61 results about "Mobile robots path planning" patented technology

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 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 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 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.

The invention discloses a path planning method and device based on signal sequential logic and nerve guidance sampling, and relates to the technical field of mobile robot path planning. The method comprises the steps of obtaining working environment information of the mobile robot and converting the working environment information into an environment point cloud data set; analyzing and splitting a to-be-executed signal sequential logic task into a plurality of sub-tasks; for each subtask, inputting the environment point cloud data and the dynamically updated starting and ending point masks into a pre-trained PointNet + + deep network, and outputting a guide point cloud data set; carrying out mixed sampling by adopting a fast exploration random tree star algorithm and generating a new node; selecting a father node for a new node by using a hybrid cost function fused with sequential logic robustness, and recursively updating the accumulated cost; if it is judged that the trajectory meets all subtask constraints in sequence, the optimal path meeting global signal sequential logic constraints is extracted, output and executed, and the path planning success rate and solving efficiency in the complex environment are remarkably improved.

The invention provides a mobile robot path planning method and system based on a marine predator algorithm and a storage medium, and relates to the technical field of mobile robot path planning, and the method comprises the specific steps: S1, collecting motion state parameters and information parameters of N obstacles with the same size on a robot moving path, the motion state parameters of the obstacle comprise a moving state and a static state, and the information parameters of the obstacle comprise the real-time position of the obstacle, the real-time distance between the obstacle and the robot and the moving speed. The method comprises the following steps: dividing the obstacle into a static group and a mobile group through a clustering algorithm, analyzing the position of the obstacle in the static group and the distance between the obstacle and the robot, obtaining the shape feature of the obstacle, obtaining the shape of the mobile obstacle through static map matching, feature extraction and template matching, and obtaining an optimal path solution according to a target function. Therefore, the moving path of the robot is comprehensively optimized, and the precision of path planning is improved.

The application relates to a mobile robot path planning method based on an improved bidirectional RRT algorithm, and belongs to the technical field of mobile robot path planning. a b The bidirectional random tree T a b is constructed by taking a starting point and an ending point as root nodes, adaptive sampling is realized by adopting a directional deviation heuristic function containing a bias angle sampling strategy, node distribution is optimized by combining a redundant point removal strategy based on adaptive adjustment of an environmental complexity threshold, and the random tree is directionally expanded by introducing an improved artificial potential field method. The application can quickly generate a safer optimal path which is shorter and smoother, significantly improves the efficiency and robustness of path planning, and is suitable for complex scene path planning tasks of various mobile robots such as service robots and unmanned toy cars.

The invention discloses a wheeled mobile robot path planning method integrating an improved A * algorithm and an enhanced DWA algorithm, and belongs to the technical field of mobile robot autonomous navigation. Firstly, in a global planning layer, based on a robot kinematics model, an A * algorithm is improved, and an adaptive weight heuristic function, a direction priority expansion strategy, an obstacle sensing node selection mechanism and a path smoothing post-processing method based on sight line detection are introduced. Then, in a local planning layer, an enhanced DWA algorithm is provided, and a look-ahead point from a global path is introduced as a local planning target to guide the robot to advance along a global optimal path. The problems that a traditional A * algorithm is low in search efficiency and insufficient in path smoothness and a traditional DWA algorithm is prone to falling into a local minimum value can be solved, the method is superior to a traditional method in the aspects of search efficiency, path smoothness, obstacle avoidance capacity and real-time performance, efficient, safe and stable navigation of the mobile robot in a complex environment can be achieved, and the method is suitable for popularization and application. And the method has high engineering application value.

This invention relates to a mobile robot path planning method for optimizing safe distance, belonging to the field of mobile robot path planning technology. The method introduces a soft and hard constraint point judgment method to expand new nodes in the traditional A* path planning method, and adds turning penalty cost and soft and hard constraint point cost during the cost calculation process. After obtaining the path nodes, the path curve is smoothed and optimized using Bézier curves to achieve the optimal planned path while ensuring motion safety.

The invention discloses a path planning method and device for a port yard multipoint inspection robot, and belongs to the technical field of mobile robot path planning. The method adopts a double-layer architecture of upper layer YARD-RRT * global planning and lower layer UO-MPC local rolling optimization: the upper layer introduces turning cost and an adaptive sampling strategy on the basis of a real-time laser map, and quickly generates a smooth and traceable global path; and the lower layer is based on unified obstacle perception model predictive control, superellipse, multi-layer circle nesting and capsule reachable set constraint are respectively established for the static container, the moving obstacle and the shielding area, and safe speed and course instructions are output in a rolling manner. The robot continuously updates the local map in the driving process, clears the map after arriving at each target point, and then starts the navigation of the next section, thereby achieving the multi-point inspection task of efficient passage in a narrow channel of a storage yard and safe obstacle pre-avoidance in a sheltered area. Compared with the prior art, the method has the advantages that the passing rate, the safety and the operation continuity in a complex storage yard environment are remarkably improved.

The application relates to the field of mobile robot path planning, and particularly relates to a robot rapid path optimization method based on multi-strategy fusion, which comprises the following steps: step 1, initializing iteration greedy algorithm parameters, and generating an initial path by using a bidirectional rapid expansion random tree method; step 2, using a difference guided path optimization mechanism to destruct and reconstruct the path, and expanding a path exploration space; step 3, performing path pruning, node insertion and node mutation operations by selecting an adaptive adjustment strategy, and locally searching the current path; step 4, accepting the path according to a simulated annealing criterion; step 5, judging whether the algorithm termination time is reached; if the algorithm termination time is reached, the algorithm ends, and the current best path is output; otherwise, returning to step 2. The application effectively solves the problems of minimizing path length and path smoothness in robot path planning, and has the positive effects of improving path planning efficiency and optimizing path quality.

This application discloses a U-Q-Learning-based mobile robot path planning method in unknown environments, comprising: designing a composite reward function including a repetitive movement penalty, a distance reward, and a state transition heuristic reward; initializing a Q-table, an action selection count table, and a state visit count table, as well as an exploration rate, a learning rate, and a discount factor; calculating the upper confidence bound of actions based on the Q-value of the state, the number of action selections, and the number of state visits, and dynamically adjusting the exploration rate; if the current exploration rate is large, randomly selecting an action from the action space; otherwise, selecting the action with the largest upper confidence bound; the robot executing the selected action, transitioning to the next state, and calculating the immediate reward; adaptively adjusting the learning rate, updating the current Q-value by combining the immediate reward, the discount factor, and the maximum Q-value of the next state, updating the state to the next state, repeating multiple iterations until the algorithm converges, and outputting the optimal path based on the optimized final Q-table. This application can improve the efficiency and stability of path planning.

The invention discloses a multi-infrared fusion bed surface cleaning robot dynamic path planning system, relates to the technical field of intelligent bed surface cleaning and mobile robot path planning, and is used for solving the problems of low coverage efficiency, insufficient anti-falling safety and difficulty in flexible obstacle avoidance of a bed surface cleaning robot in a high bed complex bed surface scene. The bed surface cleaning robot constructs local grid environment representation and bed edge high-risk marks under a body coordinate system of the bed surface cleaning robot through calibration of a multi-path infrared obstacle avoidance sensor and a falling sensor and establishment of a unified time reference, and the local grid environment representation and the bed edge high-risk marks are fused with a static working grid map. Edge exposure intensity, flexible disturbance intensity and support stability are introduced to construct a multi-dimensional risk field and dynamic risk budget, linear velocity and angular velocity reference values are screened, and a rolling brush and a dust collector are driven to perform cleaning and sweeping.

The invention discloses a mobile robot path planning method and device based on T-RRT and RRT * deep fusion of an artificial potential field and a medium, the method uses the artificial potential field to guide path search, then the T-RRT method and the RRT * method are deeply fused, randomness is introduced to serve as a method for avoiding local minimum traps, and meanwhile, in order to further improve efficiency, the method for planning the path of the mobile robot based on the T-RRT and RRT * deep fusion of the T-RRT and the RRT * deep fusion of the T-RRT and the RRT * deep fusion of the T-RRT is provided. Potential state nodes are accepted or rejected by adopting an improved conversion test, so that invalid expansion is controlled, a random expansion tree is expanded to a low-cost space, the convergence speed of the method is improved, a path is continuously optimized while the random expansion tree is expanded to the low-cost space, and an optimal path can be quickly found in a relatively complex environment.

According to the mobile robot path planning method based on the adaptive RRT *-Connect algorithm, firstly, the target offset probability and the extension step length are adaptively initialized, and the two parameters are adaptively adjusted according to the collision detection times in the path planning process. In the sampling and expansion stage, if collision does not occur, target bias and a common guiding strategy are adaptively fused for expansion; otherwise, recording the position of the collision node in real time, dividing safe and dangerous areas according to the position, and adaptively distributing subsequent sampling probabilities according to the area proportion of the divided areas. And when the dangerous area proportion and the collision frequency exceed threshold values, adaptively generating a guide node to guide the random tree to quickly pass through the complex area. Once the distance between the two random trees is smaller than a set threshold value and no collision connection exists, a path is generated. The method can adapt to different environments and rapidly plan the global path, so that the movement requirements of the robot are better met.

The invention belongs to the technical field of mobile robot path planning, and provides a global-local path planning method adaptive to fruit tree inter-row operation. According to the method, a dynamic weight mechanism is introduced into an A * algorithm in global path planning, and the weight of a heuristic function is adaptively adjusted according to two factors, namely the ratio of the distance from a node to a target point and the transverse spacing of fruit trees, so that the search precision is improved; a * algorithm is utilized to pre-plan and construct a real cost matrix which is used for guiding segmented optimization of the ant colony algorithm, meanwhile, an adaptive pheromone volatilization mechanism based on a solution improvement rate is adopted to improve convergence stability, and path smoothing is realized in combination with redundant point deletion. In local planning, a path deviation cost item is introduced into a dynamic window method to quantify trajectory deviation, and a dynamic weighting strategy is adopted to adaptively adjust the weight of each evaluation function according to the obstacle distance. According to the method, efficient, smooth and stable path planning can be realized in a complex orchard environment, and the method is suitable for a mobile robot for fruit tree row-to-row operation.

The invention relates to a mobile robot multi-dimensional path planning method based on an AROA improved optimization algorithm, and the method comprises the steps: obtaining a task scene of a mobile robot, building a grid map model through the task scene, and marking a path starting point, a target point and obstacle information; constructing a multi-objective cost function, and performing path planning on the grid map model by adopting an improved optimization algorithm based on AROA; the improved optimization algorithm based on AROA is obtained by fusing an AROA optimization algorithm with a BKA optimization algorithm; in the path planning process, the multi-target cost function is used for calculating the path adaptability of population individuals, and the individual with the minimum path adaptability serves as the optimal path individual to be used for obtaining the optimal path of the mobile robot. The method can effectively solve the problem that the path planning of the mobile robot is easy to fall into local optimum and slow in convergence speed in a complex multi-dimensional environment, and the precision and engineering practicability of the path planning are remarkably improved.

The application discloses a kind of mobile robot path planning method and system considering narrow channel, including map construction module, initial path solving module and path optimization module;Map construction module: the environment including narrow channel that robot is located is modeled and described;Initial path solving module: improved RRT algorithm fuses the detection mechanism to narrow channel, from the given starting point, by searching the configuration space, generate new node, constantly expand the tree structure constructed by random sampling configuration point, find a collision-free path from initial position to target position;Path optimization module: on the basis of RRT algorithm of fusing narrow channel detection mechanism, improve path node search mode, optimize the initial path processing, find parent node and find out a asymptotically optimal optimization path;The application is high to the response efficiency of narrow channel, fast execution speed, the asymptotically optimal path obtained can guarantee the safety of robot movement.

The application discloses an elastic safety constraint trajectory generation method and system for orthographic image navigation, and relates to the technical field of mobile robot path planning. In view of the problems that the orthographic image has splicing errors, AI recognition leads to fuzzy edges of passable areas, and traditional grid expansion easily causes narrow passages to disappear, the application constructs a grid map by acquiring and preprocessing the orthographic image, and calculates the distance field value of each grid unit to the nearest obstacle. A safety risk penalty term negatively related to the distance field value is introduced in the A* algorithm to dynamically control the safety distance between the path point and the obstacle and generate a global path. The discrete path points are fitted into a time parameterized B-spline curve to generate dense trajectory points. The elastic safety constraint takes into account the passability and safety of the path, generates a smooth and trackable trajectory, improves the autonomous navigation ability and motion control precision of the robot in a complex environment, and can be widely applied to air-ground integrated rescue, field inspection and other scenes.

The invention provides a mobile robot path planning method, system and device and a storage medium, and belongs to the technical field of path planning, and the method comprises the steps: initializing a population and basic parameters of a grey wolf optimization algorithm; calculating a fitness value of each individual in the population according to a fitness function integrating a plurality of path optimization indexes; sorting the fitness value of each individual, and determining a leader wolf; performing population position updating based on the dynamic weight under the leader of the leader wolf; performing path feasibility check on the position of each individual, and performing mutation operation on the individuals in the population based on an adaptive mutation mechanism; and repeating the above steps until a preset iteration ending condition is reached, and outputting an optimal solution to obtain a path planning result of the target area. According to the method, a self-adaptive variation mechanism is introduced into the grey wolf optimization algorithm, so that the capability of jumping out of local optimum of the algorithm is enhanced, the convergence speed and the path planning quality are improved on the whole, and the method can better adapt to a complex environment.

The invention provides a mobile robot path planning method based on an improved ant colony algorithm, and belongs to the field of mobile robot autonomous navigation. Comprising the following steps: S1, initializing a pheromone matrix by using an ant colony algorithm; s2, ant colony algorithm pheromones are dynamically updated; s3, after the maximum number of iterations of the ant colony algorithm is reached, a genetic algorithm is carried out to initialize a population; s4, designing a fitness function; s5, crossover, variation and path smoothing are carried out; and S6, performing final smoothing processing on the optimal path output by the genetic algorithm, and storing and outputting the optimal path. According to the method, the global optimal solution solving capability of the ant colony algorithm is reserved, meanwhile, the convergence speed and the path smoothness are high, and the navigation requirement for the mobile robot in the real environment is better met.

The invention provides an improved artificial potential field method path planning method and system based on a memory potential field, and solves the problem that a conventional artificial potential field method is liable to fall into a local minimum value by constructing a three-component total potential field model containing a target attraction potential, an obstacle rejection potential and a memory potential. The memory potential field records historical track points through a strategy based on space-time density, is constructed in combination with a weight index attenuation mechanism and a Gaussian kernel superposition method, can dynamically mark an explored area and generate repulsive force, and guides the robot to advance towards an unexplored space. Meanwhile, a fixed-capacity circular buffer area is adopted to manage memory points, so that the algorithm practicability is ensured. On the basis that the advantages of calculation simplicity and reactivity of a traditional artificial potential field method are reserved, the method has limited escapability, asymptotic completeness, local stability and global convergence, it can be ensured that the robot safely and efficiently reaches the target point from the starting point in the complex unknown environment, and the method is suitable for a sensor-driven mobile robot path planning scene.