102 results about "Mobile robots path planning" patented technology

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 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 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 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 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 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 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 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.

The invention discloses a convex area coverage path planning method and system for an autonomous sweeper, which are applied to the technical field of path planning of autonomous mobile robots, and the method comprises the steps: generating an area coverage parallel track through a self-adaptive direction track generation algorithm based on a rotary caliper; the method comprises the following steps: constructing a GTSP model based on a region coverage parallel track, and standardizing the GTSP model into an ATSP model by adopting Noon-Bean conversion; and solving the ATSP model by adopting an LKH-VNS hybrid search algorithm to obtain a path planning result. According to the method, efficient and intelligent path planning is realized through adaptive direction orbit generation, standardized modeling from the GTSP to the ATSP and collaborative optimization of the LKH-VNS hybrid search algorithm.

The invention discloses a mobile robot path planning method based on reinforcement learning. The method comprises the following steps: firstly, designing a double-experience pool to store experiences in positive and negative aspects respectively, and setting a sampling proportion of the two experience pools to balance a relationship between exploration so as to accelerate a convergence rate; secondly, the generation direction of the repulsive force is optimized, an artificial potential field method is enhanced, an improved APF method is used for replacing a Double DQN random exploration strategy, and the convergence speed is further increased; and finally, eliminating redundant nodes by analyzing the spatial relationship between the path and the obstacle, and smoothing the path by using a Bezier curve, thereby improving the quality of the path. According to the invention, the learning network training process is optimized, so that the robot can make the optimal path planning action.

The invention discloses a mobile robot path planning method and system fusing an improved A * algorithm and an adaptive genetic algorithm, and the method comprises the steps: reducing redundant search in a dense obstacle region and enhancing path exploration in a sparse region through improving a dynamic weight mechanism of the A * algorithm, and giving consideration to the search efficiency and path optimality; the problems that a traditional algorithm is slow in convergence and prone to local optimum are solved in combination with an adaptive genetic algorithm, meanwhile, a path repairing mechanism is introduced, A * search and replacement point adjustment are dynamically fused, environment changes are dealt with in real time, the average path is shortened, and the path planning success rate under environment disturbance is increased. The advantages of the two algorithms are complementary, the improved A * algorithm provides a high-quality initial path, the adaptive genetic algorithm optimizes multi-target parameters, and finally the optimal comprehensive performance is realized in the aspects of path length, smoothness and obstacle avoidance adaptability.

The invention provides a mobile robot path planning method, device, equipment and medium, and relates to the technical field of path planning, and the method comprises the steps: guiding the expansion direction of a random tree of an RRT * algorithm through an artificial potential field method according to a starting point and a target point of a mobile robot, preferentially selecting sampling points of the random tree, and deleting redundant nodes, generating a global planning path; determining a current position and a local target point based on the global planning path; and according to the current position and the local target point, adopting the artificial potential field method and the simulated annealing algorithm strategy to generate a local planning path. The method can solve the problems that an RRT * algorithm is poor in guidance and difficult to approach to a target point quickly, rejects invalid nodes far away from a target, improves the sampling pertinence, and can guide a robot to be separated from a local extreme value.

The invention relates to the technical field of path planning, in particular to a mobile robot path planning method based on an improved artificial potential field, and the method comprises the steps: obtaining environment information of a mobile robot, and constructing a plane coordinate system model of the mobile robot, an obstacle and a target; an improved artificial potential field method is constructed, the distance between the target and the obstacle is compared with an influence distance threshold value of the obstacle, and whether the included angle between the mobile robot and the center points of the target and the obstacle is introduced or not is judged according to a comparison value. According to the method, the problems of target unreachable and local minimum when the path is generated by an artificial potential field method are solved.

The present invention discloses a mobile robot path planning method, system, robot and storage medium, and relates to the field of path planning technology. The mobile robot path planning method comprises: using a grid method to divide a map into 1×1 grids, using preset color blocks to represent traversable areas or obstacles; establishing a mathematical model; initializing whale optimization algorithm parameters; initializing the population using the Tent chaotic sequence strategy; performing a linear equidistant interpolation strategy on all whale individuals to add new position vectors; calculating fitness; selecting the whale individual with the smallest fitness as the current global optimal individual; updating the whale individual; when the current number of iterations t reaches the maximum number of iterations T, outputting the current global optimal individual; calculating and generating a cubic uniform B-spline curve to complete the path curve construction. The mobile robot path planning method proposed in the present invention has the characteristics of fast running speed, high solution quality, strong optimization ability, etc., and can better solve practical problems.

The invention discloses a mobile robot high environment adaptability path planning method based on IDBVSB-P-RRT. The mobile robot high environment adaptability path planning method comprises the following steps of S1, a sampling stage; the method comprises the following steps: firstly, defining two extension trees V1 and V2, two extension lists E1 and E2 and a list Xsoln; s2, an expansion stage; s3, a collision detection stage; s4, a connection stage; and S5, performing a pruning strategy. On the basis of the RRT algorithm, the sampling area is optimized in the sampling stage, the search range is limited within an oval range, the accuracy of the sampling search range is higher, the operation cost of sampling, expansion and collision detection is greatly reduced due to the optimized sampling area, and the IDBVSB-P-RRT algorithm has the advantages that the algorithm is simple, the algorithm is convenient to operate, and the reliability is high. The complexity in the path planning process of the mobile robot can be remarkably reduced, and the path planning efficiency is improved.

The invention relates to an improved QR-DQN algorithm based on average value estimation and mixed priority experience playback. The improved QR-DQN algorithm comprises the following steps: step S101, constructing an average quantile estimation mechanism; step S102, designing a mixed priority experience playback mechanism; step S103, jointly optimizing a path planning algorithm; step S104, carrying out iterative training and parameter updating; and S105, outputting a path planning result and an evaluation index. According to the mobile robot path planning algorithm based on the improved QR-DQN, a plurality of target network output mean values are integrated through a mean value estimation mechanism, quantile estimation deviation is reduced, and the stability of action value distribution is improved; a mixed priority experience playback mechanism is designed, the sampling weight is dynamically adjusted according to the sample error and the reward value, and the utilization rate of high-value experience is increased.

The present application relates to the technical field of industrial robot path planning, in particular to an industrial mobile robot path planning method based on artificial potential field method and dynamic prediction. Firstly, the present application collects environment point cloud data, and distinguishes static and dynamic obstacles based on time difference and Euclidean distance; the future position of dynamic obstacles is predicted by using Kalman filter, and the static obstacles are simplified by using density clustering algorithm; the improved artificial potential field function is constructed by combining target point, static obstacle set and dynamic obstacle prediction result to calculate attraction and repulsion; based on the robot motion state, the potential collision risk is judged, the repulsion of dynamic obstacles with risk is brought into the potential field synthesis in advance, and the final resultant force guiding the motion is generated; when it is detected that the local optimum or oscillation state is trapped, the virtual attractive point escape strategy is executed until the target point is reached. The present application realizes the fusion of static clustering, dynamic prediction and improved potential field, and improves the obstacle avoidance and path planning efficiency in complex dynamic environment.

The application 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: a grid map model of a robot moving environment is constructed; a kinematic model of the robot is established based on the grid map model, and a moving rule is set; a plurality of paths from a starting point to an ending point are initialized to generate an initial path set; a group merging strategy is used to iteratively merge and optimize the paths in the initial path set, and a current global optimal path is selected; the current global optimal path is used as a guide to update and derive paths based on an improved pigeon optimization map and a compass operator to obtain a first-stage optimization set; landmark operators are iteratively optimized to dynamically calculate landmark centers and update path positions based on the value of the iteration to obtain a second-stage optimization set; an optimal path is selected and connected and smoothed to generate an optimal complete path; and the application can efficiently and stably plan a global optimal path in a complex environment.

The present invention discloses a path planning method for a mobile robot, a mobile robot, and a storage medium. The method comprises: offline establishment of a sparse directed connected graph based on a probability map; and online path search based on the sparse directed connected graph to plan the optimal path for the mobile robot. Embodiments of the present invention utilize the sparse directed connected graph for path search, improving the speed and efficiency of path search compared to traditional methods by establishing a sparse directed connected graph offline based on a probability map and performing online path search based on the sparse directed connected graph to plan the optimal path for the mobile robot.

The application relates to the technical field of DQN algorithm, in particular to a mobile robot path planning method based on DM-DQN, which comprises the following steps: a mobile robot path planning model based on DM-DQN is established; a state space, an action space, a DM-DQN network model and a reward function of the DM-DQN algorithm are designed; the DM-DQN algorithm is trained to obtain an experienced reward value, and collision-free path planning of a robot is completed. The application introduces a competitive network structure, decomposes the network structure into a value function and an advantage function, thereby decoupling action selection and action evaluation, so that the state is no longer completely dependent on the value of the action for judgment, individual value prediction can be carried out, and the problem of slow convergence speed is solved; and through the design of a reward function based on an artificial potential field, the problem that the robot is too close to the edge of an obstacle is solved.

The invention discloses a trajectory generation method based on shielding geometric modeling and risk quantification, and relates to the technical field of mobile robot path planning. The method comprises the following steps: blind area modeling: enabling a shielding object in any shape to be equivalent to an approximate model, establishing association between the area of a blind area and the distance between a robot and the shielding object, and forming a quantitative risk index; visible leading edge extraction: obtaining three-dimensional point cloud data through a sensor, and identifying leading edge points and leading edge representative points through screening, polar coordinate mapping and sorting; performing visibility leading edge screening, and obtaining a stable and effective leading edge point set through motion direction, collinearity constraint and multi-frame point cluster management; generating a global trajectory, and providing long-term direction reference by adopting a path search algorithm; guide track generation: determining an initial guide track based on the global track, and adjusting track point positions and maximum speed limitation in combination with leading edge representative point distribution; and optimizing the trajectory, and constructing a convex quadratic programming problem.

The present application relates to mobile robot path planning technical field, especially to a kind of grinding machine full area coverage path planning method.The technical scheme includes:by obtaining work environment grid map and carrying out inflation treatment to obstacle to obtain safety grid map, the passable area is marked and connected component is constructed Room adjacency graph, determine room access order;In each room, according to the aspect ratio of circumscribed rectangle to generate parallel sweep section, and the path connection between sweep section and room is realized using A star algorithm, while path straightening and circular arc smoothing processing are carried out.The present application divides room by work area and combines sweep path generation and path smoothing optimization, realizes the full area coverage of grinding machine work path, reduces path redundancy and turning number, improves path smoothness and work efficiency.