572 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 provides a medical material distribution method and system based on an offshore medical rescue unmanned aerial vehicle. Wherein a marine environment model is generated; according to the marine environment model, an AI scheduling algorithm is applied to analyze the specific demand, priority and emergency degree of a marine receiving point, and an optimized task sequence and a material loading scheme are obtained; establishing a cooperative communication mechanism between offshore medical rescue unmanned aerial vehicle formations through an AI-driven distributed consensus algorithm, and dynamically adjusting a flight path and generating a distribution path by using an AI path planning technology in combination with real-time situation awareness and obstacle detection analysis; and after arriving at a specified receiving point, automatically calibrating the landing coordinates, and synchronizing the material state, handover information and environmental data to a medical team and a central scheduling system through a low-delay wireless transmission mode according to the distribution path. According to the technical scheme provided by the invention, the efficiency and reliability of offshore material distribution are greatly improved.

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 fixed-wing unmanned aerial vehicle predetermined time cooperative path following formation control method, and relates to an unmanned aerial vehicle technology. A multi-unmanned aerial vehicle cooperative path following control strategy based on preset time stability, neural network adaptive control and preset performance control is provided; according to the method, the multiple fixed-wing unmanned aerial vehicles can realize preset time convergence of path following errors and preset time preset performance convergence of control signal tracking errors under the conditions of external disturbance and actuator faults, so that a high-precision and quick-response cooperative path following formation control task is completed; and meanwhile, the control precision and robustness are improved.

The invention discloses an unmanned aerial vehicle cluster cooperative formation control method and system based on an unknown environment, and the method comprises the steps: setting a formation expectation position according to a tracking target position, and flexibly adjusting a specific position in a formation according to the position of the unmanned aerial vehicle, so as to construct a target point of each unmanned aerial vehicle; an optimal path from the position of an unmanned aerial vehicle to a target point is constructed, and after the optimal path is smoothed, the unmanned aerial vehicle is controlled by a model prediction controller to move along a track after the optimal path is processed; in the moving process of the unmanned aerial vehicle, whether the acceleration or height of the rear vehicle is adjusted or not is determined by setting the speed adjusting area and the position adjusting area of the front vehicle to avoid collision, so that the potential collision risk is effectively avoided, the flight safety of the unmanned aerial vehicle in a complex environment is improved, and particularly, the performance is outstanding in a dense or dynamically changing flight area.

The invention discloses a path planning and cooperative control method for a heterogeneous unmanned cluster system in a dynamic environment. The method comprises the following steps: firstly, constructing a distributed position observer of predefined time, and estimating initial and final formation target positions of each follower unmanned aerial vehicle by utilizing local information interaction between the unmanned aerial vehicles; constructing a global path planner based on an adaptive variable solution space RRT algorithm, and planning a safe global path when each unmanned aerial vehicle passes through the dense obstacle area; a local path planner based on a predefined time prediction filter is constructed, and the potential collision risk between the unmanned aerial vehicle and a dynamic obstacle in formation tracking is avoided; and finally, performing motion control on the unmanned aerial vehicle by adopting a sliding mode control method according to the safe global path and the local path planning result, thereby realizing fixed-time formation tracking control of the unmanned aerial vehicle cluster. By using the method, the multi-unmanned aerial vehicle system can realize safe flight in a complex dynamic environment, and the control performance when a formation tracking flight task is realized is ensured.

The invention discloses a low-altitude aircraft air traffic management method and system, and relates to the technical field of aircraft air traffic management, and the method comprises the following steps: a traffic management system formulates the flight path, speed, course and formation of each unmanned plane according to a preset flight plan and performance demands before the flight performance begins; by monitoring the flight state of the unmanned aerial vehicle in real time and combining external disturbances such as wind speed and airflow, the path and speed of the unmanned aerial vehicle deviating from the trajectory can be dynamically adjusted, trajectory deviation caused by wind speed change is effectively prevented, the flight formation is kept stable, the relative distance is prevented from being too close or disordered, and the collision risk is reduced. Meanwhile, the deep learning model intelligently evaluates the flight consistency deviation, identifies the unmanned aerial vehicle deviating from the trajectory, and adjusts the path in time when the deviation exceeds a threshold value, thereby ensuring the flight safety, preventing the collision caused by the trajectory deviation, and guaranteeing the stability and safety of the group flight.

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 provides an air-ground unmanned cluster system cooperative formation control method driven by reinforcement learning. Establishing a dynamic model for the air-ground unmanned cluster system; a distributed predefined time observer is constructed to estimate the state of a virtual leader, and a state reference is provided for cooperative formation tracking control; and reconstructing a dynamic model of the augmented air-ground unmanned cluster system, and further constructing a control gain matrix. Segmented constant initial excitation is applied to the augmentation system, state data of early-stage operation of the augmentation system is collected and stored, then an initial stability control strategy is obtained by using a data driving method according to the early-stage operation data of the air-ground unmanned system, and finally, the stability of the air-ground unmanned system is improved based on an offline strategy reinforcement learning algorithm and the initial stability control strategy based on the data. And learning an optimal formation tracking controller to realize optimal formation tracking control of the air-ground unmanned system. According to the invention, under the condition that a system model is unknown, the optimal time-varying formation tracking control of the air-ground unmanned cluster system can be realized according to complex task requirements.

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.

Disclosed are systems and associated methods for calculating and displaying formation flight information, to include aircraft spacing, predicted trajectory data, collision avoidance alerts, time on target details, and chalk-specific information, on an augmented reality display designed to interface with aviation helmets. Two or more networked computing devices, each on a separate aircraft, collect some combination of aircraft altitude, location, and inertial data, preform certain calculations, and then develops a virtual overlay according to aircraft relative position and nearby aircraft trajectories. The virtual overlay is further informed by compass and gyroscopic data from an operatively coupled augmented reality display device. The developed virtual overlay is then transmitted to the display device for viewing by the pilot. The display of relevant formation flight information using augmented reality tools may result in improved formation flight spacing, emergency procedure response, and collision avoidance.

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 provides a high-speed aircraft time cooperative guidance method and system based on sliding mode control, and belongs to the field of aircraft guidance and control. The problems that an existing guidance method is large in calculation amount, insufficient in real-time performance and poor in cooperation precision are solved. According to the method, the residual flight time of multiple aircrafts is quickly predicted through a centralized collaborative strategy, and the time deviation of the aircrafts is adjusted according to the principle that who follows the aircrafts slowly; designing a bottom layer time controllable guide law based on sliding mode control, and introducing a saturation function to improve a buffeting phenomenon and solve a singular point problem; realizing transverse trajectory bending of the aircraft through a three-dimensional offset proportional guidance law so as to adjust the flight time; the precision and efficiency of residual flight time calculation are improved by using a variable step size numerical integration method; and completing formation transformation of multiple aircrafts at the tail end of the gliding section to support a terminal guidance task. According to the method, the calculation complexity is reduced, meanwhile, the precision and robustness of time cooperation are remarkably improved, and reliable technical support is provided for a multi-aircraft cooperative guidance task.

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 embodiment of the invention discloses a formation control method, device and equipment for a distributed vertical fixed-wing unmanned aerial vehicle cluster and a storage medium, and relates to the technical field of unmanned aerial vehicle formation control. The method comprises the following steps: receiving topological position information of a formation, and determining a planned path; determining a first flight speed according to the current position information and the expected position information in the planned path; determining a second flight speed according to the current position information, the expected position information and topological position information in the formation; determining a third flight speed according to the current position information and the position relationship with other unmanned aerial vehicles in the formation; calculating a target flight speed based on the first flight speed, the second flight speed and the third flight speed; according to the target flight speed, the formation is controlled, and the stability and safety of the vertical fixed-wing unmanned aerial vehicle cluster formation are effectively guaranteed.

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 relates to a multi-unmanned aerial vehicle formation control method based on offline sample correction reinforcement learning. The method comprises the following steps: constructing a depth deterministic strategy gradient network for offline training; the wingman interacts with the environment according to the current strategy to generate historical trajectory information of the wingman, and an off-line sample is obtained; off-line samples are randomly extracted, and importance weights used for correcting the off-line samples are calculated according to the difference between a current strategy and a previous strategy; performing offline training on the depth deterministic policy gradient network based on the importance weight so as to update network parameters of the depth deterministic policy gradient network; constructing a depth deterministic policy gradient network for online execution, and loading network parameters trained offline; the real-time interaction information of the wing aircraft and the environment is used as input, and the optimal action output is generated for the wing aircraft. The method can effectively improve the formation strategy learning efficiency of the multi-unmanned aerial vehicle system, and has a very high application value.

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 an unmanned aerial vehicle power supply management system, belongs to the technical field of unmanned aerial vehicle power supply management, and solves the problem that no power supply management for cooperation of a whole unmanned aerial vehicle cluster is provided to improve power supply utilization. Comprising a flight monitoring module, an endurance monitoring module, a track comparison module and a flight control module, the flight monitoring module, the endurance monitoring module and the track comparison module are all in communication connection with the flight control module, and the flight monitoring module and the endurance monitoring module are also in communication connection with the track comparison module. The flight monitoring module is used for collecting flight parameters of each unmanned aerial vehicle in the unmanned aerial vehicle cluster and flight formation of the unmanned aerial vehicle cluster and feeding back to the flight control module, and the endurance monitoring module is used for collecting endurance information of each unmanned aerial vehicle in the unmanned aerial vehicle cluster and feeding back to the flight control module. The leader unmanned aerial vehicle and the communication unmanned aerial vehicle are replaced regularly, so that all the unmanned aerial vehicles share energy consumption, cluster influence caused by insufficient electric energy of a single unmanned aerial vehicle is avoided, power management is optimized, and cluster flight time of the unmanned aerial vehicles is prolonged.

The invention relates to the technical field of formation control of an air-sea heterogeneous cluster system, and discloses a predefined time formation control method based on a fuzzy neural network, and the method comprises the steps: building kinematics and dynamics models of a six-degree-of-freedom four-rotor unmanned plane and a three-degree-of-freedom unmanned ship; establishing a formation tracking error and a speed tracking error of the air-sea heterogeneous cluster system; constructing a preset performance function, and determining an error conversion function; determining a predefined time virtual control law; establishing a predefined time fuzzy neural network, and determining a distributed predefined time formation controller of the air-sea heterogeneous cluster system in combination with a predefined time virtual control law; and performing distributed predefined time error constraint formation control on the air-sea heterogeneous cluster system according to a distributed predefined time formation controller of the air-sea heterogeneous cluster system. According to the method, the robustness of the air-sea heterogeneous cluster is improved, so that the system can converge within the settable predefined time and does not depend on the initial value of the system.

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.