1051 results about "Robot path planning" patented technology

The invention discloses a robot path planning method based on a parallel sampling point optimization RRT algorithm. The robot path planning method mainly aims at the problems that the RRT algorithm is low in random tree search efficiency, the path is zigzag and discontinuous, and the algorithm adaptability to different environments is low. The method comprises the following steps: firstly, randomly sampling a plurality of nodes by adopting a multipoint parallel weighted heuristic sampling strategy, establishing an evaluation function introducing a distance factor and a corner factor, and selecting an optimal sampling node for expansion; a dual-tree guiding mechanism is constructed, information of newly-generated nodes is fully utilized, invalid sampling of a local space is reduced, and rapid fusion of the two trees is achieved; determining an extension step length according to the surrounding environment information of the node to obtain a collision-free path with low cost from a starting point to a target point; and then global and local rewiring optimization is performed on the initial path, a B spline curve is introduced for fitting smoothing, and finally an optimized path satisfying robot curvature constraint is generated. According to the method, the planning efficiency and the path quality can be improved, and the method has relatively high stability and robustness for different environments.

The invention discloses a logistics robot path planning method based on multi-modal perception, and relates to the technical field of robot path planning. Laser radar, visual camera and IMU data are fused, and environment state feature vectors are generated through multi-modal data synchronization and space-time alignment; the method comprises the following steps: analyzing environmental semantics by using models such as PointPill and YOLOv8, extracting dynamic characteristics, and identifying obstacles; constructing a space-time risk field, searching a path by a space-time algorithm, converting path points into a continuous trajectory, and optimizing the continuous trajectory; deviation is evaluated in real time, dynamic re-planning is triggered, and multi-robot cooperation and environment semantic understanding are included. Through multi-mode perception fusion, hierarchical planning, multi-target optimization and a cooperation mechanism, the obstacle detection accuracy and the obstacle avoidance success rate are improved, the path planning time is shortened, the energy consumption is reduced, the multi-robot conflict is reduced, the task efficiency is improved, the environment semantic understanding and task adaptive ability is enhanced, and the method is suitable for scenes such as intelligent storage and the like and has wide application prospects. The automation level is improved.

The invention provides a teaching-free welding robot path planning system based on visual autonomous learning, and relates to the technical field of industrial welding, and the teaching-free welding robot path planning system comprises a pose compensation module which is used for arranging three infrared reflective mark points on the surface of a clamp base in an L shape, collecting original point clouds of the mark points and a workpiece by using a stereo camera, and preprocessing the point clouds; generating a pose compensation matrix based on the offset of the actual coordinate and the theoretical coordinate of the mark point, and outputting a de-noising point cloud data set and a compensation matrix; and the pose correction module is used for fitting a workpiece plane equation by adopting a random sampling consistency algorithm on the basis of the de-noised point cloud data set, acting a pose compensation matrix on the workpiece plane equation, correcting a pose error caused by clamp drifting, calculating a welding seam track end point coordinate and outputting a corrected welding seam pose parameter and a scanning pose instruction. According to the method, clamp drift can be compensated, weld joint poses can be corrected, three-dimensional features are extracted, a collision-free path is planned, and the welding precision, efficiency and automation degree are improved.

The embodiment of the invention relates to robot path planning, in particular to a self-adaptive robot trajectory planning method and system based on deep reinforcement learning, and the method can completely present the motion condition of a robot by obtaining a real-time trajectory data set covering an environment interaction state and a motion attitude sequence; key features can be accurately extracted through dynamic track feature extraction processing; a self-adaptive trajectory optimization strategy is generated based on a pre-trained deep reinforcement learning strategy network, so that the strategy can be flexibly adjusted according to dynamic characteristics; incremental trajectory correction processing is carried out on the robot motion posture sequence, the trajectory can be optimized step by step, and an optimized trajectory sequence conforming to dynamic environment adaptability is generated; and the optimized trajectory sequence is synchronized to the motion control system, so that the robot can quickly and accurately execute the optimized trajectory, and the motion planning capability and execution efficiency of the robot in a complex dynamic environment are remarkably enhanced.

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 provides a robot path planning method and system based on fusion of an improved ant colony algorithm and DWA, and the method comprises the steps: building a grid map of the movement of a robot, and generating a global path of the robot in the grid map through employing the ant colony algorithm; transmitting the global path to a DWA dynamic window model to generate a dynamic window; adding unknown static and dynamic obstacles into the grid map, and setting a local target point by using a local target point setting method based on the global path; based on the local target point and the robot movement information, robot speed sampling is carried out in the dynamic window, and a simulation track of robot movement is obtained; evaluating the simulation track by using an obstacle avoidance evaluation function, and if the robot reaches a target point, obtaining an optimal path; and if the robot does not arrive at the target point, regenerating a simulation track for iterative calculation until the robot arrives at the target point. The improved algorithm can effectively improve the performance and adaptability of robot path planning in different environments.

The invention relates to the technical field of artificial intelligence, in particular to a road surface automatic inspection robot path planning method and device based on artificial intelligence, and the method comprises the steps: collecting road surface environment data in real time through various sensors, carrying out the feature extraction through a convolutional neural network based on the road surface environment data, and constructing a dynamic environment model through a graph convolutional network. According to the method, the change trend of potential road surface damage areas and obstacles is predicted, path planning and optimization are performed by using a deep reinforcement learning algorithm, after path planning is completed, the robot autonomously travels according to a planned path, and real-time adaptive adjustment is performed on the path through an online learning method so as to cope with environmental changes. Collected data and path planning information are fed back to the central control unit in real time to be stored, and a road surface health condition report is generated. According to the invention, the efficiency and the precision of the inspection task are improved, and the method has high environmental adaptability and is suitable for complex and dynamically changing road environments.

The invention provides a multi-modal image analysis-based robot-assisted wound treatment method and system, and relates to the technical field of robots, and the method comprises the steps: collecting a multi-modal image of a to-be-treated wound, determining an image registration result, obtaining a wound region image through a multi-scale fusion algorithm and wound region segmentation, and carrying out the type classification of wound tissues. A wound tissue type classification result is obtained; performing surrounding shooting on a wound area to obtain a wound multi-view image, registering subsequent depth maps frame by frame by taking a first frame of depth map as a reference, accumulating point cloud data, determining a wound three-dimensional point cloud, generating a wound three-dimensional reconstruction result by combining a curved surface evolution algorithm, and determining a wound parameter measurement result; based on the wound tissue type classification result and the wound parameter measurement result, a robot path planning algorithm is adopted, the optimal movement track of the robot is generated, the movement parameters of the robot are adjusted in real time through a robot control algorithm of vision and force feedback, and wound adjuvant therapy is completed.

The invention discloses a robot path planning and obstacle avoidance method combining an artificial potential field and reinforcement learning. The method comprises the following steps: carrying out sensor data acquisition and fusion; environment complexity evaluation is carried out, a multi-scale planning mechanism is established, and global planning and local planning are determined based on switching conditions; carrying out artificial potential field path planning based on global planning; carrying out reinforcement learning path planning based on local planning; and judging whether the target point is reached, and returning to the determined plan until the target point is reached. According to the invention, the data of the infrared sensor and the laser radar are fused, the complementary characteristics of the two sensors are fully utilized, the comprehensive perception of static and dynamic obstacles is realized, and the adaptability of the robot in different environments is improved. According to the method, through reinforcement learning, strategy optimization and uncertainty estimation, the obstacle avoidance capability of the robot in a dynamic environment is enhanced, and the local optimum problem of a traditional artificial potential field method is solved.

The invention discloses a seabed tracked robot path planning method and system based on reinforcement learning, and the method comprises the steps: obtaining environment data in real time through multiple sensors, extracting submarine topography three-dimensional features, obstacle distribution and ocean current dynamic parameters in combination with a neural network, and carrying out the combined feature extraction; establishing a seabed environment simulation model, and synthesizing various landform training data by using an adversarial generation technology; a hierarchical reinforcement learning framework is designed, a global layer collaboratively optimizes a long-distance path through a distributed agent, a local layer designs a high-frequency control strategy, and the global and local strategies realize multi-dimensional collaborative optimization through a dynamic weight adjustment mechanism; model parameters in a dynamic environment are updated in real time through an online optimization module, and a simulation strategy is quickly deployed to an entity robot through transfer learning. According to the invention, a neural network feature extraction and multi-level reinforcement learning collaborative autonomous decision-making system is constructed, and a high-reliability and low-energy-consumption autonomous operation solution is provided for a deep sea operation scene.

The invention relates to the technical field of path planning, and discloses a robot path planning method, which comprises the following steps that a hybrid cost map is constructed, and the hybrid cost map comprises a static obstacle field, a dynamic obstacle probability prediction field and a terrain energy consumption field; constructing an anisotropic heuristic function, and searching on the mixed cost map to obtain an initial path; parameterizing the initial path into a group of piecewise polynomial curves, constructing a joint optimization target which takes the total curvature, the piecewise polynomial curves and the space-time overlapping integral of a dynamic obstacle probability prediction field, and performing trajectory optimization under the condition of meeting the kinematics constraint of the robot to obtain a smooth space-time trajectory; when the task is executed, the information divergence is continuously calculated, and when the information divergence exceeds a preset threshold value, the current state of the robot serves as a new starting point, and the complete path planning process is executed again. According to the method, the future collision risk is prospectively avoided, the energy consumption of the robot is considered, and the comprehensive quality of the path is improved from the source.

A method for robot path planning using skills extracted from human-taught motion programs applied to a new obstacle environment. A three-dimensional convolutional neural network is used to extract features characterizing an obstacle environment, where the feature vector representation of the obstacles overcomes problems encountered when using point cloud obstacle data. The obstacle feature data and robot path start and goal points are provided to an encoder / decoder neural network system which is trained to extract skills from a database of human-generated motion programs. The encoder / decoder neural network system produces a distribution of waypoints for the current obstacle environment and start / goal points. The distribution of waypoints is used to perform a final collision-free path generation using either a rapidly-exploring random tree (RRT) technique or an optimization-based technique.

The invention provides a robot path planning method for mixed-flow water turbine runner blade defect inspection. The robot path planning method comprises the following steps that S1, feasibility analysis of robot defect inspection is conducted on a blade, and an effective detection area is determined; s2, extracting a three-dimensional mesh model of the blade surface from the effective detection area, and establishing bidirectional mapping between a three-dimensional space and a two-dimensional UV parameter space by projecting the three-dimensional mesh model to the two-dimensional UV parameter space; s3, generating a boundary offset path of the two-dimensional UV parameter space and a robot arch scanning path; and S4, based on the bidirectional mapping, processing the boundary offset path and the arch scanning path of the robot, and generating a path back projection and three-dimensional detection track. Through accurate curved surface parameterization, an efficient path generation algorithm and an accurate three-dimensional mapping strategy, the method aims at improving the detection efficiency and quality of the runner blade of the mixed-flow water turbine, and technical guarantee is provided for safe and stable operation of a hydropower station.

The invention discloses an intelligent storage robot path planning method, and belongs to the technical field of intelligent robots, and the method comprises the steps: improving a bidirectional A-Star algorithm, and combining a Time Elasticic-Band (TEB) obstacle avoidance algorithm, and providing a new intelligent storage path planning method. The improved A-Star algorithm adopts bidirectional search to replace unidirectional search of a traditional A-Star algorithm; 5-neighborhood search is adopted to replace 8-neighborhood search; introducing a self-adaptive dynamic search factor to improve a heuristic function; and smoothing the path by adopting a redundant node removal and linear interpolation method. The improved bidirectional A-Star algorithm searches to obtain a global optimal path, and the TEB algorithm optimizes and adjusts a local path according to the global path to complete dynamic obstacle avoidance. According to the method, the speed and precision of path planning are effectively improved, the local path can be adjusted in real time to avoid dynamic obstacles, and the requirement for path planning of the intelligent storage robot is met.

The invention relates to the technical field of path planning, in particular to a pipeline robot path planning and detecting method based on multi-data fusion, which comprises the following steps: collecting data from vision, ultrasound, magnetic flux leakage and laser radar, performing timestamp alignment and space coordinate correction on the data, performing data fusion and checking the consistency of the data, an environmental data set is generated. According to the method, by constructing the three-dimensional pipeline model, not only is visual representation of the pipeline provided, but also the understanding of the internal structure of the pipeline is enhanced, so that a maintenance team can make a repair strategy more effectively, and the robot is allowed to check in the most effective sequence through the access priority determined through cost-benefit analysis and the optimized access path; the detection time is shortened, the operation cost is reduced, instant fault diagnosis and quick response are ensured, the detection accuracy and the operation efficiency of the robot are remarkably improved, the pipeline detection efficiency and accuracy are remarkably improved, manual intervention is reduced, and the maintenance cost is reduced.

The invention belongs to the technical field of path planning, and particularly relates to a robot path planning method based on an RRT improved algorithm, and the method comprises the steps: obtaining environment space information needing path planning, defining parameters, and obtaining the minimum distance between a coordinate point and an obstacle; a bidirectional random tree is constructed and initialized, and sampling points are obtained through a sampling function; finding a nearest neighbor point, selecting a new node, carrying out safety distance judgment, and if not, carrying out sampling again; generating a discretization path; setting a cost function during local node expansion; performing loop iteration to generate discrete point cost, taking a minimum cost value, searching an optimal node and inserting the optimal node into the current tree; an overall path Path after local optimization is obtained; and discretizing the whole Path, setting a cost function of nodes during global planning, selecting the node with the minimum cost for reconnection, and finally obtaining a smooth and effective optimal path. According to the method, the optimality and smoothness of the path are effectively improved.

The invention discloses a path planning method based on an improved Hybrid A * algorithm and a DWA algorithm. The path planning method specifically comprises the following steps: establishing a grid map for a working environment and setting task starting and ending points; improving a heuristic function in the A * algorithm; the cost function of the Hybrid A * algorithm is improved, the path cost in the improved A * algorithm is used as the complete heuristic constraint cost with obstacles, the extension distance of the Dubins curve is used as the incomplete constraint heuristic cost without obstacles, and a variable step size search strategy based on the obstacle distance is adopted; smoothing the path searched by the global path planning algorithm; extracting path key nodes; optimizing a DWA local path planning algorithm; and outputting the optimal motion path. The efficiency of path planning of the workshop inspection robot in the unstructured complex environment is effectively improved, the real-time performance and safety are enhanced, and the workshop inspection robot can dynamically avoid obstacles while finding the optimal path in the complex environment.

The invention discloses a multi-robot layered space-time optimization path planning method based on conflict resolution, and relates to the technical field of multi-robot path planning, and the method comprises the steps: constructing a semantic topology and a grid hierarchical graph of a multi-robot operation environment, and obtaining a robot preset path; robot path conflicts are detected and classified; calculating corresponding dynamic priorities of the robots with the path conflicts; evaluating the total cost of each conflict resolution strategy of the robot path conflict; obtaining an optimal conflict elimination strategy of each robot according to a principle of minimum total cost; according to the corresponding dynamic priorities of the robots, to-be-processed robots are screened out; and performing path re-planning on the to-be-processed robot based on the optimal conflict elimination strategy of the to-be-processed robot and a preset path. According to the method, the robots with low priorities can be re-planned with the lowest cost, and finally robot conflicts are eliminated.

The invention discloses a distributed multi-mobile robot path planning and obstacle avoidance method based on ACO-DWA, and belongs to the technical field of path planning. The invention discloses a distributed multi-mobile robot path planning and obstacle avoidance method based on ACO-DWA. The method comprises the following steps: constructing an environment model; performing global path planning by using an ant colony algorithm ACO; establishing a motion model of the robot, and selecting an optimal speed group; and designing a conflict judgment rule, introducing a priority strategy, and solving conflicts according to the importance and urgency of robot tasks. According to the distributed multi-mobile-robot path planning and obstacle avoidance method based on ACO-DWA, an improved dynamic window method (DWA) and an ant colony optimization (ACO) algorithm are utilized, effective local path planning and global path optimization are achieved in the environment with high-density obstacles and unknown static obstacles, and safe obstacle avoidance of the robots is ensured. Meanwhile, a motion conflict judgment rule and a priority strategy in the multi-robot system are provided to coordinate the motion between the robots and between the robots and obstacles, and safe and efficient navigation is ensured.

The invention provides an improved local path planning method suitable for a multi-robot inspection task, and belongs to the technical field of robot path planning. The limitation of path planning during cooperative work of multiple robots in a traditional DWA algorithm is solved; the method is based on a dynamic window method DWA and introduces relative velocity obstacle RVO soft constraint, and comprises the following steps: generating a dynamic window and candidate tracks based on the dynamic window method DWA; constructing an RVO relative speed obstacle area: acquiring the relative speed and position between the robot and other robots in real time, and constructing a relative speed obstacle area for representing a potential collision speed area; calculating a multi-target evaluation function: comprehensively scoring the candidate tracks by adopting course angles, speeds, distances and safety; path selection: selecting the candidate trajectory with the highest score as the optimal path according to the score of the multi-target evaluation function, so that the robot safely and efficiently approaches the target point; the method is suitable for multi-robot cooperative obstacle avoidance.

A method of dust pollution monitoring and risk forewarning for a dry bulk port based on an inspection robot belongs to the technical field of environmental monitoring. The method plans an inspection route of the inspection robot in the stage of the inspection robot path planning, and makes the inspection robot perform an inspection task automatically along the planned route; in the stage of data acquisition, transmission, and processing, a multi-source environmental monitoring module installed on the inspection robot is used for monitoring the concentration of dust pollutants in the air and environmental parameters in real-time; and in the stage of data analysis and risk forewarning, the monitoring data is analyzed and visualized to judge a possible over-standard risk of dust pollutant concentration and perform forewarning. The present invention can improve the resolution and flexibility of dust pollution monitoring in the dry bulk port.

The invention discloses a robot path planning perception method and system based on a large language model. The method comprises the following steps: acquiring an initial data set; selecting an SLAM algorithm to process the initial data set; performing rasterization processing on the first map; performing semantic annotation processing on the second map; performing path planning according to the large language model to generate a path planning scheme; performing conversion according to the path planning scheme to obtain execution information of the robot; target sensing information and environment information of the robot in the moving process are obtained, and a motion planning adjustment scheme of the robot is generated through a large language model according to the target sensing information and the environment information; and a man-machine interaction module is constructed to realize information interaction between the user and the robot. According to the embodiment of the invention, a more reliable and smoother path can be generated, the navigation and perception capabilities of the robot in a complex environment are improved, and the method can be widely applied to the technical field of computers.

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 ship plane segmented welding multi-robot path planning system based on visual inspection, and relates to the technical field of ship manufacturing, the ship plane segmented welding multi-robot path planning system comprises a multispectral visual inspection module which is mounted on a portal frame and comprises a laser scanning sensor and an infrared thermal imager, the system is used for collecting three-dimensional point cloud and temperature distribution data of ship plane segments in real time. According to the ship plane segmented welding multi-robot path planning system based on visual inspection, the problem of path planning misalignment caused by assembly errors, thermal deformation and complex working conditions in large ship plane segmented welding is fundamentally solved through a multispectral visual real-time perception and material self-adaption dynamic correction mechanism. A composite correction vector and space-time grid collaborative pre-judgment technology is adopted, the welding seam track precision is effectively improved, it is ensured that the weld leg size strictly reaches the standard in the high-difficulty processes such as wrap angle welding and vertical position welding of the maximum-size workpiece, the defects such as air holes and cracks are eliminated, and the rework rate and the quality risk are greatly 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 path planning method based on an artificial mouse travel algorithm. The method comprises the following steps: constructing a map; the method comprises the steps of parameter setting, initialization, initial path fitness evaluation, global search, local search, new path evaluation and local enhancement search, a dynamic adaptive search radius strategy is used for dynamically adjusting a search range C, and when an exploration-development switching threshold value E is larger than 1, namely Egt; the method comprises the following steps: 1, starting global search by adopting a dual-mode hybrid strategy, Elt; the method comprises the following steps: 1, starting local search, adopting an adaptive probability Levy flight or small-range disturbance strategy, introducing a local search enhancement module, carrying out refined search on a current global optimal path Xbest through a simplex method Nelder-Mead, and evaluating the fitness of the optimal path of iteration each time through the algorithm; and outputting an optimal path sequence according to the fitness value of the recorded optimal path. The method has the characteristics of improving the search capability, obtaining the optimal path and the like.

The invention discloses a robot optimal path planning method based on an improved angle skid optimization algorithm, and the method comprises the steps: designing a single-target optimization problem under a constraint condition based on the angle skid optimization algorithm, setting a target function as the shortest distance between a starting point and an end point, and determining the constraint condition; a good point set strategy is adopted to replace random node initialization distribution, and an adaptive dynamic weight, a dynamic weight, an adaptive coefficient, a Brownian motion random disturbance parameter and a random walk and adaptive t distribution disturbance strategy of step length adjustment are introduced. A hiding behavior, a blood jetting behavior, an escape behavior, a skin blackening and brightening behavior and a melanocyte stimulating hormone (MSH) rate formula of the algorithm are optimized respectively, and a planned path is optimized through the improved HLOA algorithm. Compared with the prior art, the method has the advantages that the convergence speed, the optimization precision and the global search capability of the angle exid optimization algorithm in robot path planning are improved, and the method is suitable for optimal path planning in a complex environment.

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.