170 results about "Random tree" patented technology

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 discloses a track generation method and device for casting blank deburring and a medium, and relates to the technical field of grinding process.The track generation method mainly comprises the steps that according to FPFH feature descriptors of respective outer contours of an actual point cloud model and a standard point cloud model, key points matched with the standard point cloud model are searched through a KD tree, and the FPFH feature descriptors of the outer contours of the actual point cloud model and the standard point cloud model are obtained; performing multi-contour segmentation division of the actual point cloud model according to the distance between the key points; receiving polishing parameters input by the user based on the segmentation division results of the contours, and generating an initial track according to the polishing parameters; smoothing discrete trajectory points of the initial trajectory through B-spline interpolation, and generating a local obstacle avoidance path in combination with an industrial robot kinematics model and a fast expansion random tree star algorithm; outputting the final polishing track to a control terminal, and adjusting the track depth in real time according to the feedback of the force sensor. According to the method, regional differentiation track generation is supported, and the process adaptability and the personalized demand response capability of a complex contour are improved.

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.

The invention provides a robot intelligent path planning method based on K-value adaptive multi-algorithm cooperation, and belongs to the field of robot path planning. The robot path planning method mainly comprises the following steps: preprocessing an environment map, and constructing a three-layer data structure of a quadtree, a Voronoi graph and a connected graph; generating an initial path based on an improved fast extension random tree algorithm, and extracting key points; establishing a K value evaluation system including five dimensions of obstacle density, local complexity, channel characteristics, path curvature and topology complexity, and realizing accurate quantification of the environment; carrying out adaptive region division based on the K value, and dividing the environment into an open region, a narrow channel region and an obstacle dense region; aiming at the characteristics of different regions, an improved bidirectional A algorithm and an improved fast expansion random tree algorithm are respectively adopted to carry out collaborative planning; and finally, smoothing the path through a Bezier curve. According to the method, the quality and the planning efficiency of path planning in a complex environment can be improved, and higher robustness is achieved.

The invention relates to a self-adaptive step length RRT (Rapidly-expansion Random Tree, RRT) path planning method based on collision detection, and belongs to the field of robot path planning. The method comprises the following steps: firstly, initializing a system, namely initializing map information, determining a robot obstacle space and a non-obstacle space, determining position coordinates of a path initial point and a target point, a cardinal number of each connection step length, a distance target point threshold value and the like; then, path planning is carried out by using an improved RRT algorithm, including sampling in a non-obstacle space, optimization of random sampling points in combination with a target bias strategy and an APF algorithm, and expansion in a random tree expansion direction according to a dynamic step length strategy; then, judging whether the new node is smaller than a threshold value, if so, finding a path, otherwise, jumping to the sampling step; and finally, after the path is found, the path length is shortened by using a path pruning strategy, the final path is smoothed, and the path is output. Compared with a traditional RRT algorithm, the improved RRT algorithm has the advantages that the path searching speed is increased, redundant nodes are eliminated, and the path quality is improved.

The invention is suitable for the technical field of unmanned aerial vehicles, and provides an unmanned aerial vehicle cruise detection method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the global path planning through employing an improved fast extension random tree algorithm; when a conflict risk is detected, a dynamic reward function is designed with returning to a global flight path after obstacle avoidance as a target, and a deep reinforcement learning model is adopted to realize control of actions of the unmanned aerial vehicle; and controlling the unmanned aerial vehicle to fly along the global aircraft path and the local obstacle avoidance path, and executing a target detection task based on the target detection model in the flight process until the task is completed and / or a target position is reached. According to the invention, global path planning is carried out by adopting the improved fast extended random tree algorithm, a local obstacle avoidance path is obtained in combination with the deep reinforcement learning model when facing an emergent obstacle, the autonomous cruising ability of the unmanned aerial vehicle is improved, real-time target detection is realized based on the target detection model in the flight process, and the task execution efficiency of the unmanned aerial vehicle is improved.

The invention relates to the technical field of unmanned aerial vehicle trajectory planning, in particular to a bidirectional tree random search method based on a complex environment node cost function, which comprises the steps of surveying obstacle data in a flight area and modeling, then introducing a target deviation strategy and a bidirectional search tree mechanism, and combining with an improved minimum cost method to obtain a random search result. Alternately expanding the two trees to generate an initial path, and integrating the path length, the obstacle distance and the flight height energy consumption by a cost function; and finally, pruning the initial path, removing redundant nodes, smoothing the trajectory by adopting a cubic spline interpolation method, ensuring curvature continuity, and generating an optimal path meeting the flight performance of the unmanned aerial vehicle. According to the invention, through an improved bidirectional minimum cost fast expansion random tree algorithm, dynamic association of obstacle constraint, target deviation and path cost in unmanned aerial vehicle path planning in a complex environment is represented, and a more efficient search convergence speed and better path quality are obtained. And rapid convergence and global optimization of the track of the unmanned aerial vehicle in a high-dynamic environment are realized.

The invention relates to an adaptive step length Combine-RRT * path planning method, which belongs to the technical field of robot path planning, and comprises the following steps: modeling and binarizing according to an obstacle map, determining a starting point and a target point, and setting initial parameters; initializing a random tree according to the obstacle map; determining a normal distribution sampling process and parameters; a sampling strategy of a sampling process is selected by using a dynamic greedy factor for sampling to generate sampling points; determining a dynamic expansion step length during node expansion by using a variable step length growth strategy, and generating a new expansion node; performing collision detection, updating the tree atlas, and outputting an initial path; determining an elliptical sampling range; using a dynamic greedy factor to select elliptical region sampling or normal distribution sampling to generate sampling points; re-searching father nodes and re-connecting in a specified range of the new extended nodes, and searching a path with the minimum cost; and a B spline curve rear end optimization path is introduced.

The invention relates to the technical field of robot path planning, and discloses an improved robot path planning method based on an RRT algorithm, and the method comprises the steps: carrying out the global path searching through an improved fast exploration random tree (RRT) algorithm, and employing a special random sampling strategy and a KD-Tree to accelerate the nearest neighbor searching, and improving the searching efficiency; a greedy strategy is utilized to optimize a path, and redundant nodes are reduced; a smooth trajectory is generated by means of cubic spline interpolation, and control points are dynamically adjusted through collision detection. The method effectively solves the problems of path redundancy and poor smoothness of a traditional RRT algorithm, is suitable for the fields of robot navigation, logistics transportation and the like, and can remarkably improve the path planning efficiency and quality.

The invention relates to the technical field of path planning, in particular to a mechanical arm path planning method and system based on environment parameter self-adaptive adjustment, and the method comprises the steps: taking the tail end position of a mechanical arm as a starting node, taking the position of a target object as a target node to construct a random tree, and setting a search radius to dynamically obtain obstacle data; the step length is dynamically adjusted according to obstacle data, an adaptive sampling strategy is introduced, and an initial path is generated by selecting an optimal father node and reconnecting peripheral nodes; smoothing the initial path, adjusting an offset point, optimizing a target node search area and constraining a path direction included angle to obtain a final path; and the path node coordinates are converted into mechanical arm physical coordinate system coordinates, and a mechanical arm is driven to execute an obstacle avoidance grabbing task. According to the method, the path planning efficiency is remarkably improved, the planning time is shortened, the path quality is optimized, and the safety and stability in a complex environment are enhanced.

The invention provides a network attack processing method and system based on a zero-sum game, relates to the field of data processing, and can solve the problem of low network attack processing accuracy. The method comprises the steps that a target sample in samples is determined according to the optimal weight of all classifiers in a classification system and predicted values of all the classifiers for the samples, the classification system at least comprises a random forest classifier, an extreme random tree classifier and a gradient boosting classifier, and the samples comprise network attack information; then, carrying out quantity expansion processing on the target class samples through a generative adversarial network model to obtain expanded samples; and then, carrying out network attack detection according to the expanded sample and the sample, and determining whether a network intrusion attack behavior exists or not. And an expansion sample with authenticity and diversity is generated through the generative adversarial network model, so that hidden network intrusion attack behaviors can be found. Therefore, the processing accuracy of the network attack is improved.

The invention discloses a tower crane motion planning method and device in a complex environment, and the method comprises the steps: building a nonlinear model for comprehensively describing the motion characteristics and transient behaviors of a crane-load system on the basis of kinetic analysis, and carrying out differential flat analysis; a BiRRT * algorithm based on direction bias is provided, a node expansion process is optimized by introducing a target bias mechanism of fusion region probability sampling and based on an improved potential field function direction guiding mechanism, and the efficiency and quality of path planning are improved. An improved random tree extension mechanism is combined with a depth deterministic policy gradient (DDPG) of reinforcement learning, and adaptive adjustment of sampling direction and step length parameters is realized. Path points obtained through path planning serve as profile value points of trajectory planning, system full-state constraint conditions such as load obstacle avoidance and shimmy reduction are fully considered, multi-target trajectory planning is carried out based on an NURBS curve, and a multi-target comprehensive optimal trajectory on the aspects of total hoisting time, operation energy consumption and load shimmy suppression is obtained.

The invention provides an incremental reinforcement learning path planning method based on dynamic reward remodeling, which is used for solving the problems of training efficiency and composite optimization in path planning. According to the method, a rapid exploration random tree and an artificial potential field method are combined with reinforcement learning, a dynamic reward function is designed, real-time feedback is provided for an intelligent agent, and strategy learning is accelerated. The system specifically comprises a reward remodeling module which provides process rewards through a fast exploration random tree and an artificial potential field method; the sub-target course learning module is used for gradually increasing task complexity and optimizing sample utilization; the progressive reward adjustment module dynamically adjusts the reward weight and solves the reward coupling problem; and the strategy fine tuning module dynamically adjusts the learning rate and supports strategy optimization. Experimental results show that in static and dynamic path planning tasks, compared with a traditional method, the method has the advantages that the training efficiency and the composite optimization performance are remarkably improved, the reward coupling problem is effectively solved, and the intelligent agent can efficiently learn and converge to a global optimal strategy.

The invention discloses a UUV path planning method based on an improved fast search random tree. The method is suitable for the fields of autonomous navigation, task execution, obstacle avoidance, safety guarantee and the like of UUVs. The method comprises the following steps: S1, determining an initial step length; dynamically adjusting the initial step length according to the scale of the task space and the complexity of the environment; and S2, carrying out target bias sampling. In the sampling process, according to a set bias threshold value, an algorithm biases a sampling point to a target area according to a certain probability, so that the target guidance quality of search is enhanced; and S3, removing redundant points. A Dijkstra algorithm is adopted to simplify a path, unnecessary turning points are removed, and more nodes are inserted into the path to improve the intensity of the path, so that the optimization space is increased. Then, redundant point removal is executed again, so that the path is further simplified; and S4, smoothing the path. Through the maximum curvature constraint, it is ensured that the turning angle of the path does not exceed the preset limit, meanwhile, in combination with a polynomial fitting method, abrupt turning in the path is reduced, and the smoothness of the path is improved.

The invention belongs to the technical field of path planning, and provides a robot path planning method and system with a local search path optimization function, and the method comprises the steps: determining the positions of a starting point and a terminal point, and the number and positions of obstacles according to a map environment of robot movement, initializing two random trees with the starting point and the terminal point as root nodes, determining the maximum number of iterations and a termination condition; a root node of a third random tree is generated and initialized; according to the position of the obstacle, iteratively utilizing three random trees to alternately carry out node expansion, and in the expansion process, adopting an adjacent point local search strategy, and when the random trees carry out continuous expansion to a certain node, carrying out local search on a newly generated node to seek a better path; and obtaining the current optimal planning path of the robot until the maximum number of iterations or the termination condition is met. According to the method, the trajectory planning efficiency can be remarkably improved, and the trajectory generation stability is improved.

The invention provides a mechanical arm path planning method and device based on an improved random tree, and the method comprises the steps: obtaining a real scene image, and constructing a three-dimensional obstacle model; performing grating processing on the three-dimensional obstacle model to generate a repulsive force field; planning a path trajectory in the three-dimensional obstacle model based on the improved random tree; and generating a joint space path point corresponding to the mechanical arm through the inverse motion so as to control the mechanical arm. According to the method, the repulsive force field of the obstacle is generated, and the randomness of the random tree is influenced by the repulsive force field, so that the obstacle can be bypassed in the random tree planning process, and the planning efficiency is improved.

The invention discloses a mechanical arm path planning method and system based on self-adaptive expansion sampling, belongs to the technical field of path planning, and solves the problems of low sampling rate, low convergence rate and poor initial path quality caused by too random sampling area and improper selection of expansion step length during path planning of a mechanical arm in the prior art. According to the self-adaptive expansion sampling strategy provided by the invention, on the basis of the F-RRT * optimization algorithm, the growth direction of the random tree is corrected by introducing the target bias force, so that the random tree deviates to the target point, the inflection point is optimized, the generated path is smoother, and by introducing the concept of target point gravity deviation, the optimal path is obtained. A strategy is adaptively expanded according to the obstacle complexity in the environment, and expansion is performed by dynamic step length, so that the stability and precision of the trajectory are improved, and the length of the generated path, the sampling frequency and the convergence time are reduced.

The invention discloses an automatic wiring method and system of a secondary screen cabinet, a medium and a product, and belongs to the field of automatic wiring of the secondary screen cabinet, and the method comprises the steps: obtaining the design drawing data of the secondary screen cabinet, and extracting the installation position of each component and the insertion starting point and end point of a wiring harness; setting a plurality of bundling middle points for each wire harness based on the positions of the components, and forming a node sequence by combining starting and ending points; generating path fragments for each pair of adjacent nodes in the sequence by adopting a fast exploration random tree connection algorithm with sampling region constraint and an obstacle approaching penalty term; and sequentially splicing all the segments into a complete wiring path, and controlling a wiring device to automatically complete wiring harness arrangement and fixation according to the complete wiring path. By implementing the method, the problems of low efficiency and poor safety of automatic wiring path planning of the wiring harnesses in the secondary screen cabinet in the prior art can be solved.

The invention discloses an electric power inspection method and system based on big data and a random tree algorithm, and relates to the field of electric power system management and maintenance. Comprising the following steps: collecting key information, historical information and personnel basic data information of power equipment in real time, and processing the obtained key information; based on the processed key data, a plurality of factors are comprehensively considered, and an inspection line model is constructed; solving the inspection line model by using a fast search random tree algorithm; and outputting the optimized power inspection path, and carrying out data storage. According to the method, the information subsets of the electric power equipment and the inspection maintenance personnel object provided by the big data platform are introduced, multiple feasible paths subjected to coarse screening are further screened, the optimal electric power inspection path is generated through iteration for a certain number of times, the line scheme is output and stored, and intelligent optimization of the electric power inspection path is achieved.

The invention provides a limited angle six-degree-of-freedom mechanical arm low-energy-consumption path planning method based on an improved RRT * algorithm, and the method is based on the improvement of the RRT * algorithm, and comprises the following steps: S1, completing the setting of working space parameters, the initialization of a data structure and the construction of a 3D graphic environment; s2, random sampling points are generated through a target point sampling strategy, and joint angle constraint check is carried out; s3, expanding new nodes in the random tree, and adding the new nodes into the random tree after obstacle detection; s4, calculating the energy consumption of a new node by using an LQR controller, calculating the Euclidean distance through a formula, and obtaining a heuristic value through a heuristic function; s5, storing the new node and the heuristic value thereof into a priority queue realized by a minimum heap; s6, selecting a node with the minimum heuristic value from the priority queue, and optimizing a path, including updating a father node and performing reconnection operation; s7, judging whether to stop or not according to the distance from the new node to the end point and the maximum number of iterations; and S8, when a termination condition is satisfied, backtracking to generate a complete path.

The invention discloses a ship cabin welding mechanical arm obstacle avoidance motion path planning method. The method comprises the steps that starting points and target points of two random trees are set; generating a random tree through bidirectional growth; a sampling point xrand is generated, and target bias is carried out; a nearest node xnear is searched for; calculating the resultant force Ft of the attractive force and the repulsive force borne by each nearest node xnear; expanding from the nearest node xnew to the direction at a fixed step length s to generate a new node xnew; collision detection; and iterating repeatedly to generate a preliminary path. A target bias strategy is added into a bidirectional RRT * algorithm, an artificial potential field is improved to plan the tail end movement track of the welding machine, the algorithm combines attraction force and repulsive force in the path planning process, obstacles can be effectively bypassed, the search process is more concentrated in a target area, the connectivity and planning quality of a path are enhanced, and the path planning efficiency is improved. The problems that an RRT * algorithm is uneven in sampling, too many in redundant points, too many in iteration times and too long in path are solved, the algorithm searching efficiency is improved, and the obstacle avoidance motion of the welding mechanical arm is more efficient and safer.

The invention relates to the technical field of path planning, in particular to an automatic path planning method for a mechanical arm. According to the technical scheme, the method comprises the steps of environment perception and modeling, global rough planning, learning parameter initialization based on a deep reinforcement algorithm, local fine planning and learning optimization, and path execution and real-time feedback adjustment. The rapid exploration random tree algorithm is adopted for global rough planning, a basic framework is provided for subsequent local fine planning, the global optimal path can be effectively searched through the deep reinforcement learning algorithm, the path is further optimized through local fine planning, the problem that a traditional intelligent path planning algorithm is prone to falling into a local optimal solution is solved, and the path planning efficiency is improved. The multi-sensor fusion technology is used for sensing environment information in real time, the environment model and the state space are updated in time, the robustness and reliability of the system are improved, the mechanical arm can adapt to diversified working environments and task requirements, and more intelligent decision making and path planning are achieved.

The invention discloses a multi-robot affine formation path optimization method driven by a virtual navigator, and the method comprises the steps: firstly setting a point set composed of the virtual navigator and followers, enabling the virtual navigator to form a convex hull and meet an affine localization condition, so as to guarantee that the followers are always located in the convex hull; generating a collision-free reference trajectory of the mass center of the virtual navigator based on a fast random tree algorithm; constructing a nonlinear constraint optimization problem NCO which contains linear transformation constraint and meets smoothness, obstacle avoidance, speed and angular speed constraint; and establishing a communication graph of the virtual navigator and the follower, calculating balance stress, designing a smoother follower distributed differential speed control law, and realizing formation path optimization and obstacle avoidance control. Compared with an existing method, the formation path generation method has the advantages that the smooth and stable formation path fitting the reference trajectory can be generated while obstacle avoidance and physical feasibility are guaranteed, and the cooperative motion capability and the control performance of a multi-robot system in a complex environment are improved.

The invention discloses a path planning method for cooperative operation of multiple unmanned aerial vehicles, and belongs to the technical field of unmanned aerial vehicle control. The method comprises the following steps: acquiring real-time state information of each unmanned aerial vehicle; dynamic tasks are allocated based on a lightweight auction protocol, and distance, energy consumption, task urgency and load are comprehensively considered in a bidding function; generating a global reference path for each unmanned aerial vehicle, and applying for a space-time reserved window of the three-dimensional space logic grid unit in a specific time interval from the coordination agent; the coordination agent performs conflict detection and feeds back a reservation result; each unmanned aerial vehicle carries out local re-planning in combination with an improved artificial potential field-fast extended random tree algorithm so as to avoid dynamic obstacles; and when the task is changed or the unmanned aerial vehicle fails, a task release and redistribution mechanism is triggered. According to the method, safe, efficient and extensible cooperative path planning of multiple unmanned aerial vehicles in a high-dynamic environment is realized, and the method is suitable for scenes such as logistics distribution, electric power inspection, emergency search and rescue and the like.

The invention discloses a mine transport vehicle path control method and system, and relates to the technical field of path planning. Comprising the following steps of: 1, searching a global path from a starting point to a target point according to an existing map, and sampling and planning the global path in a state space by utilizing a fast expansion random tree algorithm RRT; step 2, acquiring a global path, cutting the global path into a local map range, simulating a plurality of running tracks of the mining transport vehicle by using a double-delay depth deterministic strategy gradient algorithm TD3, evaluating the running tracks, selecting an optimal track as a local path of current running, and taking the optimal track as the local path of the current running; and combining the global path and the local path to realize the path control of the mining transport vehicle. The stability of a data processing network is improved, and the process planning speed is improved while the path quality is ensured.

The invention discloses a method and a system for path planning of a spraying mechanical arm based on an RRT algorithm of dynamic step size, the method for planning the path through the RRT algorithm is optimized, sampling points are discriminated in advance through the optimized RRT algorithm, and generation of redundant path points on a random tree and unnecessary collision detection are avoided; meanwhile, a target point gravitational component is added for guiding, the problem that a traditional method is poor in guiding performance is solved, the extended step length can be dynamically adjusted when approaching an obstacle, the step length can be recovered when being far away from the obstacle, the extended search capacity of the algorithm is improved, and meanwhile the environmental adaptability of the algorithm can be improved to a certain degree.

The invention discloses a path determination method and device of a mechanical arm, a robot, a storage medium and a computer program product. The method comprises the steps that the working space of the mechanical arm and the starting point and the target point of the path of the mechanical arm are obtained; estimating a path cost and determining a path cost threshold according to the starting point and the target point of the path; a fast search random tree is constructed bidirectionally from the starting point and the target point of the path, and an initial path meeting a path cost threshold value is obtained; and key points of the initial path are extracted, the extracted key points of the initial path are updated, and an optimized path of the initial path is obtained and serves as the path of the mechanical arm, so that the path of the mechanical arm is determined. According to the scheme, the path cost is estimated, the path cost threshold value is determined, the bidirectional fast search random tree is constructed to determine the initial path, the key points of the initial path are extracted and updated to optimize the initial path, the path with the low cost can be fast obtained, and the flexibility and the real-time performance of path planning of the mechanical arm can be improved.

The invention discloses a wharf warehouse AGV transportation operation path planning method and system, and the method comprises the steps: dividing a warehouse into K cells based on a digital warehouse model, enabling each cell to represent a storage interval point, setting an AGV to start from an initial position, conveying goods to a target storage interval position, and enabling the AGV to start from an initial position, returning to an initial position after the task is completed, and constructing a multi-target path mathematical model; aiming at the multi-target path mathematical model, finding N feasible paths from an initial position to a target position for AGV transportation operation by adopting a fast extended random tree algorithm RRT; a crownporcupine optimization algorithm CPO and a sparrow optimization algorithm SSA are combined, N feasible paths serve as initial solutions of the CPO-SSA algorithm, the optimal path of the AGV is obtained through optimization, and it is ensured that the distance of the path is minimized. According to the invention, the transportation efficiency and management efficiency of the wharf warehouse can be improved, the energy consumption of the AGV is reduced, and the transportation cost is reduced.

The invention discloses an industrial robot grabbing obstacle avoidance path planning method based on an RRT-TAGB algorithm, and belongs to the field of mechanical assembly. Firstly, a self-adaptive target bias sampling strategy is provided for the problem that a traditional RRT algorithm is large in search randomness, the target sampling probability is determined through the volume relation between an obstacle and a grabbing work space, a random tree is made to be expanded to a target point more quickly, and the guidance quality of the algorithm in the search process is improved; secondly, in order to effectively solve the problem that path planning fails due to the fact that local optimum is prone to occurring in the dense obstacle environment, an artificial potential field method is used for conducting local path planning, and the positions of nodes are adjusted again to obtain an optimized path; then, aiming at the problem that invalid nodes on the path are too many, a greedy strategy is adopted to remove redundant nodes of the path, and the length of the path is reduced; and finally, aiming at the problem of poor path quality, performing smoothing operation on the path by using a cubic B-spline curve, and removing unnecessary corners so as to improve the quality of the path. According to the RRT-TAGB algorithm adopted by the invention, the guidance quality of random tree growth is improved, redundant nodes on a path are greatly reduced, the search time is shortened, the final path is smoother, the overall obstacle avoidance path quality is higher, and the adaptive capacity of grabbing and obstacle avoidance of the industrial robot in a complex environment is ensured.

The invention discloses a crane working path planning method and system and application thereof. The method comprises the following steps: 1) constructing a crane operation demonstration database; 2) pre-training a convolutional neural network by using a demonstration database, and extracting a mapping relation between environment features and tracks; 3) inputting the current starting point position, the target point position and the environment information obtained by the laser radar into a path generation network, and outputting an initial action sequence of the rotation degree of freedom, the dolly motion degree of freedom and the hook degree of freedom; 4) performing local re-planning on the initial action sequence by adopting a fast random tree algorithm based on physical constraint, wherein the optimization criterion is the weighted sum minimization of the path length, the load swing angle and the action sequence variation; and (5) the crane is controlled to move according to the optimized action sequence, the load swing angle is monitored in real time, and emergency braking is triggered when the swing angle exceeds a threshold value. By means of the method, efficient and safe operation of the crane in the complex environment can be achieved, and good engineering practicability and popularization value are achieved.