178 results about "Mobile robots path planning" patented technology

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 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 relates to the technical field of industrial robot path planning, in particular to an industrial mobile robot path planning method based on an artificial potential field method and dynamic prediction. The method comprises the following steps: firstly, acquiring environment point cloud data, and distinguishing static and dynamic obstacles based on time sequence difference and Euclidean distance; kalman filtering is used to predict the future position of a dynamic obstacle, and a density clustering algorithm is used to simplify a static obstacle; combining a target point, a static obstacle set and a dynamic obstacle prediction result to construct an improved artificial potential field function to calculate gravitational force and repulsive force; the potential collision risk is judged based on the motion state of the robot, the dynamic obstacle repulsive force with the risk is brought into potential synthesis in advance, and the final resultant force for guiding motion is generated; and when it is detected that the system is caught in a local optimal or oscillation state, executing a virtual gravity point escape strategy until the system reaches a target point. According to the method, the fusion of static clustering, dynamic prediction and potential field improvement is realized, and the obstacle avoidance and path planning efficiency in a complex dynamic environment is improved.

The invention belongs to the technical field of path planning, and provides a dynamic path planning method, system and equipment based on multiple mobile robots and a medium based on the multiple mobile robots in order to solve the problems that the calculated amount is increased sharply and unexpected situations cannot be handled in the current path planning of the multiple mobile robots, and the independent path planning of each robot is completed by adopting mixed A *-epsillon. A dual-domain priority queue is introduced, child nodes are generated through node expansion, then a path is re-planned for the robot with constraints, and therefore a conflict-free path of the robot is generated; and performing control sequence sampling on each robot, taking a conflict-free space-time path as a reference path, obtaining an optimal control sequence according to the cost value of each predicted trajectory, and adjusting the path in real time through rolling optimization so as to cope with a dynamic obstacle. The method can actively avoid a dynamic obstacle or an obstacle which is not considered during planning.

The invention discloses a mobile robot path planning method and system based on a circular trajectory control obstacle function, and aims to realize trajectory smooth control in an obstacle avoidance process. Firstly, an envelope circle is used for modeling an obstacle, an actual boundary is approached in a continuous and simple geometric form, and the calculation complexity of collision detection is reduced. Secondly, designing a control obstacle function based on a circular trajectory, constructing a derivable constraint in combination with a geometrical relationship between a tangent line and an obstacle boundary, and achieving trajectory smoothing, reducing speed fluctuation and prolonging the service life of an execution mechanism while guaranteeing a safe distance. For disturbance caused by factors such as ground unevenness, historical data are learned by using a Gaussian process, the disturbance trend is predicted in real time, and the motion stability is improved. And finally, through nonlinear optimization solution, generating control input with safety, continuity and disturbance adaptive capability. According to the method, geometric modeling, safety constraint, disturbance prediction and a dynamic adjustment mechanism are fused, and the safety and adaptability of robot path planning are remarkably enhanced.

A mobile robot path planning method based on an SAC algorithm belongs to the technical field of deep reinforcement learning and mobile robot navigation, integrates the thought of hierarchical reinforcement learning skill combination into the SAC algorithm, and uses a time sequence abstraction method to integrate low-level fine-grained actions, so as to improve the precision of the SAC algorithm. A higher-level coarse-grained action with definite semantics can be learned; in reinforcement learning training, a distributed soft strategy iteration method is used, maximum entropy reinforcement learning is expanded into a distributed learning version, and variance and overestimation of a Q value are reasonably controlled through a learning state-action return distribution function, so that the overall strategy quality of robot path planning is improved. According to the method, training and testing are carried out in a simulation environment Image-Env, and the performance of the algorithm is verified on Turtlebot2. The method can efficiently avoid obstacles, is short in path, and has a good obstacle avoidance effect in a complex dynamic environment.

The invention provides a mobile robot path planning method and device based on an improved RRT algorithm and a medium, and belongs to the technical field of mobile robot path planning. The method comprises the steps that a starting point, a target point and obstacle distribution information in a space map are set, and an algorithm is initialized; determining a random sampling point source by using a probability target bias strategy; determining a node closest to the sampling point in the random tree and marking the node as a nearest node; introducing a self-adaptive target gravitation method and a component in a random sampling point direction to determine an expansion direction of a new node; judging whether a connecting line between the new node and the nearest node collides with an obstacle or not, and determining a gravity coefficient; determining an original path from a starting point to a target point, and removing redundant nodes; and performing optimization processing on the path from which the redundant nodes are removed, and outputting an optimized path. According to the method, the path planning time can be greatly shortened, the path is smooth, and the obstacle avoidance path planning efficiency of the mobile robot is further improved.

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

The invention discloses a mobile robot path planning method and system, and belongs to the technical field of robots, the method comprises global path planning and local path planning; wherein the global path planning comprises the steps of enabling a path to be constantly away from an obstacle by improving an A * algorithm and introducing a bilateral obstacle detection algorithm and an offset obstacle avoidance method so as to avoid collision with the obstacle during turning, and performing optimization and smoothing processing on the path by combining a thinning algorithm and a middle point outward expansion method so as to generate a global optimal path; the local path planning comprises the steps of improving a TEB algorithm, introducing a sensing window and an evaluation function, proposing an acceleration and jerk double-constraint dynamic obstacle avoidance strategy, and ensuring stable movement of the robot in the obstacle avoidance process. According to the scheme, by combining the advantages of global path planning and local path planning, the path planning capability and the transportation efficiency of the robot in a complex and changeable environment are improved.

The invention relates to an unknown dynamic environment mobile robot path planning method based on reinforcement learning, and belongs to the technical field of path planning. According to the method, a two-layer reinforcement learning structure is adopted, a bottom layer module learns global path planning, and an ellipse specific and precise strategy and a GliEF-Dyna-Q algorithm are provided in the early stage; the high-level module learns an obstacle avoidance rule to complete local dynamic obstacle avoidance; according to the GliEF-Dyna-Q algorithm, elliptic geometric knowledge is introduced to design an elliptic knowledge special and precise strategy to narrow the sampling range, a reward mechanism and path search are constrained and optimized through global and local perspectives, and an improved SA-epsilon greedy strategy is fused into Dyna-Q to achieve efficient global path planning. The high-level module provides a brand-new obstacle avoidance behavior reward mechanism and a more adaptive value function updating method so as to carry out comprehensive local obstacle avoidance activities. According to the invention, the robot can more efficiently realize obstacle avoidance and complete optimal path driving in a dynamic environment.

The invention provides a collision-free global path planning method for a mobile robot, belongs to the field of mobile robot path planning, and is used for solving the problems of intention misjudgment, low role allocation efficiency and high collision risk in a dynamic scene caused by dependence on statistical association in related technologies. According to the method, agent intention reasoning is achieved by constructing a mental atlas containing a causal relationship, efficient role allocation is performed based on a quantum role superposition state, path search is optimized in combination with a conflict risk field, role collapse is triggered when a conflict risk exceeds a threshold value, and cooperation of adjacent agents is guaranteed through a quantum entanglement cooperation state. According to the scheme, intention prediction accuracy and role allocation efficiency are improved, and collision-free and efficient global path planning in a dynamic environment is realized.

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

The invention relates to a mobile robot path planning method based on an improved eagle optimization algorithm, and belongs to the technical field of robot path planning, and the method comprises the following steps: S1, constructing a two-dimensional grid map according to map information, and marking a starting point, a target point and an obstacle; s2, constructing a multi-objective function, and evaluating the advantages and disadvantages of the path according to the path length and the path smoothness; s3, using uniformly distributed Logistic chaotic mapping and combining with an LOS sight line detection algorithm to initialize a population; s4, improving an egret optimization algorithm by adopting a self-adaptive nonlinear inertia factor and a doliolaria group algorithm, selecting a corresponding global search mode and a local search mode according to different iterations, and updating the position and the target function value of an egret individual; and S5, outputting the optimal path.

The invention discloses a mobile robot path planning method based on an improved A star algorithm, which comprises the following steps of: firstly, acquiring environment information of a current map through a sensor, establishing a grid map, and simultaneously determining a starting point coordinate, an end point coordinate and an environment obstacle coordinate of a robot; an OPENLIST list and a CLOSEDLIST list are created, and a starting point is put into the OPENLIST list; selecting a new distance calculation function, and setting the Euclidean distance as a median value of the Euclidean distance and the Manhattan distance; improving a heuristic function, and introducing an adaptive estimation function; the total distance cost of the nodes is calculated from the starting point according to the node expansion mode, the optimal path nodes are placed in the CLOSEDLIST till the end point is traversed, and the optimal path is generated; and deleting redundant nodes in the path based on a collinear principle to obtain a final optimized path. The search time of the algorithm is shortened, the algorithm can better adapt to different environments and scenes, the applicability of the algorithm is enhanced, and the planning efficiency of the algorithm is effectively improved.

The invention provides a mobile robot path planning method based on an improved ant colony-guided optimization algorithm. According to the method, after a grid map environment is constructed, a dynamic selection factor is introduced to optimize a state transition rule of an ant colony algorithm, so that the efficiency at the initial stage of search is improved; a multi-step search strategy is adopted to enhance the flexibility of path planning, and an end point distance index is utilized to replace the traditional pheromone concentration, so that the risk of falling into local optimum is reduced; and carrying out smoothing processing on the path in combination with a guide optimization algorithm to realize iterative correction and global path optimization of a feasible region. The method has good path quality and convergence performance, and is suitable for a mobile robot path planning task. Simulation results show that compared with a traditional ant colony algorithm and an improved method thereof, the algorithm has obvious advantages in the aspects of path length, the number of corners, convergence efficiency and the like.

The invention relates to the field of electric power automation, in particular to an indoor mobile robot path planning method and system for transformer substation equipment inspection, and the method comprises the steps: building a grid map according to a working scene which actually needs to be inspected, determining the positions of a starting node and a target node, sorting nodes in an open list, and storing the sorted nodes in the open list; setting the node with the minimum node cost as the current node, adding the current node into a closing list, and then expanding the current node by using a child node expansion strategy; when the node existing in the open list is searched, calculating the node cost again; outputting a path according to the closing list and carrying out repeated broken line optimization; the trained improved DQN algorithm is used for carrying out obstacle avoidance processing on unknown obstacles in the global path until a collision-free path from the starting point to the terminal point is planned, and the problems that a traditional inspection robot path planning algorithm is low in efficiency, unsmooth in path and poor in obstacle avoidance effect during path planning are solved.

The invention discloses a mobile robot path planning method combining kinematics constraint and variable density sampling, which comprises the following steps: setting a starting point and a target point, adding the starting point into a vertex set, adding the target point into a sampling set, initializing a node priority queue and an edge priority queue, and establishing kinematics constraint and environment parameters at the same time; searching a main circulation path; if the vertex cost is better, performing vertex expansion and generating candidate edges, and if the edge cost is better, performing feasibility verification and accessing the path tree; when the search is stagnated or trapped in local optimum, an escape mechanism is triggered, and the exploration capability is enhanced through radius expansion and supplementary sampling; an improved heuristic function and a local cost function are established, search convergence is guided, and track smoothness and performability are improved; and when a termination condition is reached, outputting a path planning result. According to the method, the sampling efficiency and the searching speed are improved while the path feasibility is ensured, so that the navigation requirement of the mobile robot in a complex dynamic environment is met.

The invention relates to the technical field of robot path planning, and discloses a mobile robot path planning method and system based on a rat optimization algorithm, and the method comprises the following steps: environment modeling: carrying out the modeling of a moving environment of a mobile robot, dividing the environment into a plurality of grid units, distinguishing an obstacle area and a passable area; defining a fitness function: converting a path planning problem into an optimization problem, and constructing the fitness function taking a path length and an obstacle avoidance constraint as targets; a rat optimization algorithm is initialized, wherein the position and algorithm parameters of a rat population are initialized; according to the method, the rat optimization algorithm is adopted, local optimization is effectively avoided through diversified behavior modes, the global optimization capability is enhanced, and the convergence speed and the optimization precision are improved while global search is ensured by the algorithm by introducing the adaptive weight and improving the local search strategy.

The invention provides a mobile robot path planning method based on egret group optimization, and the method comprises the steps: carrying out the population initialization through Tent chaotic mapping in an exploration stage, and reducing the path search cost; a sine and cosine algorithm and a greedy strategy are adopted to improve the position updating of the egret individuals so as to balance the local development and global search capability of the method; a Levy flight method is adopted to escape from a local optimal trap; and a safe and reliable planning path is obtained by using an area limit value method and a feasible point coordinate fine adjustment strategy. And in the optimization stage, a shortest path method, redundancy removal operation and a segmented Bezier curve method are adopted to optimize the trajectory, so that the final motion trajectory of the mobile robot is obtained. The mobile robot path planning method based on egret group optimization has the advantages of being high in path planning efficiency, short in total consumed time, better in path, and capable of reducing the number of path turning times, reducing the number of path nodes and enhancing the local optimal escape ability.

The invention discloses a mobile robot path planning method based on deep reinforcement learning, and relates to the technical field of robot path planning. According to the method, a mobile robot path is divided into global planning and local obstacle avoidance, and local obstacle avoidance is realized through a BiLSTM-D3QN model; analyzing the first sensing data through a decision branch of the BiLSTM model to obtain a decision parameter, and comparing the decision parameter with a set parameter threshold to judge whether local obstacle avoidance is performed or not; when it is determined that local obstacle avoidance needs to be carried out, the BiLSTM model outputs obstacle avoidance parameters, then a D3QN model is used for generating a local obstacle avoidance path, and autonomous obstacle avoidance of the mobile robot is achieved; according to the invention, the BiLSTM model bidirectionally captures the context dependency relationship of the time sequence data, can simultaneously fuse forward and backward time sequence features, judges whether local obstacle avoidance is needed or not by using multi-dimensional data, defines the access opportunity of local obstacle avoidance, and improves the accuracy and reliability of local obstacle avoidance.

The invention provides a mobile robot path planning method based on an improved adaptive dynamic window method, and relates to the technical field of mobile robot path planning. Firstly, a high-precision grid map of a robot working environment is established, an improved A-star algorithm is utilized to generate a global optimal path, redundancy elimination optimization and arc interpolation smoothing processing are carried out, and then an optimal path is selected according to an evaluation function of an improved adaptive dynamic window method and a deflection degree screening mechanism. The improved A-star algorithm and the improved dynamic window method are fused to obtain a fusion algorithm having the advantages of the two algorithms, the improved A-star algorithm improves the algorithm search efficiency, and redundancy elimination optimization and arc interpolation smoothing improve the smoothness of the path. And by using the self-adaptive dynamic window method based on deflection degree screening, the speed of the algorithm is improved, obstacles can be avoided more effectively, the running speed of the robot in a narrow channel is improved, and the running time of path planning is effectively shortened.

The invention discloses a ground mobile robot path planning method and system, and relates to the technical field of intelligent robot autonomous control. The method comprises the following steps: constructing a grid map model of a robot moving environment; based on the grid map model, establishing a kinematic model of the robot and setting a movement rule, and initializing to generate a plurality of paths from a starting point to an ending point to form an initial path set; iteratively merging and optimizing the paths in the initial path set by adopting a group merging strategy, and screening out a current global optimal path; based on the current global optimal path guidance, a derivative path is updated based on an improved pigeon inspired optimization map and a compass operator, and a first-stage optimization set is obtained; and through iterative optimization of a landmark operator, dynamically calculating a landmark center according to a cost value and updating a path position to obtain a second-stage optimization set. And selecting an optimal path and carrying out connection smoothing processing to generate an optimal complete path. According to the method, the global optimal path can be efficiently and stably planned in a complex environment.

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

The invention discloses a mobile robot path planning method suitable for a complex environment, and the method comprises the following steps: 1, obtaining an environment map, determining a path planning starting point A (xs, ys) and an end point B (xg, yg) in the environment map, and converting the path planning starting point A (xs, ys) and the end point B (xg, yg) into a binary map; 2, when no obstacle exists between the straight line segments # imgabs0 # of the path planning starting point and the path planning ending point in the environment map, directly taking the path # imgabs1 # connecting the path planning starting point and the path planning ending point as a planning path; or, when one or more obstacles exist between a path planning starting point and a path planning ending point straight line segment # imgabs2 # in the environment map, one or more segmented paths are determined according to the positions of the obstacles, and each group of segmented paths connected end to end between the path planning starting point and the path planning ending point are spliced to serve as alternative planning paths; and step 3, outputting the robot path obtained by planning. The invention further discloses a path planning system for realizing the path planning method and application, and the path planning system and the application have wide application scenes.

The invention relates to the technical field of robots, in particular to a mobile robot mixed path planning method and application. A path planning algorithm is researched and improved according to a global path planning algorithm based on a static environment and a local path planning algorithm based on a dynamic environment, and the improved global path planning algorithm and the improved local path planning algorithm are fused. And generating a mobile robot path planning algorithm meeting the requirement of the mobile robot on the basis of the time elastic band model in a complex environment. Therefore, the mobile robot has the capability of autonomously planning an efficient and smooth path which avoids the collision risk and meets the requirements of an application scene in a changeable environment.

The invention provides a mobile robot path planning method based on a Kindynamic RRT * algorithm in the field of mobile robot global path planning, which comprises the following steps of: 1, updating a global map, determining a starting point and an ending point and setting related parameters; 2, sampling by using a target deviation strategy; 3, searching for adjacent points by using a KD-Tree; 4, checking node connection possibility by using an expansion function, and expanding nodes according to a kinematic model; 5, calculating the cost between the nodes by using a cost function, and further searching a minimum cost path between the nodes; 6, pruning the final path; and 7, repeating the steps until the optimal path from the starting point to the ending point is found or after a preset condition is met, checking whether the path is completed, if so, outputting the path, otherwise, returning to the step to continue searching. According to the technical scheme, the path planning efficiency is improved, the stability and the accuracy are enhanced, and the mathematical model and the algorithm are simplified, so that the method is particularly suitable for a mobile robot platform with limited computing resources.

The invention belongs to the field of mobile robot path planning, and relates to a multi-agent path planning method coupling multi-task sorting and priority inheritance, which comprises the following steps of: acquiring motion area information of a mobile robot, constructing a kinematics model of the robot, and setting a re-planning frequency; a robot task sequence is reordered by adopting a hybrid particle swarm optimization algorithm based on a Manhattan distance, conflicts among all agents are processed through a high-layer multi-agent path planner, and a path conforming to priorities, kinematic models and time constraints is generated for each agent through a low-layer single-agent path planner. The path generated by the method not only can avoid collision with obstacles, but also can effectively avoid mutual conflicts among intelligent agents, can continuously run in an environment of task implementation dynamic allocation, gives a conflict-free path for the robot in a short time, and does not need secondary optimization of a planned track, so that the path planning efficiency is improved. And the method can be directly applied to actual industrial scenes.

The invention discloses a mobile robot path planning method based on JPS. The method comprises the following steps: modifying a heuristic function of an algorithm; setting forced neighbor determination, and obtaining a path by using an improved JPS calculation method; and optimizing the calculated path by using a path detection algorithm, and optimizing the inflection points of the corners by using a Durbin curve. The problems of time efficiency, path planning credibility and the like of robot path planning calculation are researched, an A * algorithm and a JPS algorithm are disassembled, the algorithm conditions of the A * algorithm and the JPS algorithm are organically combined, the advantages of the A * algorithm and the JPS algorithm are selected through internal problems of the algorithms, and a new algorithm is formed through fusion. And checking whether the path condition can be optimized to reduce the path points or shorten the path or not by using a path detection algorithm, and improving the path efficiency, reliability and path length by using the path detection algorithm.

The invention discloses an autonomous mobile robot path planning obstacle avoidance method and system, relates to the technical field of autonomous mobile robot path planning and obstacle avoidance, and is used for solving the problems that in a deep mine multi-layer roadway, multiple environmental risks are difficult to quantify, and once a key bottleneck channel is blocked, task failure is easily caused, and the reliability of path planning is insufficient. After an autonomous mobile machine is powered on, sensor calibration parameters are loaded, pose fusion is completed through extended Kalman filtering based on a multi-source odometer, an inertia sensor and a laser vision sensor, and an occupation grid map with static obstacles, dynamic candidates and passable marks is constructed. The method comprises the following steps: constructing a simultaneous multi-cause environmental risk feature vector to obtain an environmental comprehensive risk cost, performing layered screening and comprehensive cost sorting on search nodes in combination with task blocking sensitivity of off-line calculation on a mine laneway traffic map, and then generating a time parameterized speed reference trajectory; the Mecanum wheel omni-directional drive mechanism is guided to pass through the high-risk area along the safe and smooth path.

The invention belongs to the technical field of robot path planning, and provides a mobile robot path planning method and system based on an adaptive RRT * algorithm, and the method comprises the steps: initializing a multi-tree structure based on an expansion superposition method and clustering analysis; determining a dynamically adjusted sampling area by adopting a self-adaptive sampling strategy; new nodes are generated in the sampling area, collision detection is carried out on the new nodes, if the new nodes do not collide with obstacles, the new nodes are incorporated into a tree structure, and path optimization operation including father node reselection and rewiring is executed; the connection possibility among the multiple trees is checked regularly, when the starting point tree and the target tree are connected through the guide tree and a feasible path is detected, a complete initial path is extracted, and the sampling area is updated after the path is optimized; evaluating the path cost of the new node and the target point, and if the path cost meets the precision requirement or reaches the maximum number of iterations, terminating the search and outputting the current optimal path; otherwise, continuing to optimize. According to the invention, the adaptability and efficiency of a path planning algorithm in a complex environment are improved.