350 results about "Flight formation" patented technology

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 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 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 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 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 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 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 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 invention provides a cooperative output control method for a honeycomb type flight aircraft carrier, and belongs to the technical field of honeycomb type flight aircraft carriers. An autonomous aircraft formation initialization matrix is constructed to establish a hexagonal honeycomb type space geometric structure model to determine the relative position relation of all aircrafts; a dynamic load sensing system is used for collecting load gravity center change data in real time through a torque sensor to calculate a dispersed load vector, a cooperative configuration matrix is decomposed into a low-rank matrix combination to recognize a load distribution mode, and a control vector is automatically supplemented through a matrix rank loss detection algorithm to recover a full-rank characteristic. Based on the lightweight dynamic distribution model, the output distribution processing frequency is adaptively adjusted in different intervals according to the dispersed load vector deviation value, the output distribution coefficient of each aircraft is optimized, and the technical problem of low cooperative output control precision caused by unbalanced load distribution in a multi-aircraft formation system is solved.

The invention relates to a multi-source remote sensing cooperative system based on an unmanned aerial vehicle formation and a control method, and the system can synchronously collect laser radar point cloud data, multispectral images, thermal infrared images and hyperspectral images, and breaks through the limitation of single data dimension caused by the fact that a traditional single unmanned aerial vehicle platform can only carry a single sensor. Meanwhile, the formation control module controls the heterogeneous unmanned aerial vehicle cluster to form a preset formation and perform cooperative flight to ensure spatial position cooperation of the unmanned aerial vehicles in the operation process, and the task allocation module dynamically allocates route tasks and sensor working parameters to the unmanned aerial vehicles based on target area environment information. And the data fusion module performs geometric registration, spectrum fusion and three-dimensional reconstruction processing on the multi-source remote sensing data acquired by each unmanned aerial vehicle to generate a three-dimensional spectrogram of the target area, so that accurate integration of the multi-source data in space and spectrum dimensions is realized, and the multi-source remote sensing data is acquired. Therefore, the system gives consideration to large-range area coverage capability and high monitoring precision.

The invention relates to the technical field of urban low-altitude flight control, in particular to a low-altitude aircraft path planning and dynamic adjustment method for urban low-altitude logistics. Constructing a dynamic evaluation model of the flight risk factor R based on the urban three-dimensional environmental parameters; planning the path of the aircraft by adopting a multi-objective optimization algorithm, and determining an optimal path; dynamically adjusting paths by using an environment prediction model and a reinforcement learning algorithm: constructing a conflict graph between paths of each aircraft, and constructing a path collaborative optimization model according to path cost, energy consumption, flight time, path conflict and formation collaborative income; and outputting an optimized path set through the path collaborative optimization model, and generating a formal deployment scheme of multi-aircraft path collaborative scheduling. Compared with an existing static optimization and single path scheduling method, the method has the advantages that the path planning precision, the dynamic response capability, the multi-aircraft cooperation efficiency and the complex airspace adaptability are remarkably improved.

The invention provides a collaborative array position planning method for remote support interference of an electronic warfare aircraft, and belongs to the technical field of shipboard aircraft combat operation. The method comprises the following steps: firstly, in a long-distance support interference scene, determining an interference coverage range of an electronic jammer from two aspects of pitching and orientation; and secondly, calculating the interference effect of the electronic jammer based on the interference unified equation. Thirdly, based on the interference coverage range, designing constraint conditions and a target function, and establishing an electronic jammer collaborative array planning problem model; and finally, based on the electronic jammer collaborative array planning problem model, a genetic algorithm is utilized to solve an electronic jammer optimal array planning scheme of long-distance support interference. The method is suitable for a collaborative array planning problem of electronic warfare aircraft remote support interference, interferes the enemy radar when the jammer formation executes a remote support interference task, reduces the detection threat of the enemy radar to the strike formation, and guarantees the smooth execution of our strike task.

The invention discloses an unmanned aerial vehicle path planning method and device based on reinforcement learning, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: setting the size of a three-dimensional flight environment of an unmanned aerial vehicle formation; acquiring a starting point position and a target point position of a long aircraft set in the unmanned aerial vehicle formation, and determining the number and the positions of obstacles at the same time; determining a simulated three-dimensional flight environment of the unmanned aerial vehicle according to the number and the positions of the obstacles, and discretizing the simulated three-dimensional flight environment of the unmanned aerial vehicle to establish a rasterized grid map; establishing an unmanned aerial vehicle state, a state space and a discretized unmanned aerial vehicle action space in a three-dimensional discretized space according to an unmanned aerial vehicle simulation three-dimensional flight environment; and based on the starting point position and the target point position of the long aircraft, an initial path of the long aircraft is obtained by adopting an IDDQN algorithm, and the initial path is optimized by adopting a Bezier curve to obtain final path information. The problem that in the prior art, the unmanned aerial vehicle path planning mode is low in efficiency is solved.

The invention relates to a multi-unmanned aerial vehicle formation fault-tolerant control method based on an extended state observer. The method comprises the following steps: firstly, establishing a dynamic model of a multi-unmanned aerial vehicle formation; a multi-unmanned aerial vehicle formation communication topological structure is constructed according to the actual demand of a formation task, and information interaction among multiple unmanned aerial vehicles is realized based on a piloting-following method architecture and a distributed control method; establishing a global position tracking error and a speed tracking error by using the states of the adjacent unmanned aerial vehicles; introducing an extended state observer to estimate the unknown state of the formation system in real time; and a sliding-mode adaptive fault-tolerant controller is designed to effectively deal with the influence of actuator faults, system uncertainty and unknown external disturbance on formation performance, and stable control of the multi-unmanned aerial vehicle formation system is realized. According to the method, under the condition that a single unmanned aerial vehicle or multiple unmanned aerial vehicles break down, reliable transmission of task information and effective keeping of the formation can still be guaranteed, and the method has high system robustness and environment adaptability and is suitable for complex flight task scenes.

The invention relates to a target guidance-oriented multi-unmanned aerial vehicle task planning and cooperative positioning method and system, and the method comprises the steps: building an optimized pairing relation between multiple unmanned aerial vehicles and a cluster target through the cooperative scheduling of a multi-unmanned aerial vehicle system and a depth traversal method; optimizing a target guiding area by combining target tracking, laser irradiation and threat avoidance; planning a flight path satisfying flight time, speed and threat avoidance based on an RRT method, superposing a minimum separation distance constraint to ensure the safety of a flight path interval, and optimizing the length of the flight path through a Dubins curve; a positioning model is constructed, the formation with more than three machines uses multi-machine laser ranging information, and the double-machine formation uses ranging and angle measurement information to carry out target positioning; calculating target coordinates by adopting a total least square method; for multiple targets in the photoelectric image, selecting a proper image for positioning according to target distribution, and calculating coordinates by combining collinear positioning and adjustment; according to the invention, the target positioning precision of multiple unmanned aerial vehicles is improved.

The invention relates to the technical field of unmanned aerial vehicle autonomous control, and discloses an unmanned aerial vehicle cluster formation control method and system based on a multi-level intelligent algorithm, and the method comprises the steps: obtaining the external environment monitoring data and motion state data of an unmanned aerial vehicle cluster, constructing a multi-target comprehensive cost function, and carrying out the optimization of a cluster formation initial track, obtaining an optimal trajectory set meeting dynamic constraints; executing collision risk assessment on the sampling points on the optimal trajectory set, and outputting a trajectory safety judgment result; performing mathematical characterization and eigenvalue modulation on the formation topological structure through a Laplacian matrix, and generating target formation parameters adapted to environmental constraints; and finally, converting the optimal track set and the target formation parameters into a bottom layer flight control instruction, driving the unmanned aerial vehicle to execute a flight action, and collecting real-time motion state data at the same time. Therefore, the problems that in the prior art, trajectory optimization convergence is slow, collision detection real-time performance is poor, formation dynamic adaptation is insufficient, and system module collaboration is weak are solved.

The invention discloses an aircraft multi-target performance constraint collaborative optimization method based on multi-dimensional virtual and real parameter mapping, and the method comprises the steps: constructing a real parameter space and a virtual parameter space, and uniformly organizing the task key elements, such as target arrival, obstacle risk, formation maintenance error and minimum safety margin, into multi-dimensional real parameter input; speed scheduling quantity is uniformly organized into virtual parameters to be output, so that consistent parameter expression and a unified decision interface of cross-scene and cross-risk levels are realized, and engineering applicability and reusability in a task constraint scene are improved; interaction response data are obtained in a multi-scene set, a track sample set is established, and PPO reinforcement learning is adopted to train a mapping network from actual parameters to speed virtual parameters, so that the system does not need to explicitly construct complex manual rules or segmented threshold control logic; therefore, self-adaptive tradeoff can be formed among multiple targets such as arrival efficiency, formation keeping and speed stability, so that the manual parameter debugging cost is reduced, and the strategy generalization capability is improved.

The invention discloses an unmanned aerial vehicle cluster elastic formation control method and system and computer equipment, and the method comprises the steps: embedding a local auction mechanism in each unmanned aerial vehicle, calculating a safe neighbor set, and updating bidding through employing a maximum consensus mechanism, achieves the decentralized task distribution between unmanned aerial vehicles, only needs the local communication between neighbors in each auction, and improves the auction efficiency. Global coordination is not needed, and the communication overhead is greatly reduced. On the basis of keeping a communication topology, an artificial potential field method based on a Leader-Follower formation theory is introduced, a small number of leaders guide the whole formation to move through a predetermined track or a task target, and other followers dynamically keep and adjust the formation by using attraction and repulsive force in the artificial potential field. The method is especially suitable for unmanned aerial vehicle cluster formation performance, high-altitude flight tasks and other scenes. The lightweight distributed algorithm developed by using the characteristics of the unmanned cluster provides technical support for a subsequent unmanned cluster formation method.