99 results about "Multirobot systems" patented technology

The invention provides an industrial multi-robot intelligent collaborative planning method based on deep learning. The industrial multi-robot intelligent collaborative planning method comprises six parts including environment modeling, feature extraction, task allocation, trajectory planning, control instruction generation and rule distillation. The method comprises the following steps: acquiring multi-robot environment information by constructing a probability grid map and a topological structure, and extracting state and task features to form a comprehensive feature matrix; training an optimal task allocation strategy by adopting deep reinforcement learning, and combining CVAE and CEM joint modeling to optimize trajectory generation; an adaptive impedance controller based on MADDPG is further designed, and dynamic adjustment of interaction parameters is achieved; and finally, the control strategy is converted into a decision rule set through knowledge distillation, the control interpretability is improved, and the deployment complexity is reduced. According to the invention, the task cooperation efficiency and the control stability of the multi-robot system in a complex industrial environment can be effectively improved.

The invention relates to the technical field of robot control, particularly discloses a path planning and dynamic obstacle avoidance control method for multi-task collaborative operation of an industrial robot, and aims to solve the problems of task scheduling conflict, dynamic obstacle response lag and low collaborative efficiency in a multi-robot system. The method comprises the following steps: constructing a task-resource joint scheduling model and generating initial task allocation; planning a conflict-free collaborative path based on an improved space-time A star algorithm; predicting a dynamic obstacle trajectory by using an LSTM network and generating a space-time envelope; constructing a second-order safety barrier function fusing task priorities; local obstacle avoidance re-planning is realized through rolling horizon model predictive control; and the global rescheduling is triggered when the task delay exceeds the limit or the deadlock risk occurs. According to the technical scheme, closed-loop linkage of global task collaboration and local dynamic obstacle avoidance is realized, and the system collaboration efficiency, the obstacle avoidance success rate and the operation robustness are remarkably improved.

The invention discloses a multi-machine system number-real consistency error collaborative prediction method and system based on multi-agent reinforcement learning, and belongs to the technical field of multi-robot systems and artificial intelligence. According to the method, each robot is modeled as an intelligent agent, a centralized training distributed execution framework is adopted, and collaborative prediction of errors is achieved. Each agent collects own, neighbor and environment state information in real time, multi-source information integration is carried out through feature embedding and gating fusion, the influence of different information sources on errors is dynamically evaluated by adopting a layered attention mechanism, and propagation and coupling rules of the errors in a cluster are effectively modeled. In the training stage, the centralized commentator network is utilized to evaluate the global prediction performance, the cooperative optimization of each executor network is guided, and the prediction precision is improved. According to the method, active and collaborative prediction of the multi-machine system number-real consistency error is realized, and a key technical support is provided for error feed-forward compensation and system reliability guarantee.

The invention discloses a distributed task scheduling system and method for multi-robot collaborative drawing, and the system employs a distributed architecture of a master control server and a plurality of robot clients, and achieves the decoupling of drawing task generation and execution through a task queue. The master control server receives the task request, asynchronously generates a robot control instruction from the image data through a built-in task calculation module, and maintains a robot state management module; and the robot client registers the unique identifier and keeps communication with the server. During scheduling, the server actively queries the state management module, selects an available robot to issue an instruction and atomically updates the state of the available robot to be unavailable; and after the task is completed, the client feeds back a receipt, and the server updates the state to be available. According to the method, the problems of high concurrency, repetition prevention and loss prevention of task scheduling in a multi-robot system are solved, and efficient and reliable parallel execution of non-standardized artistic creation tasks and elastic expansion of the system are realized.

The invention relates to the technical field of industrial automation, in particular to a multi-robot control method and system based on the industrial Internet of Things and a medium. The method comprises the following steps: acquiring industrial warehouse channel data; generating a warehouse digital twin model based on the industrial warehouse channel data; performing multi-robot path conflict detection according to the warehouse digital twin model to obtain path conflict data; determining a robot number according to the path conflict data; performing motor winding damage detection based on the robot number to obtain motor winding damage data; performing iron core loosening analysis based on the motor winding damage data to obtain iron core loosening data; performing vibration suppression control optimization according to the iron core loosening data to obtain vibration suppression control data; and robot scheduling is carried out according to the path conflict data to obtain robot scheduling data. According to the invention, the path planning precision and fault response efficiency of the multi-robot system are improved based on the industrial automation technology, and the operation efficiency of industrial warehouse operation is improved.

The invention discloses a multi-robot affine formation path optimization method driven by a virtual navigator, and the method comprises the steps: firstly setting a point set composed of the virtual navigator and followers, enabling the virtual navigator to form a convex hull and meet an affine localization condition, so as to guarantee that the followers are always located in the convex hull; generating a collision-free reference trajectory of the mass center of the virtual navigator based on a fast random tree algorithm; constructing a nonlinear constraint optimization problem NCO which contains linear transformation constraint and meets smoothness, obstacle avoidance, speed and angular speed constraint; and establishing a communication graph of the virtual navigator and the follower, calculating balance stress, designing a smoother follower distributed differential speed control law, and realizing formation path optimization and obstacle avoidance control. Compared with an existing method, the formation path generation method has the advantages that the smooth and stable formation path fitting the reference trajectory can be generated while obstacle avoidance and physical feasibility are guaranteed, and the cooperative motion capability and the control performance of a multi-robot system in a complex environment are improved.

The invention discloses a multi-robot safety control method and system based on a large language model, and the method comprises the steps: receiving first information through a first large language model, and generating a reference planner meeting the task requirements; if the reference planner output by the first large language model has compiling errors, the multi-robot distributed safety control system automatically captures error information and transmits the error information to the second large language model, the second large language model checks and modifies codes, it is ensured that the codes can be correctly executed, and the modified correct codes are input into the reference planner; and according to the control signal obtained by the reference planner, all the robots of the multi-robot system are controlled to work cooperatively through the control signal to complete a specified task and safely reach a specified target point. According to the invention, the stability and safety of all robots in the robot system can be ensured in a complex environment.

The invention relates to the technical field of robot fault recognition, and provides a multi-robot consistency Byzantine fault detection method. The method comprises the steps that observation motion information of each target robot in the multi-robot system at the current moment is acquired; encrypting each piece of observation motion information to obtain encrypted information of each piece of observation motion information, and uploading all the encrypted information to the block chain; decrypting and integrating all encrypted information at the current moment on the block chain to obtain a state data frame at the current moment; and when the state data frames are conflict data frames, determining the number of supporters and the number of counters of each target robot according to the conflict data frames, and determining a Byzantine fault robot from all the target robots based on the number of supporters and the number of counters of all the target robots. According to the method, the Byzantine fault identification reliability can be improved.

The invention relates to the technical field of data processing, and discloses an AMR transmission path data comprehensive processing method and system. The method comprises the following steps: processing environment sensing data through a data fusion algorithm to obtain an environment state matrix for recording information of an obstacle, a dynamic target and a passable area; robot state information and the environment state matrix are combined to obtain a joint state space integrating global and local information; training the joint state space through a multi-agent reinforcement learning algorithm to obtain a collaborative decision model composed of an independent strategy network and a shared evaluation network; processing the control instruction through a trajectory prediction model to obtain a collaborative path set storing robot path information; and analyzing the collaborative path set through a conflict detection algorithm, and triggering a scheduling strategy to obtain a path optimization result when a conflict is detected. According to the invention, the cooperative efficiency of the multi-robot system and the intelligent level of path planning are obviously improved.

In order to solve the problem, the invention provides an intention-aware multi-robot scheduling optimization method based on a graph neural network, and the method comprises the steps: constructing an explicit intention vector of a multi-tuple structure, fusing a robot state, task information and a resource use condition, constructing an intention graph representing a task conflict and a cooperative relationship, and introducing a spectrum sparsification mechanism; then, a graph attention network and a strategy updating module are adopted to jointly train a strategy model, strategy optimization of each robot node on a graph structure is achieved, and therefore the overall task income is maximized, path conflicts and resource competition are minimized, and a universal path planning algorithm is used for dynamically constructing a conflict avoidance path; compared with the prior art, the method has higher real-time performance, expandability and environment adaptability, and the scheduling efficiency and the task completion rate of the multi-robot system in dynamic scenes such as disaster response and the like are remarkably improved.

The invention relates to the technical field of multi-robot collaboration, and provides a multi-robot collaboration method and system and a multi-robot system. The multi-robot cooperation method based on service state triggering comprises the following steps: continuously sensing an environment state and user state information through a multi-modal sensor to construct a service state vector for quantitatively describing an interaction condition among a user, an environment and a robot; responding to the condition that the deviation degree of the service state vector and the expected service state meets a cooperative triggering condition, generating a cooperative demand for indicating a target service state, and broadcasting the cooperative demand to other robots; a cooperative structure which negotiates with other robots based on the cooperative demand to determine the cooperative demand, wherein the cooperative structure comprises cooperative robots participating in execution and role allocation of the cooperative robots; and continuously monitoring a service state vector when the cooperative robot executes the corresponding behavior based on the role allocation.

The invention relates to the technical field of industrial automation, and discloses a multi-robot collaborative operation method for new energy street lamp production, and the method comprises the steps: firstly, carrying out the function, time sequence and spatial dimension decomposition of a production order, and generating a structured task package; secondly, based on a dual optimization target of maximizing global production efficiency and robot load balancing, the task package is distributed to the optimal robot unit through an intelligent optimization algorithm; thirdly, planning a conflict-free cooperative motion track for each robot through a space-time coordination algorithm, and judging and solving potential motion conflicts based on the traffic priority; and finally, through synchronization strategies such as master-slave control or distributed negotiation and in combination with closed-loop adjustment of sensor data, high-precision collaborative execution of the robot is realized. The invention further comprises a real-time monitoring and hierarchical exception handling mechanism based on digital twinning. According to the method, the collaborative operation efficiency, the flexibility degree and the operation stability of the multi-robot system can be remarkably improved.

The invention discloses a multi-robot path planning method in a complex environment based on MQAHA, and belongs to the technical field of multi-robot path planning. According to the method, firstly, an optimal point set initialization strategy based on the number theory is adopted, an initial population which is evenly distributed is constructed, and diversity and coverage of a search starting point are remarkably improved; secondly, proposing an elite-guided variation migration foraging strategy, fusing direction guidance of a current optimal solution and a disturbance mechanism of a random individual, and effectively enhancing local development capability and global escape performance; furthermore, a learning-driven strategy self-adaptive selection mechanism is introduced, the foraging behavior probability is dynamically adjusted according to the population state, efficient balance of exploration and development is achieved, and therefore the premature convergence problem of the algorithm is systematically relieved. According to the method, the multi-robot cooperative motion path can be quickly generated in a complex obstacle environment, static and dynamic obstacles are effectively avoided while the total path length is reduced, and the motion efficiency and operation safety of a multi-robot system are remarkably improved.

The invention discloses an intelligent mobile mechanical arm composite robot system and method controlled by the Internet of Things, and belongs to the field of application of intelligent robots and the Internet of Things. Aiming at the problems of disjunction between global scheduling and local security, confrontation congestion of robots, difficulty in optimizing data dispersion, difficulty in equipment migration and the like in the existing multi-robot system collaboration, a cloud-side-end collaboration architecture is provided, wherein a cloud side is responsible for task allocation and time sequence arrangement; the edge end is responsible for rolling execution and safety control; and the robots perform mutual avoidance, road occupation and relay based on collaborative semantics. The system comprises a cloud job arrangement module, an edge execution control module, a cooperative communication module and a data closed loop module. According to the method, conflict congestion is reduced through a complete collaborative semantic mechanism, a data-driven strategy optimization closed loop is established, decoupling of the method and equipment is realized, and the operation efficiency, safety and mobility of multiple robots in a complex scene are remarkably improved.

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 discloses a method and system for multi-robot collaborative operation in oil and gas fields based on a large language model, relating to the fields of robot collaboration and intelligent planning. The method acquires oil and gas field operation task information, pre-sets task construction rules, uses a large model to parse task instructions and form a hierarchical task tree, and then writes oil and gas field operation constraint information to form a constrained hierarchical task tree. Simultaneously, it acquires robot capability information and constructs a skill library, matches and selects execution robots, and thus determines the multi-robot collaborative operation arrangement to complete the oil and gas field operation task. The system includes a task acquisition module, a task rule pre-setting module, a task tree generation module, a constraint writing module, an execution subject determination module, and a collaborative execution module. This invention, through the collaborative design of the method and system, enhances the understanding and collaborative stability of multi-robot systems for complex tasks, improving their collaborative efficiency and task completion quality in oil and gas field scenarios.

The application relates to the technical field of robot fault identification, and provides a multi-robot consistency Byzantine fault detection method. The method comprises the following steps: acquiring observation motion information of each target robot in a multi-robot system at a current time; encrypting each observation motion information to obtain encrypted information of each observation motion information, and uploading all the encrypted information to a block chain; decrypting and integrating all the encrypted information on the block chain at the current time to obtain a state data frame at the current time; when the state data frame is a conflict data frame, determining the number of supporters and the number of opponents of each target robot according to the conflict data frame, and determining a Byzantine fault robot from all the target robots based on the number of supporters and the number of opponents of all the target robots. The method can improve the reliability of Byzantine fault identification.

The invention belongs to the technical field of intelligent warehouse logistics, and relates to a space-time collaborative trajectory planning method based on multi-modal fusion, which comprises the following steps: acquiring robot information and environment information, and inputting the robot information and the environment information into a trained lightweight student model to obtain a predicted trajectory; the training process of the lightweight student model comprises the following steps: acquiring multi-modal training data; the multi-modal training data comprises robot information and environment information; constructing a trajectory planning model, and training the trajectory planning model according to the multi-modal training data to obtain a trained trajectory planning model; training a student model through knowledge distillation according to the trained trajectory planning model to obtain a trained student model; according to the method, multi-modal data is adopted for coding, so that rich information is provided for the model, the model can capture complex dynamic relations and environment changes in a multi-robot system, the distribution scene is comprehensively perceived, and planning errors caused by information loss are avoided.

The invention provides a double-layer cooperative security defense strategy and system for a multi-robot system, and relates to the technical field of multi-robot system control, and the strategy comprises the steps: constructing a network layer model and a control layer model of the multi-robot system, calculating the consistent speed performance parameters of the multi-robot system, with maximization of the consistent speed performance parameters of the multi-robot system as a target, a network-control cooperative double-layer defense strategy is established, and the network-control cooperative double-layer defense strategy is modeled as a Markov decision process; and training the Markov decision process to obtain an optimal speed performance parameter of the consistency of the multi-robot system so as to obtain a security defense control strategy. According to the method, the network layer and the control layer are combined, the speed performance of the consistency of the multi-robot system is maximized, the influence of attacks on the system performance is reduced through bandwidth scheduling, and the speed of realizing the consistency of the multi-robot system is improved.

The invention provides a robot personalized federal learning method based on guided filtering feature fusion. The method comprises the following steps: step 1, initializing a system and deploying a guided model; step 2, dual-channel heterogeneous manifold coding and heterogeneous spatial manifold alignment: in each round of federated training, the server broadcasts global guide model parameters to all online robots; the robot inputs locally collected sample data into the local model and the received global guiding model at the same time; step 3, guiding filtering feature fusion based on enhanced manifold alignment; 4, an orthogonal decoupling reasoning mechanism is adopted, and a joint loss function with feature decoupling constraints is constructed; and step 5, parameter decoupling optimization and co-evolution. According to the method, the feature alignment problem of the heterogeneous robot system is solved, and the system compatibility is improved. The problems of inconsistent feature dimensions and topological structure differences caused by different hardware architectures in a multi-robot system are effectively solved.

This invention discloses a robust fixed-time encirclement tracking control method for heterogeneous multi-robot systems, applied to the field of cooperative control technology for multi-robot systems. First, a heterogeneous multi-robot system composed of first-order followers and fractional-order leaders is constructed, and a fixed-time encirclement tracking control objective is determined. Second, a robust fixed-time encirclement tracking controller composed of a sign function term and a power exponent term is designed, introducing high-gain parameters to suppress the influence of external disturbances and uncertainties. Simultaneously, adaptive techniques are used to estimate actuator failure factors in real time, thereby improving the robustness of the closed-loop system. Further, a Lyapunov function is constructed, and the stability conditions of the closed-loop error system are analyzed to obtain the upper bound of the settling time and the control parameter conditions. Finally, the designed robust fixed-time encirclement tracking controller is applied to the heterogeneous multi-robot system, demonstrating that all followers can converge to the dynamic convex hull constructed by the leaders within a fixed time.

The application discloses a kind of multi-robot task allocation methods based on distributed optimization, it is related to control and information technology field.The application combines saddle point dynamics with optimistic gradient ascent descent algorithm or super-gradient algorithm to establish the distributed task allocation algorithm of multi-robot system, can solve non-convex task allocation problem, and the application range of algorithm is wider.And the distributed task allocation algorithm established by the application is a kind of completely distributed algorithm, and it is not related to the solution of sub-optimization problem, and only through simple algebraic operation to update state, with lower computational complexity, it can guarantee that the system state of all robots converges to the optimal integer solution of task allocation problem quickly.

The invention discloses an orchard multi-robot task allocation optimization method based on hierarchical path reconstruction, and the method comprises the steps: S1, building a multi-target mathematical model, optimizing the maximum completion time and total energy consumption, including robot load, speed, energy consumption and battery capacity constraints, and employing VLDIM to generate an initial solution population; s2, optimizing the task sequence of each path by using DRRM in the microscopic layer; s3, robot-level DRRM optimization, TRRM task redistribution, CRRM charging path reconstruction and environment selection are executed iteratively; and S4, splitting the longest path by using the SRRM, redistributing the longest path through the MILP1, and outputting a non-dominated solution set. Through hierarchical coding, multi-stage collaborative optimization and a special reconstruction mechanism for core constraints, the task allocation efficiency and scheme quality of a multi-robot system under complex constraints are significantly improved, and the method is suitable for agricultural automation and warehouse logistics scenes.

The application belongs to the field of path planning, and particularly relates to a robot formation planning method based on a five-dimensional configuration space. The method comprises the following steps: workspace and configuration space definition: the workspace is a two-dimensional plane for robot movement, and the configuration space is a five-dimensional space containing formation centroid, direction and scaling factor; sampling method: random sampling in the configuration space, and ensuring that the sampling points do not cause collision between robots; distance function: defining the distance between two points in the configuration space, and eliminating the dependence on the number of robots; search: searching in the configuration space using an algorithm, and constructing a collision-free path tree; path generation: converting the path in the configuration space into the path in the robot workspace, and smoothing using a spline curve. The robot formation can pass through a narrow channel, and split and merge when encountering a large obstacle. The application provides a high-efficiency, flexible and collision-free global path planning method for a multi-robot system.

The invention relates to the technical field of multi-robot cooperative control, and discloses a cooperative control method and system for multiple robots, and the method comprises the steps: building a first mathematical model of a multi-robot system, building a second mathematical model of a moving target, building a communication topology of the multi-robot system and the moving target, and carrying out the communication of the multi-robot system and the moving target. Establishing a third mathematical model of cooperative rotation of the multi-robot system based on the first mathematical model and the second mathematical model, constructing a distributed fixed time estimator, determining the internal state of the moving target based on the distributed fixed time estimator, and determining the real-time position of the moving target based on the internal state. And control input of the multi-robot system is determined according to the distributed fixed time estimator and based on the fixed time controller, and the control input is substituted into the multi-robot system to complete fixed time cooperative rotation consistency control. According to the invention, the reliability and stability of cooperative control of multiple robots within a fixed time are ensured.

This invention relates to the field of multi-robot task technology and discloses a hybrid multi-robot collaboration method and system driven by a large language model. The method includes: receiving natural language task instructions input by a user and performing semantic parsing through a task decomposition large language model to generate a task set containing multiple structured sub-tasks; multiple robots responding to and competing for each sub-task using a decentralized negotiation mechanism to determine the task allocation result when a single robot acts as the execution subject; when the skill requirements of a sub-task exceed the skill space of any single robot, a collaborative task team building process based on the decentralized language negotiation mechanism is triggered; each execution subject executes the assigned sub-task according to the negotiation result. This invention can significantly enhance the understanding and execution flexibility of multi-robot systems for natural language tasks, improving their collaboration efficiency and task completion quality in complex task scenarios.

The present invention belongs to the field of automatic measurement and marking of complex castings. It discloses a high-precision calibration and online optimization method for dual robots. Based on hand-eye calibration and four-point calibration, the casting-measurement-marking system components are calibrated separately when the system is deployed to obtain high-precision initial values ​​of system parameters. Calibration data is collected online during the measurement-analysis-marking process based on a visual tracking system to achieve online optimization of multi-robot system parameters. The optimized robot paths are fed back to a data control center to ensure high-precision and stable operation of the robot marking.

The invention discloses a multi-robot cooperative positioning method and system based on UWB relative distance measurement, relates to the technical field of multi-robot cooperative positioning, and aims to solve the problem of cluster cooperative autonomous navigation of a multi-robot system in an environment that external navigation equipment is unavailable or weak communication exists. According to the technical key points, a state model of a multi-robot cooperative positioning problem and a UWB relative measurement model are established, and a linearization error state system is deduced; the reason of inconsistency of standard extended Kalman filtering in cooperative positioning is analyzed, and the essence of the standard extended Kalman filtering is revealed to be from considerable asynchronization between an original nonlinear system and a linearization error state system of an estimator. The invention designs a method based on linear time-varying transformation, constructs a KD-EKF algorithm with a time-invariant and unobservable subspace, and performs Monte Carlo contrast simulation to verify that the KD-EKF has higher positioning precision and better consistency compared with a standard EKF method and an FEJ-EKF method. A multi-robot system experimental platform based on the Tucker ground robot is developed, and experimental verification is carried out on the designed KD-CL algorithm. Experimental results verify the superiority and effectiveness of the proposed algorithm.

The application discloses a kind of robust distributed optimization methods with fixed time convergence under symbolic graph, specific steps are as follows: S1, the multi-robot system influenced by external interference is established;S2, design optimization algorithm based on distributed observer, so that the virtual state of each robot realizes two-part consistency in fixed time, and then solve the optimal solution of global convex objective function under symbolic graph;S3, design robust tracking controller, suppress the influence of external bounded disturbance, so that the actual state of robot can converge to virtual state in fixed time;S4, combine S2, S3, complete the fixed time two-part consistency and distributed optimization of multi-robot system;S5, the above-mentioned distributed optimization algorithm is applied to multi-robot system.The application not only can realize two-part consistency in fixed time, but also effectively solve the distributed optimization problem of multi-robot system influenced by unknown disturbance under symbolic graph.

The invention discloses a bidirectional man-machine behavior analysis method and system based on a meta-learning social heterogeneous multi-robot system. The method comprises the steps that environment state data, robot internal state data and man-machine interaction data are acquired; based on the acquired data, performing intention analysis by using a double-layer grid observation model, state coding and a large language model, and generating feature representation suitable for multi-agent reinforcement learning and meta-learning; learning a path planning strategy, an energy management strategy, an obstacle avoidance strategy and a bidirectional interaction strategy in a high-density dynamic obstacle and complex crowd interaction scene based on feature representation in combination with a three-stage curriculum learning method, an internal curiousness reward and a meta learning framework based on MAML; and based on a path planning strategy, an energy management strategy, an obstacle avoidance strategy and a bidirectional interaction strategy, performing man-machine interaction in various dynamic environments and responding to corresponding action instructions. According to the invention, the environment adaptability, the resource scheduling efficiency and the man-machine cooperation efficiency of the multi-robot system are improved.