering control, and the method comprises the steps: building a dynamic model of a ground-to-ground heterogeneous cluster, and building an undirected graph representing an interaction topological structure of the ground-to-ground heterogeneous cluster; modeling a distributed safety critical formation maneuvering control problem of the air-ground heterogeneous cluster; a fully distributed dynamic compensator facing each follower and a formation maneuvering controller based on an adjustable funnel algorithm are designed, and then a safety-critical control framework based on a disturbance observer is adopted to construct a composite controller; and acquiring a virtual navigator signal and inputting the virtual navigator signal to the constructed various controllers to obtain an actual formation control signal of each follower, and executing distributed safety-critical formation maneuvering control on the followers. Through a distributed safety critical formation maneuvering strategy based on an adjustable funnel method, collision / obstacle avoidance and disturbance suppression of an air-ground heterogeneous cluster can be realized.

The invention relates to the technical field of multi-agent cooperation, provides a multi-agent cooperative unmanned aerial vehicle cluster simulation training method, system and device, and a storage medium, and solves the problems of low penetration success rate and poor formation cooperation efficiency of an unmanned aerial vehicle cluster. The method comprises the following steps: collecting pigeon flock biological motion data, cluster trajectory data when an unmanned aerial vehicle cluster executes a historical penetration task, and magnetic field gradient data of a simulation environment; constructing a three-dimensional path library, and calculating geomagnetic course angle deviation; on the basis of the geomagnetic course angle deviation and the three-dimensional path library, a multi-target collaborative path is generated in combination with a swarm intelligent optimization model; generating a flight control instruction based on the multi-target cooperation path in combination with the real-time state data of the unmanned aerial vehicle cluster; and loading the flight control instruction to a simulation model of the unmanned aerial vehicle cluster, and driving the unmanned aerial vehicle cluster to carry out cooperative defense penetration training in a simulation environment. According to the invention, the penetration success rate and formation cooperation efficiency of the unmanned aerial vehicle cluster in a complex electromagnetic environment are improved.

The invention relates to the field of formation flight control, in particular to a multi-unmanned aerial vehicle cooperative formation obstacle avoidance method based on an improved artificial potential field. Firstly, mutual collision between the unmanned aerial vehicles in the formation is avoided through newly adding anti-collision repulsive force between the unmanned aerial vehicles; secondly, providing a local target point mechanism to prevent the piloting unmanned aerial vehicle from falling into a local minimum trap and further prevent the whole formation from falling into the local minimum trap; then, a dynamic repulsive force fusion strategy is introduced, oscillation of the unmanned aerial vehicle near the obstacle is reduced, and therefore the stability of the whole formation is improved; finally, a hiking optimization algorithm gravity coefficient adaptive controller is designed, and the formation pattern maintaining and recovering precision is improved. According to the invention, formation and obstacle avoidance are optimized, so that rapid formation and obstacle avoidance of multiple unmanned aerial vehicles in a complex environment are realized.

The invention discloses an unmanned aerial vehicle cluster cooperative flight control system and method. The system comprises a task distribution module, a trajectory planning module, a formation control module, a communication module, a state monitoring module and an autonomous navigation and positioning module. The task distribution module performs process control through a consistency algorithm improved by combining an A * algorithm, and executes task distribution; the trajectory planning module performs trajectory planning by constructing and solving a mathematical model and a control model, and determines a flight path; the formation control module enables the unmanned aerial vehicle cluster to fly in a predetermined formation configuration; the communication module is used for realizing real-time information transmission and sharing; the state monitoring module is responsible for monitoring and recording the state of the unmanned aerial vehicle in real time; the autonomous navigation and positioning module is used for providing navigation and positioning information for the unmanned aerial vehicle. According to the invention, through optimizing process control, the efficiency and precision of overall cooperative flight can be improved, and efficient and accurate information exchange between the unmanned aerial vehicles can be ensured.

The invention is suitable for the technical field of multi-unmanned aerial vehicle cooperative control, and provides a multi-quadrotor formation maneuvering flight event triggering cooperative control method and system, and the method comprises the steps: firstly building a kinetic model of a quadrotor unmanned aerial vehicle in a distributed formation system under an inertial coordinate system, designing a multi-quadrotor unmanned aerial vehicle cooperative formation synchronization error, and carrying out the cooperative formation of the quadrotor unmanned aerial vehicle; establishing a preset performance control function to constrain the formation synchronization error, and performing corresponding equivalent error transformation; designing a dynamic event trigger track subsystem controller; designing a robust attitude subsystem controller; and constructing a Lyapunov function, and verifying whether a closed-loop system is stable or not and whether an expected formation effect can be achieved or not. The method can effectively restrain the synchronization error of the multi-quadrotor unmanned aerial vehicle formation system, reduces the error overshoot, improves the coping capability for uncertainty, environmental interference and abrupt trajectory change, and guarantees the stable formation flight of the multi-quadrotor unmanned aerial vehicle system.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle low-altitude collaborative inspection method and device based on data acquisition, electronic equipment and a storage medium, and multi-aerial vehicle collaborative flight and data aggregation are realized by calling a plurality of unmanned aerial vehicles to form a logic formation set and constructing a communication topological structure by using relay nodes. According to the method, the formation is quickly established after task area analysis, parallel path coverage is realized through unified scheduling, and the inspection area in unit time is remarkably increased. In a multi-unmanned-aerial-vehicle cooperative mode, the unmanned aerial vehicles share environment sensing information, the flight path is dynamically adjusted, repeated flight and path conflict are avoided, and resource utilization is further optimized. Therefore, the method can effectively solve the problem of low inspection efficiency in the prior art, and realizes efficient, rapid and complete coverage of a large area.

The invention relates to the technical field of performance lighting equipment, in particular to a double-shaft omni-directional steering unmanned aerial vehicle laser lamp and a control method thereof. The unmanned aerial vehicle laser lamp structure comprises a transmission device and a motor, the unmanned aerial vehicle laser lamp structure can rotate by 360 degrees along the X axis and the Y axis, magnetic fluid sealing rings are adopted for packaging, high waterproofness can be achieved, the unmanned aerial vehicle laser lamp is hung below a four-axis or six-axis unmanned aerial vehicle, a space light beam system capable of freely rotating is formed, all-dimensional irradiation is achieved, and the rich three-dimensional light beam effect is created. In addition, the flight control system of the unmanned aerial vehicle can be compatible with a small unmanned aerial vehicle performance control system, so that the unmanned aerial vehicle can be accurately integrated with other unmanned aerial vehicles and cooperate with formation performance. The visual level of unmanned aerial vehicle light show is improved, compared with traditional unmanned aerial vehicle laser equipment, the system can flexibly adjust the height and the angle, the defect that existing unmanned aerial vehicle air show lacks space light beams is effectively overcome, and a more dynamic and immersive light and shadow effect can be constructed in the air in combination with fine control.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle cluster formation cooperative control method, system and device and a medium. The method comprises the following steps: establishing a dynamic model of each unmanned aerial vehicle in an unmanned aerial vehicle cluster and a dynamic model of an obstacle; establishing an information exchange topology model for describing a data interaction path between the unmanned aerial vehicles in the unmanned aerial vehicle cluster; according to each established model, designing a time-varying formation tracking protocol and a collision avoidance controller based on an artificial potential field method; all the unmanned aerial vehicles in the unmanned aerial vehicle cluster are controlled to form a preset formation which dynamically changes according to task requirements in the flight process through a time-varying formation tracking protocol, and a preset flight path which dynamically changes according to the task requirements is tracked; all the unmanned aerial vehicles are attracted to move towards a preset target point through a gravitational field of the collision avoidance controller, and all the unmanned aerial vehicles are repelled to move towards obstacles and each unmanned aerial vehicle is repelled to move towards other unmanned aerial vehicles through a repulsive force field of the collision avoidance controller, so that control over the unmanned aerial vehicle cluster is achieved.

The invention discloses a self-adaptive gesture control unmanned aerial vehicle cluster system and method, and aims to improve the gesture recognition precision and execution efficiency of an unmanned aerial vehicle formation in a complex environment. The system comprises an unmanned aerial vehicle formation unit and a cooperative work communication unit, wherein each unmanned aerial vehicle is equipped with an environment parameter acquisition module, a gesture recognition module, a data fusion module and a flight control module. The system collects multi-dimensional environment data in real time, automatically selects and starts a sensor with the strongest adaptability to collect gesture data, and calculates the comprehensive recognition confidence coefficient based on the sensor adaptability and the recognition confidence coefficient. And the data fusion module is used for generating an optimal control instruction by fusing the data of the multiple unmanned aerial vehicles, and adjusting the flight path, formation, spacing and speed of the unmanned aerial vehicles in the formation according to the optimal instruction. According to the system, the adaptive capacity and the cooperative work efficiency of the unmanned aerial vehicle formation in a complex environment are improved.

The invention discloses a fleet formation flight implementation method and device, equipment, a medium and a product, and belongs to the technical field of aeronautical communication. The method comprises the following steps: establishing an air-ground interconnection communication link to realize real-time data interaction between a ground control unit and a plurality of flying civil aircrafts and between the plurality of civil aircrafts; based on an air-ground interconnection communication link, an airborne ad hoc network with a leading aircraft as a core is constructed, and an information interaction network in the formation is formed. Acquiring formation flight environment data; determining a safe area of formation flight based on the formation flight environment data, and calculating the maximum number of aircrafts in the formation and the relative positions of the aircrafts in the formation; and issuing the maximum number, the relative position and the flight decision information to each airplane in the formation through the airborne ad hoc network so as to execute formation flight. According to the embodiment of the invention, the real-time performance and accuracy of communication between the fleet can be effectively improved, and the airspace operation efficiency and flight safety are improved through the formation flight of the fleet.

The invention discloses an unmanned aerial vehicle formation flight control method and device, an unmanned aerial vehicle and a storage medium. The method comprises the steps that formation parameters, flight task instruction data and flight state data of other unmanned aerial vehicles in a formation are acquired or updated; judging a control mode according to the flight task instruction data, wherein the control mode comprises at least one of the following modes: a normal control enabling and conventional flight mode, a calculation mode, a suspended hovering mode or a disintegration mode; according to the judgment result of the control mode, based on an L1 guide algorithm of self-adaptive track tracking, fusing unmanned aerial vehicle spacing control logic and / or formation control logic of formation dynamic adjustment to obtain a control instruction of an expected track and / or attitude, and executing the control instruction; and circularly executing the steps to ensure the continuity and dynamic adaptability of the unmanned aerial vehicle and the formation flight thereof. According to the scheme, a completely decentralized distributed control architecture is adopted, and a fault-tolerant mechanism is matched, so that the reliability and survivability of the formation system are remarkably improved.

The invention provides a brain-controlled unmanned aerial vehicle group method based on brain-computer deep collaborative fusion. The method comprises the following steps: firstly, designing a steady-state visual evoked brain control signal acquisition normal form, and generating a visual stimulation signal through combined frequency-phase coding; secondly, the electroencephalogram signals are preprocessed through filtering, independent component analysis and the like; then, constructing an intention decoding network with deep fusion of space-time frequency features, extracting multi-scale features of the electroencephalogram signals by using a convolutional neural network, and performing deep fusion decoding through a space-time frequency Transform module to realize accurate prediction of brain control intention; thirdly, a virtual pilot algorithm and an artificial potential field algorithm are used for flight decision making of the unmanned aerial vehicle group; and finally, displaying the flight state of the unmanned aerial vehicle in real time through a visual interface constructed by PyQt5. The method has the advantages that the brain control intention of the user can be accurately decoded, the formation control and obstacle avoidance capability of the unmanned aerial vehicle group is effectively improved, and meanwhile, a visual interface and personalized setting are provided.

The invention provides a large model and reinforcement learning combined hierarchical command control method, which is based on agents of a hierarchical structure, and is characterized in that the first layer of agents are RAG-based large model intelligent formation command agents; the second-layer intelligent agent is composed of an air strike marshalling intelligent agent and a reconnaissance and strike mixed marshalling The second-layer intelligent agent judges which subtask is executed according to the instruction sent by the first-layer intelligent agent; comprising the following steps that S1, an RAG-based large-model intelligent formation command system comprises an offline knowledge preparation stage and an online decision execution stage; s2, setting a state space, an action space and a reward and punishment function; and S3, respectively training the air strike marshalling agent and the reconnaissance and strike mixed marshalling agent, and constructing an intelligent game model based on a multi-agent reinforcement learning algorithm. According to the invention, exclusive knowledge support is provided for the large model through the RAG technology, then the structured decision instruction is generated through the large model, training is carried out based on multi-agent reinforcement learning to obtain the agent of a specific task, and formation-level command control is realized.

The invention relates to an air-ground cooperative cluster time-varying formation control method in a complex environment, belongs to the technical field of unmanned aerial vehicle cluster planning and control, and solves the technical problem that cluster formation cannot give consideration to track smoothness, obstacle avoidance safety and formation stability at the same time. According to the cluster time-varying formation control method of the invention, in consideration of a complex environment of safe and reliable flight of an unmanned aerial vehicle cluster, based on a leader-follower architecture, a safe trajectory of a leader unmanned system is solved, and at the same time, a follower unmanned aerial vehicle elliptical formation shape safety constraint is considered; a continuous track of a leader unmanned system and a safe time-varying formation of each follower unmanned aerial vehicle are obtained at the same time by using a convex optimization method, space-ground cooperative cluster time-varying elliptical formation is realized, a time-varying formation control protocol is obtained, and efficient obstacle avoidance and cooperative flight of an unmanned aerial vehicle cluster in a complex environment are realized.

The invention discloses a fixed-wing unmanned aerial vehicle distributed formation and obstacle avoidance method based on diffusion reinforcement learning, and the method comprises the steps: processing single observation data through a graph attention network, and enabling the single observation data to serve as a condition for generating an optimal action in a reverse denoising process; the method is suitable for distributed cluster control of a fixed-wing unmanned aerial vehicle cluster in an unknown and disordered environment, treats huge challenges brought by complex dynamics and non-integral constraints in the cluster, and effectively solves a series of problems that an existing method is difficult to capture multi-modal action distribution necessary for robust obstacle avoidance under an uncertain condition. A large number of numerical simulation results show that the method is obviously superior to an existing baseline method in the aspects of flight stability and collision rate, the potential of a diffusion model in the aspect of extensible and robust unmanned aerial vehicle flight control is highlighted, and the method has good theoretical popularization value and engineering application prospects.

The invention discloses a pipe gallery type low-altitude flight conflict resolution method and device based on reinforcement learning. The method comprises the following steps: mapping current physical motion state data of a current physical unmanned aerial vehicle into target virtual motion state data of a target virtual unmanned aerial vehicle; in the target simulation platform, determining a current virtual pipe gallery function area of the target virtual unmanned aerial vehicle according to the target virtual motion state data; generating a target virtual unmanned aerial vehicle formation based on the current virtual pipe gallery function area in combination with the target virtual motion state data and virtual formation motion state data corresponding to the initial virtual unmanned aerial vehicle formation; and calculating a virtual formation conflict resolution strategy of the target virtual unmanned aerial vehicle formation based on a reinforcement learning algorithm, and taking the virtual formation conflict resolution strategy as a physical formation conflict resolution strategy to control the current physical unmanned aerial vehicle to fly according to the physical formation conflict resolution strategy. The operation efficiency of the pipe gallery type low-altitude traffic system can be improved, and the method can be widely applied to the technical field of unmanned aerial vehicles.

The invention belongs to the technical field of clock synchronization, and particularly relates to a high-precision clock synchronization method in an aviation cluster high-speed relative motion scene, which comprises the following steps of: S1, acquiring round-trip time RTT data based on signal transmission among nodes in an aviation cluster, and acquiring a relative radial speed v by combining an inertial navigation module; calculating by using a motion correction formula to obtain a preliminary clock skew epsilon after motion correction; in order to solve the problems of asymmetric high-speed motion path time delay, URT interference, random and nonlinear error coexistence and the like, through a four-stage cooperative scheme of RTT motion correction, URT modeling compensation, Kalman filtering smoothing and BP neural network residual optimization, the clock synchronization precision is improved to 0.1-0.3 ns, the method has strong anti-interference robustness and engineering realizability, and the method is suitable for large-scale popularization and application. The method can be widely applied to aerospace, unmanned aerial vehicle formation, satellite communication and other fields requiring high-precision clock synchronization, and effectively solves the problems of insufficient precision and poor adaptability of a traditional method in a high-speed scene.

The present invention relates to a drone formation control method and system based on multi-agent distributed consistency. The method comprises the following steps: allocating coordinates of drones and target points to obtain a target allocation relationship, an alignment scale, and a translation distance; obtaining a relative state based on the alignment scale and the translation distance; obtaining a first trajectory based on the relative state between the drones using a consistency algorithm when obstacle avoidance is not required; calculating the degree of violation of the formation by the drones' obstacle avoidance when the degree of violation is large, replacing the formation and obtaining a second trajectory based on the new formation when the degree of violation is small, planning an obstacle avoidance trajectory for the drones that need to avoid obstacles to obtain a third trajectory; and obtaining a target trajectory based on the first, second, and third trajectories when reaching the end point coordinates. The method implements a drone flight trajectory optimization strategy to balance safety and trajectory executability, thereby significantly improving the drone system's ability to cope with changing environments.

The invention discloses an unmanned aerial vehicle formation communication system based on a LoRa technology and a method thereof. The communication system comprises a ground control unit, a main control processing unit, a sensing array unit, a power supply management unit and a mobile communication unit. The ground control unit generates a formation flight scheme, and the main control processing unit fuses RTK positioning data to generate a dynamic control instruction; the mobile communication unit constructs a radio data transmission module based on a LoRa technology, supports dynamic switching between an ESP-NOW broadcast mode and a TCP transparent transmission mode, and realizes low-delay instruction transmission and low-power-consumption data interaction. The sensing array unit integrates an RTK positioning module and an inertial measurement unit, monitors the state of the unmanned aerial vehicle in real time, and ensures that the formation spacing error is less than 10cm. Through long-distance low-power-consumption communication and power and frequency band dynamic optimization of the LoRa technology, in combination with high-precision positioning and anti-interference spread spectrum modulation, large-scale formation synchronization efficiency and flight stability are remarkably improved, and the method is suitable for scenes such as agriculture, rescue and cluster performance and has the advantages of low cost and high reliability.

The invention discloses a multi-unmanned aerial vehicle integrated planning and control method based on distributed model predictive control. The method comprises the following steps: S1, judging an unmanned aerial vehicle communication topology mechanism in an offline stage, determining an expected target point of each unmanned aerial vehicle, initializing a predictive state value of each unmanned aerial vehicle, and setting controller parameters; s2, the front end performs unmanned aerial vehicle positioning and local map construction according to the sensor data, and searches a reference trajectory according to the local map; s3, the rear end uses an IDMPC controller to solve according to the error between the measured value and the predicted value, and an unmanned aerial vehicle input optimal value is obtained; s4, the solved optimal input is converted into unmanned aerial vehicle attitude control quantity input through differential flatness, and unmanned aerial vehicle formation trajectory tracking is achieved. According to the method, quick response and anti-disturbance capability under the high frequency of 100Hz are realized, and the superiority of the method in the aspects of success rate, calculation efficiency and anti-disturbance performance is verified through a simulation experiment.

The embodiment of the invention provides an aerocar formation control method and system, electronic equipment and a storage medium, and belongs to the technical field of intelligent traffic control. According to the scheme, a plurality of hovercars are linearly formed, the first hovercar in the formation is determined as a head car, the hovercars following the head car in the formation are determined as following cars, and a hovercar formation is obtained; acquiring environment state information and the state quantity of the hovercar; determining a motion model corresponding to the hovercar according to the state quantity; performing path planning according to a preset starting point and an end point to obtain a reference path; based on the reference path, obstacle avoidance planning is carried out through an optimization algorithm according to the environment state information and the motion model, and an obstacle avoidance path is obtained; controlling the hovercar formation to perform dynamic obstacle avoidance according to the obstacle avoidance path; and after avoiding the obstacle, controlling the hovercar formation to return to the reference path. According to the invention, the flight complexity can be reduced, the formation is easy to control and maintain, and the obstacle avoidance safety and the personnel comfort are improved.