1578 results about "Path search" patented technology

The invention provides an intelligent question and answer method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, the method comprises the following steps: S1, integrating multi-source heterogeneous data to construct an initial knowledge graph; s2, analyzing the questions of the user, executing single-hop query and judging whether a result meets requirements or not; if not, entering S3, performing multi-hop dynamic search based on an adaptive path extension algorithm, and generating a reasoning path and evidence; s4, calling a large language model to combine with an attention mechanism to perform semantic matching on a result or a path, and screening an optimal answer; and S5, correcting the knowledge graph and performing closed-loop feedback to the query process to realize continuous optimization. Through cooperation of the knowledge graph and the large language model and combination of dynamic path search and semantic matching, the problems that a traditional method is insufficient in knowledge coverage, low in reasoning efficiency and poor in answer interpretability are solved, the method has the advantages of being efficient, accurate and self-optimized, and the performance and user experience in a complex scene are remarkably improved.

The invention relates to a multi-objective optimization scheduling method and system for a multi-AGV flexible job shop. The method comprises the steps of collecting job shop layout data, AGV operation parameters and production task data, and constructing a scheduling system basic model; performing recurrent neural network training and physical constraint model integration to obtain a hybrid scheduling prediction model; subtask decomposition and path search processing are carried out, and a dual-objective optimization model is obtained through constraint linearization conversion; performing particle swarm optimization solution on the dual-objective optimization model, and obtaining a non-dominated solution set through two-stage iterative calculation of an adaptive weight strategy and constraint conversion; performing multi-dimensional evaluation calculation on the non-dominated solution set to obtain an optimal scheduling execution scheme; and performing model feedback updating on the hybrid scheduling prediction model to obtain a target scheduling prediction model. According to the invention, multi-target balance of task completion time and energy consumption is realized, and the real-time response capability and operation stability of the system are improved.

The invention discloses a laser cutting machine track searching path planning optimization method and system, and relates to the technical field of laser cutting, the laser cutting machine track searching path planning optimization method comprises the following steps: analyzing geometrical characteristics of a to-be-cut workpiece, and planning an initial cutting path of a laser cutting machine in combination with processing drawing information; optimizing the initial cutting path by using a path optimization algorithm to generate a priority cutting path; a cutting head optimization algorithm is introduced, and a laser cutting head is controlled to avoid a repeated cutting area; cutting interference factors influencing the workpiece in the cutting task are analyzed, and the error influence range of each cutting interference factor on the workpiece is judged; and correcting the error of the laser cutting head in real time by using an error correction model based on the error influence range. According to the method, the propagation path and characteristics of the laser beam can be more accurately considered, the cutting path is optimized, and then the positioning and tracking accuracy of the laser cutting head is improved.

The invention provides an autonomous navigation path planning method, device and equipment for a humanoid robot in a complex environment. A navigation task is determined by obtaining the current position and path requirement of the robot; constructing a constraint preposition map based on body size constraint, joint motion constraint and dynamic balance constraint, and calculating a feasible motion space of the robot in advance; acquiring environment sensing data for variation trend analysis, and predicting a future movement track of the dynamic obstacle to generate a dynamic environment prediction sequence; establishing a bidirectional planning mechanism, performing forward and reverse path search at the same time, and generating a plurality of candidate path hypotheses through meeting point detection and path fusion; analyzing a human behavior pattern to identify a potential passage conflict area, establishing a social negotiation strategy, and taking an intermediate meeting point as a social key node to adaptively adjust a candidate path; the optimal safety path is determined through multi-dimensional safety evaluation of the falling risk, the collision risk and the energy consumption risk, and intelligent autonomous navigation of the humanoid robot in a complex environment is achieved.

The invention relates to the technical field of machine learning, in particular to a heating fault prediction method and system for a high-voltage circuit breaker and an isolating switch of the high-voltage circuit breaker. The method comprises the following steps: carrying out multi-source data acquisition on a high-voltage isolation switch and an associated high-voltage circuit breaker through an SCADA system, and carrying out data preprocessing to obtain a preprocessed data set; performing operation feature correlation analysis on the preprocessed data set to obtain a correlation operation feature set; performing dynamic association network construction on the association operation feature set to obtain a dynamic association network graph; acquiring historical fault data; performing propagation risk path search according to the historical fault data and the dynamic association network diagram to obtain a candidate risk path set; and performing path propagation probability calculation on the candidate risk path set to obtain a path probability data set. According to the method, the influence of the associated high-voltage circuit breaker on the heating risk of the isolating switch is effectively identified, so that the accuracy of fault prediction and the timeliness of early warning are improved, and a more reliable guarantee is provided for safe and stable operation of a power system.

The invention relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

The invention relates to a time delay optimized satellite network adaptive routing method and system, and belongs to the technical field of satellite communication. The method comprises the steps of multi-hop information age construction, multi-path routing, link state sensing, flow segmentation adaptive routing, dynamic routing decision and the like. A multi-hop information age model is adopted to consider the time-varying property of a link state, the real-time property of path information is accurately estimated, and the timeliness of data updating is ensured; a multi-path search algorithm predicts link occupation frequency according to ephemeris information to construct a plurality of disjoint transmission paths, so that the traffic congestion problem is relieved, and the link resource utilization rate is optimized; a flow segmentation adaptive routing algorithm adjusts the path flow distribution proportion, reduces the data packet loss rate and the information age, and guarantees the stability of time delay sensitive data transmission; according to the method, resource utilization is balanced in a high-dynamic satellite network, link congestion is effectively prevented, the overall transmission efficiency and reliability are improved, and a high-adaptability and efficient transmission scheme is provided for time delay sensitive services.

The invention discloses a robot path planning method and system and a storage medium, and the method comprises the steps: building an environment topology model based on sensor data and map information, building an adaptive map data structure, carrying out the global planning of a rough level through employing a low-resolution map, and carrying out the local optimization of a detail level through employing a high-resolution map. Adaptive dynamic switching is carried out between the two levels according to a path planning task; constructing a two-way alternate search strategy, and performing path search by combining an environment topology model and map information; environment change is monitored in real time, the connectivity probability of each edge in the probability graph is dynamically updated, and a path search strategy is adaptively adjusted according to the change of the probability graph; and establishing a backup path selection model based on reinforcement learning, and dynamically adjusting a backup path in combination with environment change and main path search. Through a bidirectional alternate search strategy, the path search efficiency is improved, the problem of path repetition caused by bidirectional search is solved, and the optimal path from the starting point to the end point can be found more quickly.

The invention relates to the field of airline planning, and discloses a digital twin system for low-altitude airline planning and a construction method, and the method comprises the steps: obtaining a spatial perception data set of a preset sensor in a target airspace, carrying out the fusion of the spatial perception data set, obtaining the fusion perception data, obtaining the historical flight hidden danger data of the target airspace, and obtaining the historical flight hidden danger data of the target airspace; performing multi-layer convolution on the fusion perception data by using the historical flight hidden danger data to obtain a risk analysis result, performing fuzzy fusion on preset risk factors, generating a dynamic obstacle risk field according to the risk factors after fuzzy fusion and the risk analysis result, performing flight simulation on the dynamic obstacle risk field, and obtaining a flight simulation result; and collecting flight simulation real-time data in the flight simulation process, generating an air route space cost map according to the flight simulation real-time data, and performing optimal path search on the air route space cost map to obtain a low-altitude air route scheme. In the face of airspace actual risks, the searching efficiency of the optimal path in low-altitude route planning is improved.

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

The invention discloses a quadruped robot obstacle avoidance control method and system based on path planning, and relates to the technical field of quadruped robot obstacle avoidance control, and the method comprises the steps: obtaining environment point cloud data, image data and quadruped robot motion speed data, and carrying out the terrain semantic segmentation and semantic pixel back projection of the image data, thereby obtaining a quadruped robot obstacle avoidance result; generating semantic point cloud data, combining the semantic point cloud data with the environment point cloud data subjected to motion compensation, and constructing a semantic annotation grid map; performing global path search on the semantic annotation grid map to generate a global smooth path; when the quadruped robot advances along the global smooth path, path nodes on the global smooth path are extracted at a fixed step pitch, the terrain category and the grid occupation state of each path node are judged according to the semantic annotation grid map, and a local reference path is generated; and executing a multi-stage obstacle avoidance strategy on the local reference path, and generating a foot end track sequence.

The invention relates to the technical field of electronic design automation, in particular to a local layout wiring method based on sequence value correction and historical guidance search, which comprises the following steps: calling a multi-source dynamic sequence value wave diffusion path search sub-algorithm, generating initial paths for all wire nets, and initializing a historical solution set and a current solution sequence; random disturbance is carried out based on the current solution sequence, and candidate solutions are generated; judging whether the candidate solutions meet design rule constraints or not; checking whether the maximum iteration frequency is reached or no improvement frequency limit exists, if yes, entering the step of outputting the optimal solution, and otherwise, returning to the step of generating the candidate solution; selecting a wiring path which has an optimal target function value and meets all constraints from the historical solution set, and outputting a final result; according to the local layout wiring method based on sequence value correction and historical guidance search, the problems of high-density region wiring feasibility, complex design rule satisfaction, multi-objective optimization, calculation efficiency and the like can be solved.

The invention provides an electric power inspection path planning method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle electric power inspection. The method comprises the following steps: acquiring obstacle information of an electric power facility environment, wherein the obstacle information comprises the type and position of an obstacle; based on the type of the obstacle and a preset safety distance coefficient, determining a differentiated safety distance, the type of the obstacle including a power transmission line, a transformer substation, a tower and other obstacles; based on the obstacle information and the differentiated safety distance, obtaining an initial global path through a path search algorithm; based on a preset multi-objective optimization function, the initial global path is optimized, a Pareto optimal path set is generated, and the multi-objective optimization function comprises a path length objective, a safety margin objective and an electromagnetic safety objective; and determining a target global path from the Pareto optimal path set based on a preset inspection task mode. According to the invention, the inspection efficiency and adaptability can be improved while the safety is guaranteed.

The invention relates to the technical field of path planning, and discloses an unmanned aerial vehicle path planning method based on large-range cruise, which comprises the steps of geographic information acquisition, geographic information processing, regional discretization, starting point and target point setting, feasible path search and path optimization. Geographic information collection is comprehensive and accurate, data of terrains, obstacles, no-fly zones and the like are covered, and a foundation is laid for path planning; regional discretization fits the characteristics of an unmanned aerial vehicle, cells are divided according to the minimum turning radius and the effective range of a sensor, and flight safety and perception are guaranteed. Feasible path searching is efficient and reasonable, and the optimal path is searched by well-designed cost and heuristic functions and considering multiple factors; path optimization is effective, based on a B spline curve smoothing method, curvature and length are reduced, energy consumption is reduced, and abrasion is reduced; safety verification is added, collision and cross-zone risks can be accurately judged, and safety guarantee is enhanced.

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 provides a robot autonomous learning and adaptive trajectory planning method oriented to man-machine cooperation aiming at a mechanical arm simulating a human teaching sample to obtain a motion skill and generalizing the motion skill to an adaptive obstacle avoidance behavior of a new situation, and specifically, a dynamic motion primitive algorithm is introduced to carry out learning and generalization of a motion trajectory of the mechanical arm. Based on the initial state and the search area of a generalized trajectory improved particle swarm algorithm, rapid search of a local obstacle avoidance trajectory is achieved, and mechanical arm motion trajectory planning with an autonomous obstacle avoidance function is comprehensively achieved. The method has the advantages that the method has high consistency with an original generalization track and wider adaptability, and the path searching efficiency is remarkably improved; a virtual simulation experiment and a physical experiment verify that the method has good stability and practicability.

The invention relates to a path planning method based on an improved bidirectional A star search algorithm, relates to the technical field of intelligent vehicles, and solves the problem of low search efficiency caused by bloated search nodes or special topographic reasons in an existing path search scheme. The path planning method based on the improved bidirectional A star search algorithm comprises the following steps: acquiring an obstacle grid map of a target scene; determining a motion path from a starting point position to a target position in the barrier grid map based on an optimized bidirectional A * search algorithm; wherein in the optimized bidirectional A * search algorithm, the forward heuristic function value of the child node is a heuristic function value from the child node to the child node with the minimum reverse total cost at present, and the reverse heuristic function value of the child node is a heuristic function value from the child node to the child node with the minimum forward total cost at present.

The invention discloses a ship path dynamic planning method based on a collision risk field, and the method comprises the following steps: carrying out the grid division of a map of a navigation water area of a ship, and obtaining a grid map of the navigation of the ship; performing path search on the grid map by adopting a path planning algorithm to obtain an initial route of the ship; establishing a collision risk field model of the target ship according to real-time navigation parameters of the target ship, and substituting the coordinates of the central point of each grid in the grid map into the collision risk field model to obtain a collision risk value of each grid in the grid map; when the ship sails to the grid with the collision risk value larger than a set threshold value, the grid which is closest to the terminal point and has the collision risk value smaller than the set threshold value serves as a local optimal point, and the ship route of the ship is updated; and repeating the steps until the ship arrives at the destination.

The invention discloses a branch topological structure automatic switching control method and system based on load balancing, and relates to the technical field of automatic switching control, and the method comprises the following steps: obtaining working condition data, and constructing a joint map; extracting a power conduction path from the trunk node to each load node based on the joint map to form a high-load segment set; aiming at the high-load transmission segment, constructing a multi-target switching feasibility set, and generating a coupling relation mapping graph; based on the coupling relation mapping map, constructing a path search model, and solving an optimal topology switching path by adopting an enhanced iterative algorithm; and generating a topology switching sequence according to the optimal topology switching path, and implementing power transfer regulation and control on a high-load transmission section. According to the method, the joint atlas is constructed, the optimal topology switching path is generated, local overload is effectively relieved, power fluctuation is weakened, and the overall load balancing level and the active regulation and control capability of the system are improved.

The invention relates to the technical field of UV printing, and discloses an intelligent scheduling method and system for UV printing nozzles based on multi-objective optimization, and the method comprises the steps: collecting the real-time state data of a physical UV printing system, and forming a state data set; establishing a digital twinborn model at the edge computing node; scheduling simulation is carried out by adopting an improved ant colony algorithm, and a scheduling scheme is generated through path search of virtual ants; the scheduling scheme is applied to a physical system, feedback data are collected, a physical mathematical bidirectional feedback system is constructed, and hierarchical pheromones of edge nodes and a cloud end are cooperatively updated; each UV printing nozzle autonomously calculates a multi-objective optimization function value, and an optimal working strategy is selected; according to the method, a completely distributed self-organizing scheduling architecture is adopted, so that the single-point fault problem and the expansibility limitation of traditional centralized scheduling are effectively solved.

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 is suitable for the technical field of robots, and provides a path planning method and a robot, and the method comprises the steps: obtaining the environment information of a target area, building a grid map according to the environment information, and enabling the grid map to comprise a starting point area, an end point area and an obstacle area; determining a global path through a path search algorithm according to the grid map; in the process of controlling the robot to move along the global path, performing local path optimization through a dynamic window algorithm; wherein the dynamic window algorithm calculates the score of each candidate local path through a trajectory cost function and determines the optimal local path, and a strategy network output score obtained by predicting the candidate paths through a pre-trained deep learning strategy model is introduced into the trajectory cost function. The adaptive capacity of the robot in a dynamic environment can be improved.

The invention discloses a case evidence logic deduction method based on a graph neural network. The method comprises the following steps: S1, forming a standardized case evidence data set; S2, forming a multi-level case evidence graph; s3, performing vectorization representation on case evidence data nodes and edges of the multi-level case evidence graph, and calculating weights of the case evidence data nodes; s4, inputting the multi-level case evidence graph into a graph neural network, and calculating a logic relation weight between case evidence data nodes; s5, identifying case evidence data nodes with low credibility; and S6, based on the logical relationship weight of the case evidence data nodes and a causal reasoning result, calculating an optimal case evidence deduction path by adopting a path search algorithm, identifying a case core fact chain, and generating a case deduction conclusion. According to the invention, different types of case evidence data can be reasoned in respective hierarchies, information mixing is effectively avoided, and the accuracy and interpretability of case fact deduction are improved.

The invention discloses a converged network-oriented task awareness and intelligent scheduling method, and relates to the technical field of communication networks, the method is applied to a converged network, and the method comprises the following steps: constructing a converged network architecture; extracting a user equipment node of the current to-be-processed task from the computing power identifier of the current to-be-processed task as a source node; based on the converged network architecture, determining a corresponding path by using a path search algorithm when the source node is used as a starting point and each computing power center node is used as a destination node; according to the computing power identifier of the current to-be-processed task, selecting an optimal path from paths corresponding to all computing power center nodes; according to the method, the transmission time delay and the service time delay are considered when the optimal path is selected, so that the reasonability of task perception and intelligent scheduling oriented to the fusion network can be improved.

The invention discloses a multi-agent unmanned aerial vehicle search task energy optimization method based on an entropy maximization strategy, and belongs to the field of unmanned aerial vehicle search task energy optimization. The implementation method comprises the following steps: modeling a multi-region search task of the multi-agent unmanned aerial vehicle, constructing an energy consumption model of the unmanned aerial vehicle, taking the minimum energy consumption of a multi-agent system as a priority target, adopting a reinforcement learning algorithm with maximized multi-agent entropy, making a decision on the action of each unmanned aerial vehicle, and obtaining a multi-region search task of the multi-agent unmanned aerial vehicle. According to the method, the randomness of strategy implementation of each agent is enhanced, premature falling into a suboptimal solution is avoided, real-time adjustment is performed on the next action decision of each unmanned aerial vehicle according to the environment and energy consumption observed by other unmanned aerial vehicles based on a method of centralized training in distributed execution, and when the unmanned aerial vehicle arrives at a target area, the unmanned aerial vehicle is subjected to real-time control. And carrying out full-path search on the target area to ensure the optimality of solving sub-problems, namely, realizing energy optimization of a multi-unmanned aerial vehicle search task based on an entropy maximization strategy.

The invention discloses a three-dimensional path planning method for a low-altitude unmanned aerial vehicle, which belongs to the technical field of unmanned aerial vehicle path planning and comprises the following steps: constructing a three-dimensional flight environment model; according to a particle swarm optimization method, performing global path search and path cost optimization on the three-dimensional flight environment model to obtain optimized path cost and position corresponding to each particle in a particle swarm; according to the path cost and the position of each particle in the particle swarm, performing repeated iteration of selection, crossover and mutation operation on the particles through a genetic algorithm, and obtaining a global optimal path solution based on the optimal path cost of each iteration; and according to the globally optimal path solution, performing conversion to obtain an optimal path under the target coordinate system, and performing drawing through a drawing function to obtain an optimal path in the three-dimensional flight environment model. The problems of low efficiency and insufficient reliability in unmanned aerial vehicle path planning are solved.

The invention provides a data quality detection and improvement system for a large language model, and relates to the technical field of code quality management. The data analysis and characteristic extraction module is used for extracting structured and semantic characteristics of codes and constructing a code distribution diagram; the multi-dimensional quality detection module comprehensively evaluates the data set quality from three aspects of semantic diversity, structural consistency and error mode; the code optimization and verification module generates an optimization suggestion based on a detection result, and verifies the effectiveness of an optimization scheme through dynamic evaluation and unit testing; the feedback and self-adaptive optimization module is combined with developer feedback and runtime performance test to dynamically adjust a repair strategy, and the complex code repair efficiency is improved through quantum computing auxiliary path search; according to the method, full-process automation from quality detection to optimization verification is achieved, the method is suitable for multi-dependency and multi-module complex code repair tasks, and the quality and efficiency of the code life cycle are improved.

The invention relates to the technical field of semiconductor manufacturing, in particular to an automatic loading and unloading control system for semiconductor material plates. A clamping force dynamic control module; the path planning and obstacle avoidance module is used for dividing safety levels of loading and unloading areas on the basis of material plate stacking parameters, and generating an obstacle avoidance track by adopting an improved path search algorithm; optimizing an acceleration curve of the path according to the joint load data of the mechanical arm; the closed-loop verification module is used for carrying out deviation comparison on the actual pose of the tail end of the mechanical arm and the target coordinates, and if the deviation exceeds the limit, the visual positioning and morphology detection module is triggered to cooperatively reset to the path planning and obstacle avoidance module; the visual positioning and morphology detection module and the closed loop verification module are executed in sequence, and output parameters of the previous module serve as input parameters of the next module. Through close cooperation of the modules, efficient and accurate automatic loading and unloading control is achieved, the automation level is improved, and operation safety and accuracy are guaranteed.

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

The invention discloses a mechanical arm obstacle avoidance path planning method based on an improved RRT-Connect algorithm, a smearing mechanical arm and a masonry robot. The mechanical arm obstacle avoidance path planning method comprises the steps that firstly, a fixed sampling function is adopted in a three-dimensional space formed by a starting point Xinit and a target point Xgoal of a path to generate a fixed point; and respectively and simultaneously constructing four random trees between the starting point and the fixed point and between the fixed point and the target point to carry out path search, and generating a path from the starting point to the target point. A target guiding sampling method based on a heuristic function can be introduced into the algorithm of the four-random-tree collaborative path searching mechanism, and the sampling probability is adjusted; and a better path planning effect is achieved by setting a self-adaptive step length strategy, a greedy strategy and other technical means. According to the method, the path planning of the mechanical arm is realized by setting the fixed sampling points to perform the four-way RRT-Connect algorithm, so that the rapid planning is ensured, the path time consumption is reduced, the collision with an obstacle is avoided, and the robustness of the path is enhanced.