61 results about "Multirobot systems" patented technology

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 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 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 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 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 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 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.

This invention discloses a multi-robot path planning method in complex environments based on multi-strategy self-learning, belonging to the field of multi-robot path planning technology. This method employs the Spiral Forward Adaptive Multi-Objective Artificial Hummingbird Algorithm (SFA-MOAHA) for path planning, enhancing the algorithm's local exploitation and global exploration capabilities by fusing a spiral approximation strategy and a forward fusion learning strategy. Simultaneously, a deep reinforcement learning mechanism is introduced to adaptively select the optimal search strategy based on population state and environmental feedback during the iteration process. Compared to existing technologies, this invention can efficiently solve multi-robot path planning problems in complex dynamic environments, generating high-quality collision-free cooperative paths. It not only effectively avoids collisions between robots and between robots and static / dynamic obstacles, but also significantly shortens the overall path length and improves task completion efficiency, thereby enhancing the safety and robustness of the multi-robot system.

The application discloses a kind of unknown environment in multi-robot team's self-adapting exploration and task allocation method, belong to multi-robot system technical field.To improve the exploration efficiency and accuracy of multi-robot system in complex environment, the method of the present application is deployed before each robot frontier point detection, navigation and mapping module, in host deployment task allocation, frontier point detection and map merging module;After obtaining exploration task, each robot and host are executed frontier point detection in parallel by adaptive fast exploration random tree algorithm, host uses the evolution strategy guided by bayes to allocate target point detection task for current available robot, robot moves to target point and continues to detect, constructs local map by mapping module, host integrates global map, until completing exploration task.The present application effectively improves the exploration efficiency and accuracy of multi-robot system in unknown environment, optimizes resource allocation at the same time, reduces energy consumption, improves energy utilization efficiency.

The application provides a distributed pose graph optimization method and device based on multi-level graph segmentation, which is used for communication between multiple robots, and the method comprises the following steps: performing a multi-level graph segmentation operation on an initial pose graph and constructing a distributed optimization problem; updating selected variable blocks by using a Riemannian gradient coordinate descent algorithm until an optimal solution meeting a condition is searched; and projecting the optimal solution to a feasible solution of the pose graph optimization by using singular value decomposition. Through the design of the distributed pose graph optimization method based on multi-level graph segmentation, the application constructs more balanced optimization sub-problems which are easier to process, reduces the communication cost in the multi-robot network, and improves the overall optimization performance of the multi-robot system.

The invention relates to a bionic quadruped robot coal-fired power plant global intelligent inspection and cooperative scheduling system, and belongs to the technical field of intelligent control. A high-precision environment model is established through a multi-sensor fusion sensing module, and a reliable data basis is provided for intelligent inspection; global path optimization and real-time obstacle avoidance are realized through an intelligent path planning module, and the navigation precision of the robot in a complex environment is ensured; accurate motion state feedback is provided through a real-time state estimation module, and a basis is provided for control decision making; equipment operation and automatic execution of inspection tasks are realized through a task execution control module; intelligent collaboration and resource optimization of the multi-robot system are realized through the collaborative scheduling management module; a virtual simulation environment is constructed through a digital twinning optimization module, and predictive optimization of motion parameters is achieved; and stable motion control of the quadruped robot is realized through the self-adaptive gait control module.

This invention relates to a robot-task allocation method based on the KM algorithm, belonging to the field of robotics. The invention transforms the integer programming problem into a bipartite graph optimal matching problem, solves the maximum matching problem in the bipartite graph using the Hungarian algorithm, and then uses the KM algorithm to solve the optimal matching problem. This invention provides an easy-to-implement, efficient, and effective robot task allocation method. This algorithm is more efficient than other algorithms on dense graphs and can be applied to multi-robot systems performing tasks such as patrol and rescue.

According to the robot collaborative scheduling method based on fusion navigation provided by the invention, on the basis that the closed-loop detection module tracks and accurately corrects the actual walking route of the robot in real time, the dynamic scheduling view is updated by using the observation output of the error state Kalman filter; a real-time closed loop from single-machine positioning optimization to global state synchronization and then to system decision optimization is established, so that the whole multi-robot system has an instantaneous adaptive capability of collaborative scheduling, that is, after any robot obtains a more accurate positioning position, the positioning position can be immediately converted into a more intelligent collaborative scheduling collective behavior of the whole system; and therefore, the cooperative scheduling is more effective, more accurate and more reliable.

Techniques are disclosed to facilitate multi-agent path planning and to enable navigation for robotics systems to be more resilient to wireless network related issues. The discussed techniques include enhancing path-planning algorithms to consider wireless Quality of Service (QoS) metrics for the identification of planned multi-agent paths. Moreover, the techniques include the compensation of communication and computational latencies to enable offloading of time-sensitive navigation workloads to network infrastructure components.

The invention relates to the technical field of cooperative control of a multi-robot system, in particular to a multi-robot distributed optimal tracking method for an event trigger departure strategy RL, which comprises the following steps of: performing layout based on multiple robots, and constructing a kinematics and dynamics model for each robot; correspondingly defining a value function of each robot according to the kinematics and dynamics models to obtain a Bellman equation, and determining an optimal control strategy and a worst disturbance strategy through the Bellman equation; an event triggering mechanism is designed for each robot, a triggering error is defined, and an optimal control strategy and a worst disturbance strategy are combined to construct a triggering condition; constructing an event-triggered departure strategy reinforcement learning algorithm, iterating an optimal control strategy and a worst disturbance strategy, and establishing an event-triggered departure strategy reinforcement learning model; and an evaluation neural network is adopted to solve the event trigger measure reinforcement learning model, and the weight of the evaluation neural network is updated in combination with the trigger event until the optimal network weight is obtained and a stable tracking state is achieved.

The invention discloses a multi-robot cooperative control system and method based on unified field dynamic coupling. According to the system, a continuous space-time unified field fusing an environment and a task is constructed through a field modeling module; the field dynamics optimization module predicts, evolves and optimizes field coupling parameters of each robot based on a field model; each robot obtains a local field gradient through a distributed interface and is driven by a gradient tracking controller to move. According to the method, cooperative control is converted into a continuous evolution and local tracking process of a field, low-delay decision making, high adaptability and strong extensible cooperation of a multi-robot system are achieved, and the performance bottleneck of a traditional centralized, negotiated and potential field method in a dynamic complex scene is effectively solved.

A multi-robot cooperative SLAM method for a snowfield environment comprises the following steps: 1) deploying a multi-robot system, and loading a snowfield environment knowledge graph; 2) acquiring environmental data through a multi-source sensor system, and preprocessing the environmental data; 3) performing semantic segmentation, object identification, snowfield attribute prediction and visibility estimation on the environmental data through the multi-modal large model, and outputting an environmental semantic data packet; 4) dynamically updating the snowfield environment knowledge graph; 5) estimating the pose of each robot in the Riemannian space; 6) performing trajectory prediction on a dynamic target in the environment by using the trajectory prediction model; 7) constructing a local semantic dynamic map comprising a static environment map, a dynamic object map, a semantic map and a risk map; and 8) each robot shares the local semantic dynamic map and the pose information with other robots through a wireless network, and iteratively optimizes the pose and the map to obtain a global consistent semantic dynamic map.

This invention is a multi-robot encirclement and collision avoidance control method based on pure orientation information, belonging to the field of multi-robot system cooperative control technology. The method includes: constructing a kinematic model of a nonholonomically constrained multi-robot system and a moving target; implementing an observer for each robot based on pure orientation measurement to estimate its relative position to the moving target; using the relative position information estimated by the observer as state feedback and introducing a collision-free reference trajectory generated by the auxiliary multi-robot system, designing a dynamic controller, and ultimately achieving stable encirclement and global collision avoidance of the nonholonomically constrained robots around the moving target. This invention enables state estimation using only the unit vector of relative orientation and robot velocity without relying on distance measurement, achieving multi-robot collision avoidance encirclement and formation control with a fused collision avoidance mechanism based on pure orientation information, and realizing high-precision stable encirclement of maneuvering targets.

The application discloses a complex task-driven multi-robot cooperative planning method, and relates to the technical field of artificial intelligence and robot control, and the method comprises the following steps: converting a high-level task instruction into a dynamic task graph with time sequence constraints and logical dependencies; performing initial task allocation based on a robot capability matrix and a task demand vector; constructing a joint space-time planning model which integrates task logic, dynamics constraints and environmental obstacles, and solving a trajectory by using a mixed integer linear programming; deploying a distributed monitoring mechanism, evaluating global execution consistency by using Bayesian inference and triggering local re-planning; realizing efficient online adjustment by using an incremental graph search algorithm for sudden disturbances; and optimizing a subsequent task allocation strategy by using a reinforcement learning agent based on historical execution data. The application realizes the deep integration of task semantic understanding, resource scheduling and motion planning, and significantly improves the cooperative efficiency and operation robustness of a multi-robot system in an unstructured high-dynamic environment.

A consensus-based decentralized multi-robot approach is presented for reconstructing a discrete distribution of features, modeled as an occupancy grid map, that represent information contained in a bounded planar 2D environment, such as visual cues used for navigation or semantic labels associated with object detection. The robots explore the environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and estimate the feature distribution from their own measurements and the estimates communicated by neighboring robots, using a distributed Chernoff fusion protocol. Under this decentralized fusion protocol, each robot's feature distribution converges to the ground truth distribution in an almost sure sense.

The application is suitable for the field of computer technology, and particularly relates to a method for scheduling robots to perform tasks, which comprises the following steps: in response to a user instruction for indicating a target task, generating a task sequence of at least one subtask corresponding to the target task; assigning a robot capable of performing each subtask to each subtask to obtain a robot scheduling strategy; controlling the robot corresponding to each subtask to perform the corresponding subtask; in the case that an abnormal working state of a first robot is detected, determining a first target subtask from the task sequence; and updating the robot scheduling strategy according to the first target subtask to obtain an updated robot scheduling strategy. Through the decoupled task planning and resource scheduling architecture, the target task can be dynamically decomposed and intelligently assigned to a heterogeneous robot group, optimization is realized in the aspects of dynamic fault tolerance and precise collaborative control, and the work efficiency, robustness and automation level of the multi-robot system in a complex scene are comprehensively improved.

A system and methods for operating a multi-robot system (MRS) are disclosed. An example method can include receiving at least one transportation task; determining an optimal path for executing the at least one transportation task based at least in part on: (i) one or more transportation task parameters, (ii) a shared global critic function accessible to the first robot and the at least one additional robot, and (iii) a local critic function unique to the first robot; and executing the at least one transportation task in accordance with the determined optimal path.