258 results about "Swarm control" patented technology

The invention relates to the technical field of unmanned aerial vehicle cluster control, and particularly discloses an intelligent cooperative flight path planning method and system for an unmanned aerial vehicle cluster system, and the method comprises the steps: firstly constructing a dynamic environment perception model, collecting data through all unmanned aerial vehicle sensors, and carrying out the preprocessing, attention feature extraction and federal learning fusion, and generating a global environment situation map; planning and screening a candidate track set by adopting a particle swarm-genetic hybrid optimization algorithm for adaptive weight adjustment on the basis of the image; a global optimal track consensus is achieved through an improved consensus algorithm and conflict resolution through a distributed collaborative negotiation mechanism and no human-computer interaction evaluation indexes; and finally, monitoring the environment in real time during execution, triggering dynamic re-planning when the environment is abnormal, and ensuring track adaptation through multi-level threshold and incremental planning. According to the method, the unmanned aerial vehicle cluster can quickly respond to the environment change and adjust the flight path, so that the task execution efficiency and success rate of the unmanned aerial vehicle cluster in the complex dynamic environment are improved.

The invention discloses a hierarchical multi-agent robot cluster control method and system, and aims to solve the problems of partial observability and environment non-stability of a multi-agent system in a complex environment. According to the method, a three-layer layered reinforcement learning architecture is constructed, a high-layer strategy is responsible for global task decomposition and role allocation, a middle-layer strategy converts tactical intention into a cooperative behavior mode, and a low-layer strategy executes accurate motion control; a graph neural network is adopted for cluster modeling, global graph representation and local neighborhood features are extracted in parallel through graph convolution and an attention mechanism, and hierarchical decision making is supported; a centralized graph enhancement evaluation network is designed to be combined with an MAPPO algorithm for collaborative optimization, and dynamic adversarial training is introduced to improve strategy robustness. According to the method, effective decoupling of global planning and local control is realized, and the cluster cooperation efficiency, the strategy interpretability and the adaptive capacity in a dynamic environment are improved.

The invention discloses a hybrid attack defense control method and system for a heterogeneous cluster in an urban confrontation environment, terminal equipment and a medium, and relates to the technical field of unmanned platform cluster control, and the method comprises the steps: constructing an ideal system model of an unmanned platform cluster comprising a leader and a plurality of followers, constructing an information physical hybrid attack model based on the model; using the attack model to simulate an attack behavior of an attacker on an ideal system to obtain an attacked system model; based on an attacked system model, constructing a distributed elastic security estimator with a compensation mechanism, compensating attack influence for each follower and estimating an expected position; and based on the expected position of the follower, constructing a distributed elastic safety controller with a compensation mechanism, and controlling the follower. By designing the estimator and the controller, attack interference is counteracted, control input is generated, and safe collaboration of the heterogeneous nonlinear unmanned cluster under the cyber-physical hybrid attack is guaranteed.

The invention discloses an unmanned autonomous cluster flight control method based on a bionic alert mechanism, which is applied to the field of unmanned autonomous cluster control, and is characterized in that a W-MSR algorithm and a dynamic weighted bionic alert mechanism are fused, the W-MSR filters and eliminates extreme values of neighbor individuals, the dynamic weighted bionic alert mechanism strengthens input from informed individuals, and the dynamic weighted bionic alert mechanism is used for improving the robustness of the unmanned autonomous cluster flight control. And meanwhile, the influence of suspicious individuals is inhibited, so that the local information aggregation degree is effectively improved, and group splitting can be prevented. Different from a scheme depending on explicit attacker recognition, the method can improve the motion accuracy and connectivity of the unmanned autonomous cluster in a confrontation environment by probabilistically suppressing the influence of suspicious individuals and amplifying a consistent signal, provides a new idea for improving the security of a swarm intelligence system, and has a wide application prospect. Damage of malicious individuals to the cluster is effectively defended, and robustness and recovery capability of the cluster in a complex environment are improved.

The invention discloses a hydro-junction gate group intelligent cooperative control method based on digital twinning, and relates to the technical field of water conservancy control, and the method comprises the steps: taking a water conservancy dynamic digital twinning model as a reinforcement learning environment, setting gate group control parameters and performance indexes, carrying out the training under diversified hydrological conditions, and generating a preliminary control strategy; inputting the preliminary control strategy into a water conservancy dynamic digital twinborn model, executing rollback simulation covering multiple working conditions, and obtaining simulation responses of the corresponding working conditions; scheduling performance indexes of simulation response are analyzed, high-quality control strategies are screened, scheduling conflict identification and conflict resolution among gate groups are carried out, and an optimal control strategy set is output; according to the method, by executing rollback simulation covering multiple working conditions and automatically identifying and eliminating scheduling conflicts among gate groups, the adaptability and accuracy of a scheduling strategy are improved, conflicts and risks are reduced, and efficient and safe operation of the hydro-junction is ensured.

The invention relates to the technical field of unmanned aerial vehicle cluster control, in particular to a multi-mode sensing fusion unmanned aerial vehicle cluster control method, device and equipment based on deep learning and a storage medium. According to the invention, gesture, voice, vision and touch data are collected in parallel through a multi-mode perception access layer and are preprocessed and synchronized; utilizing a special deep network to extract high-level features rich in cluster semantics; through cross-modal semantic alignment, graph neural network association learning and a multi-dimensional conflict detection and intelligent resolution mechanism, deep fusion and accurate intention analysis of four modals in a cluster control semantic space are realized; high-level instructions are efficiently converted into single-machine executable tasks and coordination strategies by adopting hierarchical task decomposition, virtual structure formation control and a multi-objective optimization algorithm; state feedback and adaptive parameter optimization are carried out in real time, and the accuracy, reliability, environmental adaptability and man-machine interaction efficiency of large-scale unmanned aerial vehicle cluster control are improved.

The invention discloses a reinforcement learning-based cluster spacecraft multi-target intelligent cooperative tracking method. The method comprises the steps of establishing a relative motion relationship between a tracking spacecraft and a target spacecraft through a nonlinear orbit kinetic equation; designing a double-layer game framework, constructing a non-zero sum game in the cluster to drive cooperation, and introducing a zero and maximum and minimum game between the cluster and a target to describe a confrontation relationship; based on communication topology and relative state information, defining a local error term and a performance index function of the tracking spacecraft and the target spacecraft, and defining an optimization target of each agent under cooperation and confrontation; and iteratively updating the value function and the control law. According to the invention, collaborative tracking can be realized without defining a system model. The method can be widely applied to the field of cluster control.

The invention relates to the technical field of unmanned aerial vehicle cluster control and intelligent scheduling, and discloses an industrial unmanned aerial vehicle cluster intelligent scheduling management method based on multi-data analysis, and the method comprises the steps: extracting a quantitative risk feature vector based on multi-source operation data; constructing and updating a four-dimensional dynamic risk field; constructing a rolling time domain optimization model to generate a cooperative scheduling scheme including adaptive formation adjustment; decomposing and issuing a scheduling instruction; and carrying out dynamic risk field updating based on the real-time returned data, and triggering the compensatory adjustment. According to the method, real-time sensing and quantitative evaluation of multi-source and dynamic risks in a complex industrial environment can be realized, flight safety and task efficiency are dynamically balanced through rolling optimization and an elastic compensation mechanism, and robustness and intelligence of overall operation of a cluster are improved.

The invention discloses a composite game reinforcement learning method and device for a four-rotor unmanned aerial vehicle cluster, and relates to the technical field of unmanned aerial vehicle cluster control. The method comprises the steps that an evaluation-execution network of the unmanned aerial vehicle is used for directly approaching a value evaluation and cooperation strategy online in a state-control input data space of cluster flight, repeated offline solving of a high-dimensional coupling HJ equation set under strong coupling nonlinear dynamics of the unmanned aerial vehicle is avoided, and therefore the modeling precision requirement and the calculation overhead are remarkably reduced. By introducing a filtering mechanism with a'forgetting factor ', weighted integral processing is performed on samples such as pose / speed / relative formation errors, control instructions, energy consumption and safety cost collected in the historical flight process of a cluster, and a composite'evaluation' network error and regression signal fusing current and historical information is constructed; and a finite excitation criterion which can be inspected on line is given on an information matrix level, so that the unmanned aerial vehicle cluster can still keep effective learning under the condition that a continuous PE condition is not met.

The invention discloses a deep reinforcement learning multi-unmanned aerial vehicle cooperative path planning method of an HCA-MAPPO, and relates to multi-agent reinforcement learning and unmanned aerial vehicle cluster control. From a multi-unmanned aerial vehicle cluster collaborative path planning method, a simulation environment comprising a three-degree-of-freedom unmanned aerial vehicle kinematic model and a dual-coordinate system management system is established, and accurate simulation of a complex geographic scene is realized; a deep reinforcement learning method based on hierarchical cross attention HCA and multi-agent near-end strategy optimization MAPPO is provided; the method comprises the following steps: introducing an HCA executor network for performing priority processing on multi-source heterogeneous information; an internal curiosity module based on collaborative consistency gating is designed, so that the effective exploration efficiency in a sparse reward environment is improved; the invention provides a tangent guidance mixed reward shaping method based on conflict perception, and solves the problem of local optimum in path planning. And the situation awareness capability, the training convergence efficiency and the path planning success rate of the unmanned aerial vehicle cluster are obviously improved.

The invention discloses a training fault tolerance method and device applied to a distributed training system and a chip product, and relates to the technical field of distributed training. The method comprises the following steps: for a distributed training system which comprises a plurality of computing node clusters and each computing node cluster comprises a plurality of computing node groups, the plurality of computing node groups are used for executing distributed training tasks of the computing node clusters in parallel, and the plurality of computing node clusters are used for executing distributed training tasks of the distributed training system in parallel; under the condition that a fault node occurs in the distributed training system, determining a fault node cluster from a plurality of computing node clusters, the fault node cluster being a computing node cluster comprising the fault node; and controlling the fault node cluster to cancel execution of the distributed training task corresponding to the fault node cluster, and controlling at least one residual computing node cluster to execute the distributed training task of the distributed training system. According to the embodiment of the invention, the training fault-tolerant capability and the training stability of the distributed training system can be improved.

The invention relates to a robot control technology, and discloses a robot cluster control method and system and a storage medium, and the method comprises the steps: receiving motion state data, network link state data and service type information of each robot; predicting a motion track of each robot in a future preset time period based on the motion state data in combination with a digital map, and predicting a communication quality change trend of a current network link of each robot in the future preset time period based on the network link state data; determining a target network link of each robot based on the motion trail, the communication quality change trend, the digital map, the service type information and the received node operation state information of each edge relay node, and generating a network switching instruction containing the target network link and the switching opportunity, and the corresponding robot executes network link switching according to the network switching instruction. According to the invention, global prediction and active switching of robot cluster communication can be realized.

The invention belongs to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle group control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the adjacent unmanned aerial vehicle information of each unmanned aerial vehicle in an unmanned aerial vehicle group in a GNSS limited or GNSS failure environment, and obtaining the adjacent unmanned aerial vehicle information of each unmanned aerial vehicle in the unmanned aerial vehicle group based on the adjacent unmanned aerial vehicle information through a division criterion and a greedy algorithm; dividing the unmanned aerial vehicle group into a plurality of unmanned aerial vehicle dynamic groups, calculating to obtain positioning information of each unmanned aerial vehicle in the corresponding unmanned aerial vehicle dynamic group by applying an extended Kalman filtering algorithm according to a target positioning optimization function and in combination with the multi-source sensor real-time data of the unmanned aerial vehicles and the adjacent unmanned aerial vehicle information, and determining the unmanned aerial vehicle dynamic group based on the target task information and in combination with the positioning information. A virtual spring-damping model is utilized to cooperatively control dynamic grouping of the unmanned aerial vehicles; through the extended Kalman filtering algorithm, the target positioning optimization function and the preset virtual spring-damping model, dynamic grouping of the unmanned aerial vehicles is cooperatively controlled, and the control efficiency of the unmanned aerial vehicle group is improved.

The invention discloses an intelligent charging pile cluster control method, and relates to the technical field of new energy automobile charging, and the method comprises the following steps: collecting the voltage and current changes of a plurality of electric vehicles at the moment of each power rise when the electric vehicles enter a charging station in a low battery residual electric quantity state, extracting the sudden change amplitude, and generating a power impact dial gauge according to the time scale, serving as a control reference; and calling a sudden change amplitude at the tail end of a power impact dial gauge, tracking an earliest trigger node, establishing a risk distribution model in combination with an adjacent power path, compressing risk distribution into a power scheduling priority index band, and generating a power distribution rhythm. Through a power impact dial gauge and multi-layer dynamic regulation and control, segmented, time-sharing and priority-sharing cooperative distribution of power during multi-vehicle concurrent charging is realized, and through combination with a slow release mechanism of a breathing type slope window, a reverse adsorption curtain and a turn-back energy bypass, bus power output is smooth and stable, voltage ripples are reduced by more than 50%, thermal stress and power impact are reduced, and the service life of the bus is prolonged. And the safety and operation efficiency of the charging station are improved.

The invention provides an unmanned aerial vehicle cluster robust control system and method for electric power inspection in a complex environment, and relates to the technical field of unmanned aerial vehicle cluster control, and the method comprises the steps: obtaining local related information when an unmanned aerial vehicle cluster executes an inspection task in the complex environment, and obtaining an optimal inspection path and task allocation based on the local related information, executing inspection based on the optimal inspection path and task allocation, and realizing unmanned aerial vehicle cluster maintenance and anti-interference control based on an H-infinity robust controller in the inspection process. According to the method, the inspection task can be autonomously completed in a weak information environment, autonomous decision, task allocation and robust control technologies are combined, the control difficulty caused by communication interruption or information loss of a traditional unmanned aerial vehicle cluster in a complex environment is solved, and the power inspection efficiency and safety are improved.

This invention discloses a hardware-in-the-loop (HIL) simulation platform for load cluster tracking control. The master station simulation module simulates the issuance of load cluster control aggregation commands. Any smart energy unit module decomposes the load cluster control aggregation commands according to a distributed collaborative control algorithm, achieving distributed collaboration among each smart energy unit. Any adjustable load in each adjustable load cluster decomposes the load cluster control commands according to the distributed collaborative control algorithm, achieving distributed clustering of each adjustable load in the adjustable load cluster. Each adjustable load calculates its current adjustment amount based on the received load control commands and adjusts its operating state in real time according to this adjustment amount. This invention employs a distributed collaborative control strategy on the HIL platform and uses a synchronization mechanism to ensure the continuity of the distributed strategy, improving the system's stability and robustness.

A method and system for AI-based collaborative energy-saving valve cluster control, belonging to the interdisciplinary field of industrial process control and artificial intelligence, comprises the following steps: First, modeling and defining the optimization problem of the valve cluster system; second, constructing an intelligent optimization model; third, training the intelligent optimization model based on federated learning; and fourth, online collaborative control and continuous learning. This is achieved through an energy-saving valve cluster control system, including a valve cluster system modeling module, an intelligent optimization model construction module, an intelligent optimization model federated learning training module, and a collaborative control and continuous learning module. This invention enables direct and dynamic mapping between the valve cluster system state and the optimal valve opening vector, dynamically and selectively integrating key local information of valves, edges, and nodes to achieve precise and adaptive collaboration; significantly reducing the total energy consumption of the system and achieving optimal global energy efficiency and dynamic continuous optimization of the valve cluster system.

Techniques and mechanisms for preventing resource starvation and service throttling during container orchestration system operations are provided. In a Kubernetes-based container orchestration system, pluggable mechanisms are applied to pod and / or associated container creation and operation at the direction of a cluster controller to prevent resource overloading and service throttling during thundering herd problems encountered by components of a Kubernetes cluster. A special-purpose plugin may be provided that causes creation of a pod and / or associated container according to a prescribed order to prevent caching overload and resulting thundering herd problems during pod and / or associated container creation and startup.

The application discloses an unmanned aerial vehicle cluster control method and system based on artificial intelligence, relates to the technical field of unmanned aerial vehicle cluster control, and comprises the following steps: acquiring global cooperative situation data through a sensor, and preprocessing the global cooperative situation data to obtain preprocessed global cooperative situation data; calculating path cost indexes of each unmanned aerial vehicle through a fuzzy C-means clustering algorithm according to the preprocessed global cooperative situation data, generating a preliminary flight path, and dynamically adjusting the preliminary flight path according to real-time environmental feedback through a reinforcement learning algorithm; and sharing the adjusted preliminary flight path and global environmental information of the unmanned aerial vehicle cluster through a 5G network, analyzing the speed, heading angle, height, acceleration gradient and height change rate of each unmanned aerial vehicle, and generating cooperative control parameters; the application realizes efficient cooperative control and reliable execution of the unmanned aerial vehicle cluster in a dynamic environment, and improves overall cooperativeness and task completion capability.

The application relates to a robot cluster control method, which collects local environment data through a robot cluster and sends the data to an edge terminal, while the edge terminal acquires global environment data. Based on deep analysis and processing of the global and local environment data, the edge terminal can accurately identify dynamic targets in the environment and their detailed dynamic information. Based on this information, the edge terminal determines a robot task and selects a target robot most suitable for executing the task from the robot cluster, avoiding blind allocation of the task. Further, the edge terminal generates task execution information for the target robot based on the robot task, so that the target robot can act according to the instruction. The application greatly improves the global planning capability of the multi-robot system, so that the robot cluster can quickly and efficiently respond in a complex environment, realize close collaborative work, and significantly improve the ability and execution efficiency of the multi-robot system in dealing with complex tasks.

This invention provides a method and system for cooperative obstacle avoidance and trajectory planning in UAV swarm formation, relating to UAV swarm control technology. By introducing three-dimensional affine transformation parameterization of formation motion, the dimensionality and search space complexity of UAV swarm system modeling are reduced, alleviating computational burden. An affine A-RRT planning algorithm is employed to construct path topology in affine space, improving sampling search efficiency and reducing redundant nodes and ineffective expansion. ESDF gradient information is used for path correction and to construct sequential convex regions, simplifying the construction process of safe regions and ensuring sufficient safety margins. By constructing a quadratic programming model integrating multiple performance indicators and solving it online, a balance is achieved between trajectory smoothness, energy efficiency, and swarm formation maintenance performance, meeting the real-time planning requirements for cooperative obstacle avoidance in complex environments.

The present application belongs to the technical field of unmanned aerial vehicle cluster control, and particularly relates to a multi-unmanned aerial vehicle cooperative target tracking path planning method based on a spider wasp optimization algorithm. The method comprises the following steps: (1) establishing a mathematical constraint model of a flight path; (2) constructing an evaluation function according to the mathematical constraint model, including a fitness function and a total distance length; (3) based on the spider peak optimization algorithm, solving the evaluation function, and constantly iterating, calculating and comparing to obtain an optimal solution or an approximate optimal solution, i.e. an optimal path; and (4) distributing the optimal path to each unmanned aerial vehicle for execution. The present application simulates the predatory behavior of spiders and the social behavior of bees, realizes efficient path planning of multi-unmanned aerial vehicles in cooperative target tracking in a complex three-dimensional environment, improves the flight efficiency and safety of unmanned aerial vehicle clusters, and enhances the adaptability of unmanned aerial vehicle clusters in complex environments.

The application discloses a UAV cluster integrated control system based on formation flight, and relates to the field of flight control technology.The system comprises a flight acquisition module, an atmosphere acquisition module, a cluster control module and a display module.The flight acquisition module acquires data and stabilizes the output of posterior coordinates, ground speed and attitude angle through drift compensation Kalman filtering.The atmosphere acquisition module relies on a database of aerodynamic characteristics, and solves airspeed, barometric altitude and angle of attack through a model of atmospheric solution that is fused with model-specific aerodynamic correction coefficients and adaptive weighting.The cluster control module establishes an airflow disturbance model to quantify airflow disturbance intensity and generate disturbance coefficients of UAVs and adjacent UAVs, and dynamically outputs adjustment instructions with the minimum formation deviation and the stable attitude of a single machine as the target.The display module is used for 3D visualization monitoring of flight formation.The system significantly improves the stability and adaptability of formation flight, and is suitable for UAV cluster cooperative operation in complex scenarios.

A system for wireless power transmission is disclosed, and includes a plurality of UAVs, each having a transfer medium reservoir, an onboard power conversion unit, a communication module, a navigation module, a power delivery interface, and at least one sensor. Each UAV is configured to interface with a transfer medium source, receive a chemical power transfer medium into the transfer medium reservoir, fly to a target area containing a power recipient having a power demand, identify and land within a landing zone, provide chemical power transfer medium to an endpoint power conversion, and evaluate at least one directive to decide what action to take based on feedback. The system also includes a fleet control system communicatively coupled to the plurality of UAVs and configured to operate the plurality of UAVs as a swarm, generate at least one directive, and distribute the directive to the communication module of each UAV.

The invention relates to the technical field of cluster control, and particularly provides a cluster control method and device, equipment, a medium and a product. The method comprises the following steps: determining the task number and task positions of sub-tasks required for executing a target task; for any subtask, obtaining efficiency information of each agent participating in the subtask; one agent corresponds to one device in the cluster; determining an execution object corresponding to each subtask, wherein the execution object is an intelligent agent with the highest global efficiency; and controlling each execution object to execute the corresponding sub-task. The intelligent agents capable of undertaking the warning task in the current warning round are screened out to serve as execution objects, the warning role distribution condition of all the intelligent agents in the unmanned cluster is determined, and proper sub-tasks are distributed to all the execution objects in combination with the global efficiency of the multiple intelligent agents. And each intelligent agent can undertake the most suitable warning task according to the capability, the position and the current environment condition of the intelligent agent. Therefore, the warning capability of the unmanned cluster can be improved.

The present invention relates to a target attack device and a swarm control system comprising same. The target attack device may comprise: a main body; a mounting unit, which is disposed on one side of the main body and accommodates an explosive member; a memory, which is embedded in the main body and stores at least one instruction; and at least one processor, which is embedded in the main body and executes the at least one instruction.

The embodiment of the invention provides a communication method based on an I2C bus, electronic equipment, a storage medium and a program product, and relates to the technical field of communication, the method comprises the following steps: sending a preset setting table to all slaves, so that the slaves participating in broadcasting obtain a data length type code and a participation sequence by analyzing the preset setting table, entering an activated state; wherein the preset setting table comprises a data length type code and an address list of slaves participating in broadcasting; determining a write-in mode of the slave based on the data length type code in the setting table; and according to the broadcast write-in instruction time sequence and the write-in mode of the slave, transmitting to-be-written data through an I2C bus, so that the slave in the broadcast activated state writes the corresponding to-be-written data. The embodiment of the invention is used for providing a technical scheme capable of solving the technical problem that grouping control cannot be synchronized in a traditional I2C broadcast mechanism.

This invention relates to the field of unmanned aerial vehicle (UAV) swarm control technology. To address the problem in existing UAV control methods where the inability to synchronize states between UAVs leads to a lack of inter-UAV collaboration, this invention provides a UAV swarm control method and system. The method executes the following steps: Step S1, collecting the local state of each UAV to generate standardized local state information; Step S2, performing distributed full-state synchronization processing on the standardized local state information to generate globally consistent state information; Step S3, performing local intelligent decision-making processing on the globally consistent state information to generate local control commands; Step S4, performing execution control processing on the local control commands to generate UAV actions; Step S5, performing effect feedback processing on the UAV actions to generate updated local state information, and using the updated local state information as the new local state information, returning to step S2.