488 results about "Robot path planning" patented technology

The invention relates to the technical field of robot path planning, and discloses a robot path planning method based on reinforcement learning. The method comprises the following steps: acquiring environment depth information and an obstacle movement track through a multi-sensor array, constructing a dynamic environment sensing network, and generating an environment state tensor under space-time constraint; building a hierarchical reinforcement learning framework, and optimizing the motion track of the robot in stages by adopting a strategy gradient algorithm to obtain an initial path strategy; designing a reward function calculation model based on an attention mechanism, and accounting an action value in real time according to an environment state tensor; deploying a distributed experience playback buffer pool, and performing priority sampling and track fragment recombination on historical decision data; and establishing a strategy iterative optimization mechanism, and searching and dynamically correcting an initial path strategy by utilizing a Monte Carlo tree. The method can accurately adapt to the dynamic environment, optimize the path decision efficiency, enhance the adaptability and reliability of robot path planning, and is suitable for various robot autonomous operation scenes.

The invention belongs to the technical field of robot navigation, and discloses a robot path planning method based on a graph neural network, through causal perception dynamic graph construction and AST-GNN training, a causal relationship, such as pedestrian steering-path change, of obstacle movement is excavated, extreme scene features are learned in combination with adversarial training, and the path planning precision is improved. Therefore, when the robot is in an emergency scene (such as object falling and pedestrian sharp turning), the obstacle movement pre-judgment accuracy is improved, the path re-planning response speed is increased, the collision risk is greatly reduced, and the operation safety in a complex dynamic environment is ensured; a block chain alliance chain and an intelligent contract mechanism are adopted, path data credibility is guaranteed through ECC encryption, a PBFT algorithm rapidly reaches a consensus, an intelligent contract automatically allocates priorities, such as emergency task priority, and a path optimized through differential geometry is combined, so that the path conflict rate of multiple robots in dense scenes such as storage and venues is remarkably reduced.

The invention discloses a cable trench inspection robot path planning method and device and a medium, a cable trench three-dimensional semantic map is constructed through multi-sensor fusion, and a laser radar and a depth camera are combined to accurately identify the spatial distribution of a cable support, a suspension cable and an obstacle. An improved directional path search algorithm is adopted, firewall passing sequential logic and lifting platform kinematics constraints are integrated, and a multi-mode inspection path is generated. The environment change is sensed in real time in the inspection process, the path is adjusted online through a dynamic path optimization engine, a planning strategy is iteratively optimized based on historical data, a digital twin system is introduced to realize path pre-verification, and the firewall interaction efficiency and the exception handling capacity are optimized by adopting reinforcement learning. According to the invention, the technical problems of poor real-time performance of path planning and low reliability of facility interaction in a complex cable trench environment are solved, and the inspection efficiency and safety are significantly improved.

The invention discloses a welding robot path planning system cooperating with industrial vision and collision detection, and relates to the technical field of robot automatic welding, the welding robot path planning system comprises an upper computer, the upper computer is in communication connection with the following modules: an environment sensing module used for collecting welding environment information in real time, comprising three-dimensional point cloud data of a workpiece and obstacle information; and the data processing and fusion module is used for fusing the visual identification result and the obstacle detection data. By integrating industrial visual recognition and collision detection technologies, the dynamic change of the welding environment can be sensed in real time, an accurate environment model is constructed based on three-dimensional point cloud data, compared with a traditional welding robot with a preset path, the welding track can be dynamically adjusted, welding defects caused by environment interference are avoided, and meanwhile the welding efficiency is improved. In combination with an assembly error compensation mechanism, the deviation between a theoretical model and an actual workpiece can be corrected, the welding position precision is ensured, and the high-precision ship manufacturing requirement is met.

The invention discloses an artificial intelligence robot path planning method and system, and the method comprises the steps: 1, carrying out the comprehensive perception of an environment geometric structure, object semantics and dynamic obstacles, eliminating scene differences, and further constructing the topological graph representation of an environment; 2, pre-training a path planning model, designing a scene context encoder, encoding a scene specific rule into a low-dimensional vector, and outputting a scene context code and updated planning model parameters to provide a planning capability adapted to a new scene for the step 3; 3, dynamically adjusting a track according to real-time sensor data by adopting a hierarchical decision-making architecture; step 2, recording success / failure path segments in the new scene, regularly updating a local planning module, regularly feeding back empirical data in the new scene to the step 2, and triggering a conservative obstacle avoidance mode when the confidence coefficient is lower than a threshold value; and 4, constructing a quantitative evaluation system, building an automatic test platform, simulating diversified scenes and dynamic interference, and automatically generating a test case.

The invention discloses a robot dog inspection path intelligent planning and dynamic adjustment method, system and device and a medium, and belongs to the technical field of robot path planning, and the method comprises the steps: calculating a task emergency degree index according to a weight coefficient of an inspection point and a time constraint, and determining a task priority; constructing a hierarchical electronic map containing the terrain difficulty coefficient and the moving cost; performing global path planning through a comprehensive cost function by adopting an A star algorithm; environment information is collected in real time through multiple sensors, and the dynamic obstacle layer is updated; performing real-time trajectory optimization by adopting a local path planning mode; selecting local path correction or global path re-planning according to the path execution state score; energy needed for completing the remaining tasks is predicted, and a charging path is planned when necessary. According to the invention, multi-target adaptive optimization of path planning is realized, a coordination mechanism of global planning and local adjustment is established, and the execution efficiency and reliability of inspection tasks are improved.

The invention relates to a robot path planning method based on multi-sensor fusion and free space topology composition, and belongs to the technical field of robot autonomous navigation. The method comprises the following steps: firstly, fusing laser radar and millimeter wave radar data, constructing multi-modal point cloud data, extracting a three-dimensional obstacle boundary by combining depth and normal vector mutation features, and generating a three-dimensional obstacle map and a free region tree; according to the constructed space model, factors such as energy consumption, dynamic obstacles, path smoothness and the like are integrated, target selection weights are dynamically regulated and controlled, and self-adaptive screening of intermediate navigation targets is achieved. And based on the intermediate navigation target, generating a safe trajectory satisfying dynamic constraints by adopting geometric-dynamic dual-mode fusion modeling, and enhancing the feasibility and robustness of the trajectory through trajectory envelope optimization and pre-execution fault-tolerant control. The method can be widely applied to autonomous navigation systems such as mobile robots and unmanned vehicles, and has good environment adaptability and path execution stability.

The invention provides a concrete construction intelligent auxiliary robot path planning method and system based on Beidou positioning, and belongs to the technical field of robot path planning. In the scheme, robot pose information and construction environment information are collected through a Beidou high-precision positioning receiver, an inertial navigation unit (IMU), a laser radar and a depth camera; processing and fusing the multi-source heterogeneous data to generate high-precision pose information and an environment map; based on the fused data, analyzing the shape features of the construction area, and identifying the boundary of the construction area and the special structure in the construction area; and generating a global path according to the shape characteristics of the construction area and concrete characteristics, and performing concrete construction by the robot according to the global path. Therefore, by utilizing the positioning information provided by the Beidou high-precision positioning receiver and combining the IMU, the possible problems of the Beidou signal in a complex construction environment can be effectively compensated, and the stable, continuous and high-precision positioning of the robot in a construction area with a complex shape is realized.

The invention relates to the technical field of intelligent mobile robot path planning, in particular to a dynamic sensing path planning method for a mobile robot in an unknown environment. According to the method, an environment complexity model is established based on the obstacle density and the dynamic activity, and map partition and obstacle avoidance judgment are adaptively adjusted according to the environment complexity model; carrying out incremental updating on the near, middle and far sub-regions based on the point cloud similarity; extracting contours of the obstacle point clouds and carrying out topological optimization; dynamic candidate points are identified through double marking of rays and scanning points, and collision time is predicted and estimated in combination with spatial clustering and trajectory; and according to the collision time, hierarchical obstacle avoidance and local or global re-planning are implemented, and a repaired path is smoothed. According to the method, the path planning efficiency and the real-time obstacle avoidance capability of the mobile robot in a dynamic complex environment are remarkably improved, the consumption of computing resources is reduced, and the method is suitable for application scenes needing high dynamic response, such as warehouse logistics and unmanned driving.

The invention belongs to the technical field of inspection robot systems, and particularly relates to an inspection robot path planning method and system.The visual semantic perception module collects an industrial environment image through a top industrial camera, after graying and Gaussian filtering preprocessing, feature points are detected and matched through an ORB algorithm, and a path planning result is obtained; in combination with an illumination self-adaptive threshold screening mechanism, mismatching points are eliminated, semantics are marked, and a semantic feature map is constructed; the initial path planning module generates an initial path through a semantic cost-containing A * algorithm based on the map; the dynamic obstacle avoidance module captures a moving obstacle by using a visual sensor, and predicts a trajectory through Kalman filtering; the path optimization module combines an initial path and an obstacle track, optimizes the path by using quadratic programming of a fusion curvature constraint and an energy consumption model, and corrects positioning by fusing vision and IMU data through a dynamic weight fusion algorithm; and the execution feedback module generates an instruction according to the optimized path, re-triggers path optimization, forms a closed loop, and ensures the inspection stability.

The invention discloses a thermal power plant inspection robot path planning method, device and equipment and a storage medium, and the method comprises the steps: carrying out the semantic analysis and information extraction of an inspection task instruction, an equipment document and a historical inspection log through a large language model; obtaining a semantic importance score, a perception modal demand, recommended residence time and a shooting angle of each inspection target; establishing a multi-target path optimization model based on an improved ant colony optimization algorithm, and inputting the semantic importance score, the perception modal demand, the recommended residence time and the shooting angle into the multi-target path optimization model to obtain a joint scheduling sequence; the thermal power plant inspection robot is controlled to execute the joint scheduling sequence to perform autonomous navigation and perception tasks, the current path planning is adjusted according to the task completion condition, the intelligence, autonomy and adaptability level of the inspection robot can be remarkably improved, and the speed and efficiency of path planning of the thermal power plant inspection robot are improved.

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 relates to a multi-mobile-robot path planning method based on swarm intelligence, and belongs to the technical field of robot path planning. The method comprises the following steps: setting the size of a map, starting and target positions and colors of a robot, creating a preset grid map, and initializing an ant colony algorithm, a genetic algorithm and a pheromone system; iteratively searching paths for the mobile robots with different starting points at the same time through an ant colony algorithm; a genetic algorithm is used for optimizing the path, conflict detection and processing are carried out after the path is optimized, and it is ensured that the final path is free of conflicts; and when the maximum number of iterations is reached, outputting the shortest path that each robot arrives at the target node and no collision exists between the robots. According to the path planning method, unnecessary turning can be reduced, the convergence speed of path searching is obviously improved, robot conflicts can be avoided, and the actual requirements of multi-robot path planning are met.

The invention belongs to the technical field of robot path planning, and provides a path planning method for an electric power inspection mobile robot, which comprises the following steps of: firstly, constructing an environment grid map, establishing a corner directional expansion safety boundary, and performing safety expansion on an outer right angle of an obstacle; secondly, establishing an intelligent neighborhood selection mechanism based on fuzzy control, and performing adaptive adjustment neighborhood search; then, a multi-target heuristic function fusing the local obstacle density, the safety distance and the traffic difficulty is established, path search is carried out, and an initial path is obtained; then, constructing a path optimization strategy, and carrying out redundant point deletion and B spline curve smoothing processing on the initial path until a target executable path is obtained; and finally, optimizing an executable track in the target executable path in a time-space domain through an improved time-domain elastic band algorithm to obtain an optimal path. According to the invention, accurate obstacle avoidance of complex geometric obstacles can be realized.

The invention provides a path planning method and system based on HER-ACO, and belongs to the technical field of robot path planning. The method comprises the following steps: S1, acquiring an environment grid map, wherein the grid map comprises a starting point, a target point and obstacle distribution information; s2, introducing an HER algorithm to improve a traditional ant colony algorithm ACO, and searching a feasible path from a starting point to a target point in the grid map by using the improved ant colony algorithm as an initial planning path; the improvement comprises the following steps: introducing an HER thought and a dynamic pheromone volatilization mechanism; increasing a direction weight factor, a neighborhood obstacle factor and a future obstacle factor; and S3, smoothing the initial planning path based on the feasibility check of the linear path, removing redundant intermediate nodes, and outputting a final planning path. A simulation result shows that the scheme has remarkable advantages in a complex environment, particularly has excellent performance in the aspects of path quality and convergence speed, and is suitable for scenes such as robot navigation and concrete robot path planning.

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 discloses a wheeled robot path planning method for different scenes and a related device, and the method comprises the steps: obtaining environment data around a robot, building a global grid map, carrying out the preprocessing of the global grid map, carrying out the geometric feature recognition and angular point extraction of an obstacle, and fusing a multi-sampling strategy to generate a path search network. Path searching is carried out according to the target point and the pose planning, and a global optimal path conforming to robot driving is obtained; segments in the global optimal path are intercepted through the rolling window principle to serve as local paths to be input into a local path planning algorithm, a local optimal track is generated, and a speed instruction is output; and updating the map in the rolling window in real time, executing an obstacle avoidance task on the obstacle through the local optimal track, driving along the global optimal path again after obstacle avoidance, and moving to a target point according to a speed instruction. According to the method, the problems of large speed fluctuation, low quality of the generated path and calculation burden caused by increase of alternative path points in the current robot driving process are solved.

The application discloses a low-temperature cooling water cooling air-cooled island cleaning robot path planning method and system, and belongs to the technical field of robot control, comprising: based on the air-cooled island fin layout drawing, initializing the workspace grid, setting the reference parameter; driving the cleaning robot to collect multi-dimensional data, calculating the actual friction coefficient of the measured grid through the main friction coefficient and the torque auxiliary friction coefficient, for the unmeasured grid, linear extrapolation is carried out through the gradient to construct the real-time friction field; obtaining the ideal contact force vector, constructing the kinematics model to obtain the joint required torque, and calculating the joint angle adjustment amount, generating the control instruction and executing through the feedforward friction compensation and PID feedback control, constructing the wear amount estimation model, obtaining the current estimated wear amount, and calculating the core index, generating the rating report, continuously improving the operation efficiency, realizing the unity of the efficiency and equipment safety of the air-cooled island cleaning.

The invention discloses an industrial transfer robot path planning method based on artificial intelligence, relates to the technical field of industrial transfer robot path planning and multi-robot cooperative control, and provides a systematic breakthrough for narrow roadway passage of an industrial transfer robot through deep cooperation of the game theory and federal learning. A robot meeting scene is modeled as a limited action game, and a revenue function is designed in combination with priority weight and security constraint, so that the robot can autonomously generate a forward, yielding or keeping strategy, and bidirectional deadlock is avoided from the source; through a synchronous output mechanism of action probability distribution and a time window, the strategy performability is remarkably improved; the track layer directly generates a synchronization path on the cost graph according to the time window, and motion interruption caused by repeated sampling in a dynamic window method is avoided; even if track deviation or communication abnormity occurs, the rollback strategy can guarantee that the system recovers to be stable immediately, and continuous operation of the production line is maintained.

The invention provides a flower bud and flower recognition and positioning method for a picking robot to perform zoned picking, and belongs to the technical field of picking robots. Inputting the segmented mask into a multi-scale feature fusion semantic recognition model based on hierarchical attention aggregation to perform preliminary classification of foreground flowers and background vegetation, performing a graph cut energy minimization segmentation algorithm on the preliminary segmentation mask to construct an undirected graph, and calculating a minimum cut set to obtain an accurate flower segmentation mask; key point heat map prediction is carried out on separated flower individual masks to extract flower center point coordinates and petal tip coordinates, depth image channel information is combined to calculate three-dimensional space position coordinates of each flower, and a flower three-dimensional positioning database is established for picking robot path planning; the problem of insufficient segmentation precision caused by serious overlapping of foreground flowers and background vegetation in a color space in a flower picking scene is solved.

The invention relates to a robot path planning method, electronic equipment and a medium, and belongs to the technical field of intelligent robos.The method comprises the steps that an obtained static cost map and an obtained dynamic cost map are combined to obtain a global cost map; performing global path planning on the global cost map to obtain a global optimal path; performing local path planning based on the global optimal path to obtain a local optimal track, and controlling the robot to execute the local optimal track until a global target point is reached; and determining the dynamic cost of the local optimal trajectory, updating the dynamic cost map, further updating the global cost map, and performing path planning again. According to the method, the dynamic cost of executing the local optimal track by the robot is updated into the dynamic cost map, so that the robot can actively avoid a historical difficult passing area in the path planning process, and the navigation stability and long-term operation efficiency of the robot in a complex and narrow environment are improved.

The invention discloses a full-automatic road marking spraying robot path planning system, and relates to the technical field of road construction. The system comprises a multi-source heterogeneous sensor fusion positioning module which realizes stable and high-precision positioning of a complex scene through cooperative acquisition of four sensors, a two-stage fusion algorithm and dynamic error compensation; the reinforcement learning dynamic re-planning module responds to dynamic obstacles in real time through obstacle classification dynamic modeling, a three-factor dynamic weight reward mechanism and a rapid re-planning technology; the path optimization module is used for reducing shutdown loss through consumable equipment state and path parameter coupling optimization and dual-prediction adjustment; the multi-machine collaboration module is used for realizing task balanced distribution and accurate splicing through digital twin modeling and distributed control; and the execution control module guarantees accurate path and uniform spraying through kinetic model trajectory tracking, PID parameter dynamic adjustment and spraying parameter matching. All the modules cooperate to achieve full-automatic high-precision efficient spraying under complex road conditions.

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 for warehouse logistics cluster operation, and belongs to the technical field of path planning. The method comprises the following steps: constructing a two-dimensional model diagram by using a grid method; the ant colony algorithm is improved; an improved ant colony algorithm is adopted to plan an operation path for each logistics robot; a local obstacle avoidance strategy is carried out through a dynamic window algorithm, and whether obstacles exist around is detected; and the collision type is judged, and a corresponding collision avoidance strategy is adopted until all the robots safely and correctly arrive at the preset target point. According to the method, the pheromone concentration is initialized by introducing the Sigmoid function, blind search in the early stage is avoided, the self-adaptive slope parameter is introduced, and the steep degree of the Sigmoid function is dynamically adjusted according to environmental characteristics; meanwhile, the ant colony algorithm and the dynamic window algorithm are fused, parameters are dynamically adjusted according to the number of iterations, the obstacle rejection weight is introduced, the algorithm is prevented from falling into local optimum, and global path planning and local dynamic obstacle avoidance are achieved.

The invention provides a robot path planning method and system based on an artificial bee colony and reinforcement learning, and relates to the field of path planning, and the method specifically comprises the following steps: initializing an artificial bee colony algorithm population according to known environment information, a starting point and a target point; and performing iterative optimization on the population based on a search mechanism of an artificial bee colony algorithm. And when a preset optimization process termination condition is met, selecting a batch of high-quality solutions from the final population to form an elite solution set. And encoding the elite solution set into priori knowledge of a reinforcement learning agent to initialize a value function of reinforcement learning, and setting a reward function fusing a path length and a smoothness target. And based on the initialized value function, running a reinforcement learning algorithm to carry out strategy search and optimization so as to carry out fine adjustment on a path strategy. And after the reinforcement learning algorithm converges, outputting an optimal path. According to the technical scheme, the problem that a short and smooth path cannot be quickly generated in a complex environment in the prior art is solved.

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 provides a mobile robot path planning method based on an improved ant colony algorithm, and relates to the technical field of robot path planning, and the method is technically characterized in that the method comprises the following steps: carrying out environment modeling by adopting a grid method, and constructing a multi-target performance evaluation function; an improved ant colony algorithm is adopted to carry out optimal path search, path planning is carried out on a grid map from a starting node to a target node, and the improvement of the ant colony algorithm comprises the steps of distributing pheromone concentration by adopting a non-uniform initialization method; improving a state transition rule; differentiating a pheromone updating strategy; and smoothing the path. An initial pheromone distribution optimization strategy is introduced, heuristic information based on a triangular inequality, a steering angle suppression function and a state transition rule of artificial potential field force are combined, a differential pheromone updating strategy based on a multi-target performance evaluation index is adopted, and finally a planned path is smoothed. Therefore, the convergence speed, the path quality and the environment adaptability of the algorithm are remarkably improved.

The invention belongs to the technical field of robot path planning, and particularly discloses a robot path planning method based on large language model guidance, and the method comprises the steps: discretizing an environment space, and building a configuration space of a robot and an obstacle; calculating a collision area in real time and segmenting a free space into connected components; generating a prompt text; calling a large language model to analyze the text and outputting a recommended obstacle list; constructing a path tree; generating a new node; generating candidate paths based on new node backtracking, and allowing node poses to shift in a preset range through a tolerance verification mechanism; and when the target area is connected with the connected component, adding the path into the solution set library, and selecting an optimal solution according to a weighted cost function of a minimum path length and a total obstacle moving distance. The search strategy is optimized by using the semantic understanding ability of the large language model, the efficiency and adaptability of path planning in a dynamic environment are remarkably improved, and the method is suitable for scenes such as warehouse logistics and disaster rescue.

The invention discloses an intelligent path optimization control system for an industrial robot. The intelligent path optimization control system comprises a task input and analysis module, an environment sensing module, an intelligent modeling and path planning module, a feedback and execution module and a robot execution unit, the task input and analysis module sends a task instruction to the environment sensing module; the environment sensing module collects environment data, forms a dynamic semantic map and sends the dynamic semantic map to the intelligent modeling and path planning module. The intelligent modeling and path planning module sends a robot joint track instruction to the feedback and execution module; the robot motion unit drives the robot to execute trajectory tracking according to a motion control instruction issued by the feedback and execution module, and meanwhile, the feedback and execution module is connected with the weight coefficient input end of the intelligent modeling and path planning module to dynamically optimize the weight coefficient. Robot path planning is automatically adjusted to avoid collision, and it is ensured that the robot efficiently completes tasks.

The invention provides a space multi-arm robot path planning method and system based on reinforcement learning, and belongs to the field of space robot path planning. A simulation model of a space multi-arm robot and a path planning task environment is constructed; performing off-line task training on the space multi-arm robot by adopting a soft actor commentator algorithm of a self-adaptive entropy adjustment mechanism to obtain an optimal control strategy; the soft actor commentator algorithm comprises the steps that an objective function accumulates rewards by maximizing expectation, an entropy regular term is introduced to maximize strategy entropy, a weight coefficient of the strategy entropy is dynamically adjusted through a self-adaptive mechanism, and network parameters are softly updated through a Polyak average method; and in the simulation model, when the optimal control strategy converges, outputting the trained optimal control strategy. According to the method provided by the invention, the system can autonomously learn a positioning task capable of inhibiting the disturbance of the floating base and accurately finishing path planning through offline task training without an accurate model.