359 results about "Robot path" patented technology

The invention provides an intelligent inspection robot path optimization method and system based on an edge inference model, and the method comprises the steps: obtaining an environment feature topological graph of a target inspection region, and determining an initial edge inference model; incremental training is carried out on the initial edge reasoning model through the real-time environment perception data flow, and a dynamic reasoning model adaptive to the current environment characteristics is generated; performing priority scoring on each path node in the environment characteristic topological graph based on a dynamic reasoning model, and generating an initial optimization path sequence; triggering a feedback calibration mechanism of the dynamic reasoning model according to the environment perception data flow updated in real time, performing dynamic path node replacement on the initial optimization path sequence, and generating a final inspection path; and controlling the intelligent inspection robot to execute an inspection task according to the final inspection path, and continuously collecting new environment sensing data streams in the inspection task process to update a parameter set of the dynamic reasoning model. According to the invention, the adaptability and reliability of path planning to a complex dynamic environment can be improved.

The invention discloses an inspection robot collaborative planning method and system based on digital twinning, and the method comprises the steps: constructing a virtual simulation environment of a large-scale component through a point cloud modeling technology, and integrating a motion capture system to achieve the real-time mapping of a physical environment and a virtual model; defining a cooperative mechanism of the scanning robot and the positioning robot, providing global coordinates and path guidance by the positioning robot, and correcting position errors in real time by the motion capture system; simulating a dual-machine motion track in the digital twin model, and identifying a potential collision risk through a conflict prediction model; designing a collaborative planning algorithm under coupling constraint, and performing optimization by taking path optimization and scanning visual angle coverage as a joint target; based on a twin intelligent framework with a body, the path and the visual angle of the robot are dynamically adjusted by combining reinforcement learning with real-time feedback, and self-adaptive collaborative planning is achieved. Through double-machine division cooperation and real-time correction, the scanning efficiency and precision of the large-size component can be remarkably improved, and the collision risk is reduced.

The invention provides an intelligent inventory robot path optimization method based on environment modeling and collaborative optimization, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: scanning a warehouse region through an unmanned plane platform, and generating three-dimensional warehouse point cloud data; after point cloud denoising, plane fitting, shelf boundary extraction and channel detection processing, a two-dimensional storage plane graph is generated and transmitted to a robot data base; generating an initial inventory planning path by adopting an A * algorithm, optimizing the initial path through a simulated annealing algorithm, and generating a target inventory planning path; based on the dynamic obstacle influence factors, the RFID signal intensity and the robot residual electric quantity are combined, and a target path is corrected through a mixed algorithm of multi-target optimization and PSO dynamic weight; and the operation end checks the checking progress through the AR equipment and manually adjusts the target path, so that the checking path of the intelligent checking robot can be optimized in real time in a dynamic environment, and the checking efficiency, the coverage rate and the cruising ability of the robot are improved.

The invention provides an inspection robot path optimization method and system based on dynamic obstacle avoidance, and the method comprises the steps: enabling an inspection robot to carry out the multi-layer convolution processing of gas and wind direction data through obtaining the turbulence data of a gas leakage source, the wind direction data of a meteorological station, and environment reflection feature data in real time, and generating a high-precision gas diffusion prediction map, meanwhile, a transparent obstacle optical contour and an unstructured obstacle geometric parameter are extracted from reflection data, and a three-dimensional space mapping relation with a gas diffusion diagram is established. On the basis of the mapping relation and the preset task priority, path control parameters are dynamically generated, global path node distribution is optimized, and the robot path is made to adapt to the composite risk environment of gas concentration sudden change and obstacle movement in real time. According to the invention, the dynamic obstacle avoidance precision and the safety response speed of the inspection robot path in a complex dangerous environment are improved.

The invention relates to the technical field of robot scheduling, and discloses an environmental sanitation robot scheduling method, device and equipment based on environmental sanitation task distribution, and the method comprises the steps: collecting the garbage accumulation information and people flow density data of a plurality of urban environmental sanitation regions, and carrying out the task distribution prediction through a space-time convolution network model, an environmental sanitation demand prediction result is obtained; according to the environmental sanitation demand prediction result, creating an urban region division scheme of a plurality of urban environmental sanitation regions and robot initial grouping configuration information; according to the urban area division scheme and the initial grouping configuration information of the robots, dynamic scheduling grouping of the sanitation robots is calculated in combination with the real-time garbage accumulation event data; the path planning and cleaning operation instruction of each environmental sanitation robot is output in groups based on dynamic scheduling of the environmental sanitation robots, intelligent planning of the paths of the environmental sanitation robots is realized, and the collaborative operation efficiency and stability of a multi-environmental sanitation robot system are ensured.

The invention discloses a sensing optical cable laying robot path planning and obstacle avoidance system and method, and belongs to the technical field of robot path planning. The system comprises an environment sensing module, a data preprocessing module, a path planning module, an obstacle avoidance control module and a post-processing module. According to the method, environment information is acquired and preprocessed through a laser radar and a camera, path planning is performed based on an improved dynamic RRT algorithm, a self-adaptive step length adjustment mechanism is introduced, the step length is dynamically adjusted according to environment complexity, obstacle distance and path curvature, and a smooth path is generated by combining real-time obstacle detection and dynamic re-planning functions. A PID control algorithm is adopted to adjust the speed and the steering angle of the robot to achieve obstacle avoidance, and a post-processing module optimizes an evaluation path. The real-time performance, the path quality and the environment adaptability of the robot in a complex dynamic environment are improved, and efficient, safe and accurate laying of optical cables is ensured.

The invention discloses a self-adaptive Gaussian mixture mechanical arm path planning method considering collision escape, and aims to solve the problems of low sampling efficiency, low convergence speed and poor path quality of mechanical arm path planning in a complex environment. The method mainly comprises the following steps: introducing a Gaussian mixture model for dynamic weight adjustment, wherein the model fuses four sampling strategies of target guidance, obstacle avoidance, uniform exploration and collision escape; the weight of each component is adaptively adjusted by evaluating the number of continuous collision times, the distance from the target and the like, so that blind search and heuristic exploration are dynamically balanced, and the sampling efficiency in a complex environment is remarkably improved. In addition, adjacent point search is accelerated by using two-dimensional spatial index, calculation resource allocation is optimized in combination with an early stop mechanism, and the quality of a final path is ensured through path simplification and smoothing. The result shows that the method has remarkable advantages in the aspects of path sampling efficiency, convergence speed and path smoothness.

The invention discloses a synchronous localization and mapping method based on PID real-time correction fusion vision, and relates to the technical field of automatic control and environmental perception, and the method comprises the steps: collecting the environmental data of a robot in real time, and carrying out the preprocessing; the direction angle of each feature point is extracted and calculated based on the environment data, and path information is calculated; a state space and an action space are defined based on the path information, and a control signal of the robot is adjusted through an SAC-PID controller; and performing visual SLAM by using ORB-SLAM to output a global consistent map. According to the method, the robot path is intelligently optimized and adjusted and dynamically corrected through the SAC-PID controller combined with reinforcement learning, so that the precision and stability of path tracking are improved, a global consistent map is constructed and optimized through the ORB-SLAM technology, the navigation ability of the robot in a complex dynamic environment is further improved, and the navigation efficiency of the robot in a complex dynamic environment is improved. The self-adaptability of the controller is enhanced, the environment perception technology is optimized, and the defects of insufficient control precision and poor map consistency in the prior art are overcome.

The invention discloses a robot redundant attitude planning method and system based on position and obstacle constraints, and the method comprises the following steps: generating a planning path of a robot based on a redundant attitude space and a known robot path position point, the planning path being represented by a position space and an attitude space; constructing an approximate key collision plane; calculating a feasible attitude area based on the obstacle information and the redundant attitude space on the approximate key collision plane; estimating a preferential sampling attitude area; continuously sampling in the position space and the attitude space, acquiring a pose node to expand the random search tree until a target node is reached, and acquiring a target path; and iterating the random search tree extension operation to obtain more target paths, and selecting a target path with the lowest total cost from all the obtained target paths. According to the method, the exploration efficiency of redundant attitude planning can be improved, the integrity and safety of the process are ensured, and the method has relatively high applicability.

The invention provides a quadruped robot path optimization method and system, and the method comprises the steps: building an overlapping operation window recognition mechanism through obtaining the state information and topographic data of a robot, generating a risk map based on a conflict probability, and constructing a hierarchical task chain; establishing path-terrain mapping, identifying a vortex region through an energy flow channel and flow field rotation characteristics, and forming a path dominant region; performing task density analysis on the dominant region, and forming a complementary rhythm through peak-valley separation and frequency analysis to generate an equilibrium flow; main and auxiliary path assignment is carried out on the balanced flow, and high-density path reserve is formed through load analysis and compression processing; performing risk detection on the main path to obtain an early warning, triggering reserve release according to the early warning, and performing cross weaving with the main path to form a reinforcing network; a cooperative propagation matrix is constructed based on a reinforcement network, flow field diffusion analysis is carried out to generate discrete path configuration, a convergence center is established, a unified control command is output, and the cooperative path optimization problem under the complex terrain is effectively solved.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention provides an artificial intelligence-assisted intelligent control decision method, which comprises the steps of obtaining real-time environment perception data, constructing an obstacle group motion intention prediction model by adopting a deep reinforcement learning algorithm, determining intention probability distribution and a prediction time window of an obstacle group, and obtaining a short-term motion trend of the obstacle group; according to prediction results of the trajectory deviation angle deviation and the deviation acceleration peak value, a robot path priority order is determined, and a candidate path set is generated; acquiring a navigation instruction of an optimal path, and adjusting motion parameters of the robot to obtain new environment sensing data; and according to the updated short-term movement trend of the obstacle group, circularly adjusting the path priority order, generating a new navigation instruction, and obtaining a continuously optimized robot movement track.

The invention discloses a multi-robot control system, and relates to the field of robot path planning. According to the system, in a robot path driving task, candidate path nodes are selected by randomly switching target guidance and a global exploration mode, feasible nodes and path segments are determined by combining historical path nodes, and the path nodes are expanded after collision detection of a static obstacle and a dynamic robot. The system also dynamically adjusts a driving step length based on a load rate, sets a safety margin including load compensation, and realizes multi-machine cooperation through local path topological optimization and distributed decision. According to the method, the multi-robot path conflict and collision risk is effectively reduced, the heavy-load working condition motion stability and the system cooperation efficiency are improved, and the method is suitable for a multi-robot cooperation operation scene.

The invention discloses a robot automatic obstacle avoidance target recognition system and method, and relates to the technical field of autonomous navigation, and the method comprises the steps: obtaining and fusing an obstacle boundary map, a passable region mask and a dynamic target hot area map, and outputting an environment feature map; inputting the environment feature map into a pre-trained deep reinforcement learning model, and generating an obstacle avoidance instruction set according to the real-time state of the robot and a global path planning result; and acquiring actual path data of the robot, comparing the actual path data with an expected path point by point, and if the continuous deviation feature exceeds a preset continuous deviation feature threshold, inputting the environment feature map, the obstacle avoidance instruction set and the continuous deviation feature into the deep reinforcement learning model for updating. According to the invention, the obstacle avoidance capability of the autonomous robot in a dynamic complex environment is improved through multi-modal sensing fusion and a closed-loop dynamic optimization mechanism.

The invention provides a magnetic suspension underwater robot path following control method, a magnetic suspension underwater robot path following control device, magnetic suspension underwater robot path following control equipment and a medium, and relates to the field of underwater robot path following control. The motion control bottleneck of a traditional underwater robot in a complex environment is broken through. In the aspect of dynamic modeling, interaction between magnetic force and fluid resistance is considered, and underwater motion characteristics of the robot are accurately described. A nonlinear control strategy combining the Lyapunov stability theory and sliding mode control is adopted, and a trajectory tracking controller with high robustness is designed to ensure that the robot can stably and efficiently operate along a predetermined trajectory in a complex underwater environment. The maneuverability and the operation precision of the underwater robot are remarkably improved, a more reliable solution is provided for the fields of ocean exploration, resource development, environment monitoring and the like, and huge application potential and market value are shown.

The invention discloses an underwater robot path tracking control method and device, terminal equipment and a storage medium, and belongs to the technical field of path tracking, and the method comprises the steps: obtaining the current pose data of an underwater robot and the path parameters of a to-be-tracked path; then, according to the current pose data and the path parameters, current operation state parameters of the underwater robot are obtained through calculation; according to the current operation state parameter and a preset path tracking model, determining a current target operation action of the underwater robot; and finally, adjusting the running state of the underwater robot according to the current target running action, so that the underwater robot advances according to the to-be-tracked path. By implementing the method and the device, the problem that the control precision is reduced due to the fact that a PID controller based on fixed parameters is adopted to realize path tracking in the prior art and cannot adapt to the constantly changing running state of the underwater robot in real time can be solved.

The invention discloses an orchard transportation robot path tracking system, and relates to the technical field of robot programmes, and the system comprises the steps: collecting an alignment data set, extracting a stability feature, a terrain trafficability feature and initial trajectory deviation data based on the alignment data set, generating a deviation scoring result based on the initial trajectory deviation data and the trafficability feature, and carrying out the tracking of an orchard transportation robot path. According to the method, autonomous path tracking and avoidance control of the orchard transportation robot are realized through multi-source perception and intelligent decision, positioning, attitude and environment structure data are fused, the advancing stability and terrain trafficability are analyzed in real time, and the accuracy and the reliability of the orchard transportation robot are improved. The path deviation trend is predicted and adaptively corrected, high-precision navigation in a complex orchard environment is realized, the robot can keep stable operation in a multi-machine cooperation and edge-of-field steering scene through a dynamic channel regulation and control and feedback learning mechanism, and automation and operation efficiency of orchard transportation operation are remarkably improved.

An automated storage and retrieval system includes a storage array, a plurality of bots, and a controller. The controller is connected to each bot to assign a series of tasks, or goals, to each bot. The controller has a bot route planner that has a multi-agent path finding algorithm resolver that determines, for each bot route effecting at least one task, or goal, occurrence and type of a conflict between bots performing the series of tasks, or goals. From the determination of occurrence and type of conflict, the multi-agent path finding algorithm resolver resolves each conflict free bot route, that determines the bot route respectively for each bot performing the at least one task, or goal, based on bot priority and precedence constraint between route legs describing, at least in part, the respective bot route of a common bot performing the at least one task, or goal.

The invention discloses a robot path planning method in a man-machine cooperation environment, and the method specifically comprises the steps: collecting the three-dimensional point cloud data of an assembly environment, calibrating the relation between coordinate systems in the assembly environment, and recognizing an obstacle region; a robot joint envelope body is constructed according to joint connecting rod parameters of the robot and the three-dimensional model, and robot size description and collision detection are simplified; spatial grid non-uniform discretization is carried out on the assembly environment according to the barrier distribution density, grids are coded, the assembly environment is divided into grid spaces with different coarse and fine granularities, and the path search efficiency is improved; a fitness function is designed, a target path search algorithm is established based on a genetic algorithm framework, crossover and mutation operations of adaptive probability are considered, rapid high-quality population iteration is realized, and robot path planning is completed through the target path search algorithm. The robot path planning difficulty in a complex environment is improved, and the quality and smoothness of robot path planning can be improved.

The invention discloses a multi-robot path coordination system based on reinforcement learning, and the system comprises a state collection module which is used for collecting the operation state and environment perception information of each robot, and generating an original data set; the state modeling module is used for constructing local state representation for each robot in each decision period; the opponent perception module is used for executing opponent learning perception training and outputting opponent parameter estimation vectors; the improved LOLA module is used for generating a final estimation result on the basis of self-strategy updating of the traditional LOLA; the strategy updating module is used for correcting the updating direction of the reinforcement learning strategy of the local machine and generating a feasible action set; the arbitration decision module is used for executing arbitration solution in combination with the candidate path action distribution to generate a scheduling result; and the execution feedback module is used for issuing the control action to a robot executor for execution, generating a path coordination result and storing the path coordination result. According to the invention, path coordination of multiple robots is realized.

The invention relates to a large-scale multi-robot coordination method under a complex and uncertain time sequence task, belongs to the technical field of robot task allocation, and solves the problems that the efficiency is low and the real-time requirement cannot be met due to the fact that an existing multi-robot coordination method needs frequent re-planning in a dynamic and uncertain environment. The method comprises the following steps: constructing a task constraint graph based on an inter-task constraint relationship of each to-be-completed task, and obtaining a current to-be-completed task set based on a preset single processing task number; based on the information of each task, the task constraint graph and the information of each robot, obtaining a local optimal task allocation result under the quantity of each task subgroup by dynamically adjusting the quantity of the task subgroups, and taking the optimal task allocation result as a global optimal task allocation result; based on a global optimal task allocation result, allocating each robot to each task subgroup, and planning a path of each robot based on an overall task type; and when a re-planning triggering condition is met, re-planning the uncompleted task.

The invention discloses a multi-robot path planning method for an electric arc additive manufacturing system. The multi-robot path planning method comprises the specific steps that a three-dimensional model is obtained, slicing and layering are conducted, environment information is obtained, and environment modeling is conducted; determining an ideal task load; performing matrix iteration, and optimizing the current task load; constructing a minimum spanning tree, and generating a two-dimensional optimal task path of each robot; and performing three-dimensional conversion and asynchronous execution. Compared with the prior art, the method has the advantages that the form requirement of an actual additive manufacturing path is fully considered, the process defects of air holes, insufficient fusion, slag inclusion and the like in the manufacturing process are effectively reduced, and the problems of region segmentation and deposition path planning in an electric arc additive manufacturing (WAAM) task cooperatively executed by multiple robots are successfully solved; and therefore, high-quality additive manufacturing of the component is realized. Meanwhile, the path design strategy proposed in the method avoids the start-stop operation of the welding gun, ensures the parallelism and independence of each robot path in the three-dimensional space, and further improves the operation efficiency and stability of the system.

To provide a method for generating a dataset of robot motion programs for training a path generation neural network.SOLUTION: A large language model uses a robot task library and an asset geometry database as inputs. The large language model breaks down a task into steps, and then generates code describing a robot and object motion to complete the task. The generated code produces robot motions for the task, and a corresponding robot motion program is created and executed in simulation. The simulated robot motion programs are used to generate collision-free robot paths via RRT and / or optimization, and the collision-free paths are validated for robot reachability and object placement success. The validated motion programs are added to a dataset and used for training a path generation neural network.SELECTED DRAWING: Figure 3

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 dynamic obstacle avoidance and path optimization method based on a body intelligence robot, and relates to the technical field of robot path planning, and the method comprises the following steps: collecting map data and task constraint data; performing path planning analysis on the map data and the task constraint data based on a set path optimization algorithm to generate initial planning path data; according to the intelligent robot dynamic obstacle avoidance and path optimization method, a robot actual driving path prediction model is constructed, and a target function is constructed for the deviation between the robot actual driving path prediction model and the real-time path planning data of the next time period to carry out minimization analysis; and the real-time path planning data of the next time period is optimized, so that the robot runs according to the optimized real-time path planning data to obtain an actual running path which fits the original real-time path planning data, and the running error of the robot is reduced.

The invention discloses a robot path optimization method based on an RRT algorithm and a particle swarm algorithm and a related device. According to the method, an initial path is generated based on an RRT algorithm, and a key path segment set is determined by using local features of the initial path; then, the key path segment set is used as key guidance of an initial population, and the quality and the information collaboration degree of initial path individuals are improved; according to the method, the route individuals are subjected to staged exploration optimization based on the route fitness, the search process is divided into three stages of exploration, development and balance, the global optimization capacity and search diversity in a complex environment are improved, the method can adapt to different environment complexity and planning requirements, and high-quality route planning is dynamically provided for a robot.

The invention provides a key point path planning method and device based on distance transformation, and the method comprises the steps: converting a grid map of a target region into a distance transformation map based on a distance definition method; a distance maximum value point is extracted from the distance transformation map to serve as a candidate key point, the candidate key point is screened to obtain a global key point, and at least two contact points exist between a circle generated with the global key point as the circle center and the distance value of the global key point as the radius and a grid representing a non-passing area; and constructing a connection relationship between the global key points, and searching and determining a navigation path based on the connection relationship between the global key points by using a # imgabs0 # algorithm. According to the method, the global key points are screened in the distance conversion map, and path search is performed based on the connection relation between the global key points, so that a more efficient robot path planning algorithm is realized.

Disclosed herein are a method for generating robot path data for robot path(s) to be followed by a robot including a coating tool during coating of at least part of the surface of a spatial substrate with at least one coating material spatial substrate, as well as respective apparatuses, or computer elements. Further disclosed is a robotic system for coating at least one surface of a spatial substrate with at least one coating material. The methods, respective apparatuses, or computer elements allow automated application of coating materials to substrates having a high variation in geometry and provide consistency of application in contrast to manual application of coating materials, for example during repair processes of automotives or automotive parts, which is highly dependent on the painter performing the application.

The invention provides a hybrid robot path switching fairing method based on curvature and time optimization. The hybrid robot path switching fairing method comprises the following steps that a machining path is divided into a plurality of element paths; parameterization is carried out on a switching section, a remaining section and a transition section in each primitive path section; a quantum particle swarm optimization algorithm is combined with a greedy algorithm to optimize the curvature of the switching section; the quantum particle swarm optimization is combined with a moving window planning method to optimize the machining time of each section; and interpolation is conducted on the geometric path to generate a motion program, then a servo motor driving instruction of a driving joint is obtained through inverse solution of the robot, and a machining task is completed. The method has the advantages that the corner curvature and the fairing deviation of the machining path can be optimized, the machining time can be optimized, the machining path is optimized in a segmented mode by combining the greedy algorithm and the moving window planning method, complex solution of a high-dimensional problem is avoided, and the machining efficiency is improved. And the machining quality and execution efficiency of the robot are improved.

The invention provides an unmanned picking robot path planning algorithm for a smart greenhouse, and the algorithm comprises the steps: initializing a map space, and obtaining the poses of a starting point and a target point, basic parameters of a robot, algorithm parameters, and obstacle information in the environment; the optimal distance between the robot and the target is calculated according to the obstacle information and the algorithm parameters; based on the optimal distance, the starting point and the target point position poses, an improved A * algorithm is adopted to introduce an obstacle adsorption strategy, path points are generated through cost function calculation and iteration, and an initial path is obtained; screening redundant path points in the initial path, and trimming and optimizing the path through curvature analysis and a neighborhood search method to obtain a final smooth, safe and efficient robot navigation path.