240 results about "Mobile robots path planning" patented technology

A temporal controller for mobile robot path planning includes a sensor module for receiving data corresponding to spatial locations of at least one object, and a temporal control module operatively coupled to the sensor module, the temporal control module configured to predict future locations of the at least one object based on data received by the sensor module. The controller further includes a temporal simulation module operatively coupled to the temporal control module, wherein the temporal simulation module configured to use the predicted future locations of the at least one object to simulate multiple robot motion hypothesis for object avoidance and trajectory planning.

The invention discloses a mobile robot path planning and obstacle avoidance method and system. The mobile robot path planning method comprises the following steps: establishing a two-dimensional grid map by utilizing known obstacle environment information; in the two-dimensional grid map, establishing a global coordinate system at the place of a mobile robot, and setting a starting point and a terminal point of the mobile robot; determining the shortest path between the starting point and the terminal point through a jump point search algorithm, wherein the shortest path comprises a plurality of local target points connected in sequence; and in the process of controlling the mobile robot to move to each of the local target points, utilizing a local obstacle avoidance algorithm to avoid a dynamic obstacle. The mobile robot path planning and obstacle avoidance method adopts the jump point search algorithm to obtain the shortest path quickly, so that path search efficiency can be improved, and storage space is saved; and through the local obstacle avoidance algorithm, accuracy and real-time performance of mobile robot path planning and obstacle avoidance can be ensured, and autonomous navigation of the mobile robot is realized.

The invention relates to a method for planning a path for a mobile robot based on environmental modeling and a self-adapting window, which relates to a method for planning a real-time path for the mobile robot. The method comprises the following steps of: performing modeling and analysis on a multiple constraint local environment; performing passable analysis; performing safety analysis; performing motion smoothness analysis; performing goal-directed analysis; and performing path planning by adopting the self-adapting window. Because the method has better environmental suitability and obstacle avoiding capacity, the method obtains good safety and reachability, has high calculation real-time property, so the method solves the problem that the mobile robot generates an obstacle avoidance path in real time in an uncertain complex environment, provides a path selection method with optimized integration, better meets the requirements on obstacle avoidance for the mobile robot, realizes the real-time path planning and control of the robot, and provides an effective collision-less path planning method for the navigation application of the mobile robot.

The invention discloses a mobile robot path planning method based on an improved RRT* algorithm. The method introduces a target biasing strategy into a standard RRT* algorithm so as to reduce the randomness of sampling points; provides an avoidance step length extension method in order that a random tree can reasonably stay away from an obstacle area and avoids falling into a local minimum; and smoothes a path obtained by the improved RRT* algorithm by using a reverse sequence connection method smoothing strategy, so as to reduce the direction-changing operations of the robot and achieve the stable movement of the robot. Compared with an original standard RRT* algorithm, the improved RRT* algorithm has a better planned path and takes less time.

The invention provides a mobile robot path planning algorithm based on single-chain sequential backtracking Q-learning. According to the mobile robot path planning algorithm based on the single-chain sequential backtracking Q-learning, a two-dimensional environment is expressed by using a grid method, each environment area block corresponds to a discrete location, the state of a mobile robot at some moment is expressed by an environment location where the robot is located, the search of each step of the mobile robot is based on a Q-learning iterative formula of a non-deterministic Markov decision process, progressively sequential backtracking is carried out from the Q value of the tail end of a single chain, namely the current state, to the Q value of the head end of the single chain until a target state is reached, the mobile robot cyclically and repeatedly finds out paths to the target state from an original state, the search of each step is carried out according to the steps, and Q values of states are continuously iterated and optimized until the Q values are converged. The mobile robot path planning algorithm based on the single-chain sequential backtracking Q-learning has the advantages that the number of steps required for optimal path searching is far less than that of a classic Q-learning algorithm and a Q(lambda) algorithm, the learning time is shorter, and the learning efficiency is higher; and particularly for large environments, the mobile robot path planning algorithm based on the single-chain sequential backtracking Q-learning has more obvious advantages.

The invention relates to a mobile robot path planning method used in an intensive storage zone. The mobile robot path planning method is technically characterized by comprising the following steps: step 1, an initialized net type path diagram is set up by a scheduling layer server; step 2, a cargo storage task is divided into an empty trunk cargo searching subtask and a loading delivering subtask by the scheduling layer server; step 3, a dynamic planning path of the mobile robot is established by the scheduling layer server and a dynamic planning path of the mobile robot is sent to the mobile server; step 4: the mobile robot executes the corresponding subtask; step 5: the mobile robot sends a state report of the mobile robot to the scheduling layer server; and step 6, the scheduling layer server updates the current position, the load state, and a weight related to an original starting point and a target point of the mobile robot. By dividing every task into the empty trunk cargo searching subtask and the loading delivering subtask, according to the planning paths of different subtasks, the complexity of a safety collision prevention operation of a plurality of mobile robots is reduced, and the instantaneity and the effectiveness of the system are improved.

The invention provides a global path planning method of a moving robot under a dynamic and complicated environment. The method includes the steps of building a global environment map according to an actual environment, building a dynamic barrier environment, obtaining a grid map by means of a grid method, converting a barrier distribution map obtained in the grid method into an empowered adjacent matrix of the map, carrying out global path planning on the environment by means of an ant colony algorithm, processing trap problems in the environment in a regressing method, judging whether a current position of a path is a target point, repeating the preceding steps if the answer is negative, and ending if the current position is the designated target point. The method is simple and easy to achieve, and the path planning effect is good.

The invention provides a mobile robot path planning method with combination of a depth automatic encoder and a Q-learning algorithm. The method comprises a depth automatic encoder part, a BP neural network part and a reinforced learning part. The depth automatic encoder part mainly adopts the depth automatic encoder to process images of an environment in which a robot is positioned so that the characteristics of the image data are acquired, and a foundation is laid for subsequent environment cognition. The BP neural network part is mainly for realizing fitting of reward values and the image characteristic data so that combination of the depth automatic encoder and the reinforced learning can be realized. According to the Q-learning algorithm, knowledge is obtained in an action-evaluation environment via interactive learning with the environment, and an action scheme is improved to be suitable for the environment to achieve the desired purpose. The robot interacts with the environment to realize autonomous learning, and finally a feasible path from a start point to a terminal point can be found. System image processing capacity can be enhanced, and environment cognition can be realized via combination of the depth automatic encoder and the BP neural network.

The invention discloses a mobile robot path planning method. The method comprises the steps that the three-dimensional grid map of a target area is acquired, wherein the three-dimensional grid map is generated according to a three-dimensional point cloud graph; the target location and the current location of a mobile robot are acquired; and according to the three-dimensional grid map, a planned path to the target location is generated. According to the invention, the path planning of the mobile robot is carried out by using the three-dimensional grid map; the height information of an obstacle can be acquired through the three-dimensional grid map; and the obstacle avoidance function of the planned path can be effectively optimized. The invention further discloses a mobile robot path planning device which has the advantages.

The invention discloses a mobile robot path planning method based on an ant colony algorithm under a dynamic environment. The method comprises the following steps of 1, modeling the environment by using a grating method; 2, building local diffusion information pheromone tau''rs; 4, computing a transition probability; 5, adding a feasible node S into a tabu table; 6, completing paths; 7, acquiringthe shortest path via path information recorded in the tabu table; 8, updating pheromone matrix elements; and 9, finding the optimal path via iteration, that is to say an algorithm ending condition ismet, and outputting a final result. According to the ant colony algorithm based on a potential field method provided by the invention, the faster convergence speed and optimizing ability are provided, the local diffusion information pheromone has relative good smoothness, the collaboration ability of individual ants is further enhanced, the local cross paths are reduced, the quantity of lost antsis reduced, and thus the ant colony algorithm is converged to the global optimum with the faster speed, and meanwhile also achieves the diversity of learning, and the defect that an artificial potential field method is easy to trap in the local optimum is overcome.

The invention relates to a mobile robot path planning method and system based on an improved ant colony algorithm. The mobile robot path planning method comprises the following steps of 1 environment modeling, 2 initial pheromone distribution and 3 optimal path searching and optimal path outputting. According to the mobile robot path planning method and system based on the improved ant colony algorithm, improvement is conducted on a previous traditional ant colony algorithm in the initial pheromone distribution aspect, ants can guide optimization of the ants at the beginning, and the early convergence speed is obviously increased; meanwhile initial parameters are reasonably selected, for instance, selection of pheromone evaluation, a result does not run into a locally optimal solution or has difficulty in forming an optimal solution, reasonable improvement is conducted on a pheromone update mode, the situation of running into the locally optimal solution can be effectively avoided, and the working efficiency and working reliability of the robot can be improved.

The invention relates to a mobile robot path planning method based on improved A-star algorithm, and belongs to the field of mobile robot path planning. The method is as follows: firstly performing global path pre-planning in a static obstacle environment; then performing secondary smoothing on path points of the global path pre-planning to obtain a pre-planned path; and finally adopting an artificial potential field method and a method for global path pre-planning in the static obstacle environment to perform local path planning when encountering a dynamic obstacle. The method is no longer limited to points in a grid. In a path-finding process, a current node and an extended node are connected, and the obstacle is detected by judging whether a route point connection line and an obstacle edge have intersections.

The invention relates to a mobile robot path planning method and system based on a genetic ant colony algorithm. The mobile robot path planning method includes the steps that 1, modeling is conducted on the environment by establishing a coordinate system; 2, part of optimal solutions obtained through the genetic algorithm are converted to pheromones initial values of the ant colony algorithm; 3, optimum path search is conducted again through the ant colony algorithm, after optimum path search is ended, interlace operation is conducted on paths meeting the requirements of conditions, and the optimum path is finally obtained. The mobile robot path planning method and system overcome inevitable defects existing in a single ant colony algorithm, in other words, the ant colony algorithm is greater in blindness at the initial stage of search, the ant colony algorithm and the genetic algorithm are complementary in advantages, the search range of path search is shortened, and search efficiency of the optimum path is improved.

The invention relates to a path planning method for mobile robots based on a particle swarm optimization algorithm. The prior path planning method for the mobile robots has single path selection and is easy to come to a dead end. The method comprises the concrete steps of: modeling the environment first; using a polygon to represent an obstacle; using a point to represent a robot, using a small square frame to represent the original position of the robot; and using a cross to represent a target point, wherein a starting point of the robot is marked as S, and the target point is marked as G; planning paths of the robot by utilizing the particle swarm optimization algorithm; and finally performing the deep first search on the planned paths. The method adds the deep first search into a polar coordinate particle swarm. By the method, the robot can effectively find one collision-free path in most of complex environments, and finally reach target points.

The invention relates to a mobile robot path planning method based on improved A star strategy. The method comprises the following steps: (a) processing laser data and mileage data acquired by a mobile robot by using a slam algorithm; (b) constructing a binaryzation raster map into a Voronoi map by utilizing a dynamic weinuo graph library; (c) constructing a corresponding Voronoi cost map according to the node coordinate on the Voronoi map; (d) transferring the starting position and the final position of the mobile robot to a map coordinate, and checking whether the starting and final coordinated indexes are legal or not; and (e) initializing g cost values of all nodes; and (f) smoothing the whole discrete path by using a smoother. The memory and calculation resources are greatly saved, the algorithmic convergence rate is accelerated; the Voronoi map is fused, so that the path is far away from barriers, and finally a best path is acquired by using the smoother.

The invention discloses a mobile robot route planning method based on a distance grid map. The mobile robot route planning method includes selecting front grids by a moving window method in the process of moving of a robot, updating the distance grid map, managing the front grids to be updated by an array management method during updating, and updating a control model of the mobile robot by an A*search algorithm in the mobile robot route planning method based on the distance grid map. The mobile robot route planning method based on the distance grid map is of great fundamental significance in high-ranking tasks like generating topological maps and planning a route in the unknown environment.

The invention discloses a mobile robot path programming and scheduling method based on visual identification. The method comprises the following steps of acquiring an image of a target area of a path to be programmed, generating a path map of the target area, and determining current position information of a mobile robot; according to the current position information and target position information of the mobile robot, generating a forward route of the mobile robot from a current position to a target position, wherein the mobile robot moves towards the target position according to the forward route; acquiring real-time current position information of the mobile robot in real time; and determining whether to generate a collision event with other mobile robots, if the collision event would be generated, according to the forward route between the real-time current position and the target position, carrying out avoidance scheduling on the mobile robot and the other mobile robots which can be collided with the mobile robot so as to prevent the collision event. In the method of the invention, path programming and avoidance scheduling of the plurality of mobile robots in a complex environment are realized.

The invention provides a mobile robot path planning method in a complex environment. The method is characterized in that 1. information of environment in which a robot is positioned is acquired, and obstacles in the environmental space are indicated by using rectangular enclosing boxes after processing and displayed on a human-computer interaction module; 2. the initial position of the robot is confirmed and recorded as an initial point; a target position expected to be reached by the robot is confirmed and recorded as a target point; 3. the initial point, the target point and the vertexes of all the obstacle enclosing boxes meeting the condition are connected by using line segments, wherein the requirement indicates that the connecting line of any two points does not penetrate through the enclosing boxes, based on which a visual graph is constructed; 4. the optimal path is planned in the visual graph via an artificial immune algorithm, and key nodes in the optimal path are stored; and 5. The entity robot is controlled to start from the initial point, pass the key nodes in the optimal path one by one and finally reach the target point. Algorithm efficiency and convergence rate can be effectively enhanced under the premise of guaranteeing solution of the optimal path.

The invention discloses an indoor moving robot route planning method based on sensor network dynamic environment monitoring. According to the method, a wireless sensor network is used to carry out environment modeling on the indoor dynamic environment, and then a route is planned for the moving robot. Specifically, the indoor roof is divided into grids with a certain density, a node is arranged in each grid so as to form a wireless sensor network (WSN); the nodes in WSN detect the obstacles on the ground though an ultrasonic wave mode, then the feedback discrete data is processed, a whole continuous three-dimensional topographic map can be established; the topographic map is subjected to a binarization treatment to establish a 0-1 grid map so as to construct a dynamic environment model; a Cricket node, which is loaded on a moving robot, is communicated with the WSN network arranged on the roof through a wireless mode, the Cricket node can obtain the relative distances between the Cricket node and three nearest nodes and then is capable of estimating the self-position through least squares algorithm; and finally the dynamic route is planned by the D*Lite algorithm through combining the current position of robot and the obstacle information.

The invention relates to a mobile-robot route planning method based on an improved genetic algorithm. A raster model is adopted to preprocess a working space of a mobile robot, in a rasterized map, an improved rapid traversing random tree is adopted to generate connections of several clusters between a start point and a target point, portions for the mobile robot to freely walk on in the working space are converted into directed acyclic graphs, and a backtracking method is adopted to generate an initial population which is abundant in diversity and has no infeasible path on the basis of the directed acyclic graphs. Three genetic operators, namely a selection operator, a crossover operator and a mutation operator, are adopted to evolve the population, wherein the selection operator uses a tournament selection strategy, the crossover operator adopts a single-point crossover strategy, and the mutation operator adopts a mutation strategy which displaces an aberrance point with an optimal point in eight-neighbor points of the aberrance point. A quadratic b-spline curve is adopted to smooth an optimal route, and finally, a smooth optimal route is generated. According to the method, the route planning capability of the mobile robot under a complex dynamic environment is effectively improved.

The invention provides a path planning method and system for a mobile robot. The method includes: establishing a map according to the traveling environment data of the mobile robot; dividing the map into a plurality of uniform grids to obtain an initial grid map, and determining the initial position and the final position of the mobile robot on the initial grid map; receiving an obstacle signal detected by a sensor on the mobile robot; determining the repulsive force of the mobile robot at the current position according to the obstacle signal; according to the repulsive force of the mobile robot at the current position, constructing an attenuated repulsion field of an obstacle area; assigning values to respective grids in the initial grid map according to the attenuated repulsion field to obtain an assigned grid map; and generating a traveling path of the mobile robot toward the final position by using a non-traversal search approach. The method and system do not involve the vector calculation of potential field force at all when planning an optimal path and eliminates the possibility of deadlock of a local optimum point that may occur in an artificial potential field method.

The invention discloses a mobile robot path planning method based on the binary quantum particle swarm optimization, which is characterized by comprising steps of 1, simplifying a robot into a point moving in a two-dimensional space, and then sensing the present position of the robot and the present positions of obstacles via the visual system, 2, processing all the obstacles sensed by the visual system of the robot into convex polygons, 3, discretizing the two-dimensional space into series grids, and performing binary encoding for eight probable motion directions of the robot at each grid, 4, defining the distance of the path between the starting point and the destination point as a target function required to be solved by the method, and 5, overall optimizing the target function in the step 4 by utilizing the binary quantum particle swarm optimization aiming to the discrete characteristics of robot path planning problems to obtain the optimum mobile robot path. The invention has the advantages of simple process, easy realizing, good robustness, high solving efficiency and the like.

The invention provides a mobile robot path planning method and device. The method includes the following steps: obtaining a current position point of a mobile robot, a target position point and barrier area information; generating a local window according to the current position point; when it is determined that the target position point is not located in the local window, obtaining a local sub-target position point according to the obtained position points and a planning formula; obtaining a local path between the obtained position points according to the obtained position points and a path search formula and recording the local path; and when it is determined that the local sub-target position point is the target position point, displaying the local path after smoothing is performed. The invention proposes a mobile robot path planning method and device, artificial potential field path planning is decomposed into a process of searching for a local target from a plurality of local windows, when the mobile robot is very close to the target, utilization of the path search formula can weaken the action of repulsive force, so that the mobile robot arrives at the target point, and when the mobile robot is away from the target, the mobile robot is not easy to fall into a local minimum point.

The invention discloses a mobile robot path planning method, which comprises the following steps of: A, determining a moving destination of a robot, and setting the number of sensors of the robot and the number of directions towards which the robot can move; B, detecting the environmental information of the surrounding by using the robot; C, defining a mapping relationship between the path planning method and an artificial immune network; and D, resolving the maximum concentration of the artificial immune network and determining an antibody corresponding to the maximum concentration as the moving direction of the robot. The mobile robot path planning method of the invention has the advantage of reaching a destination point under a complex obstacle environment with the local minimization problem, and is feasible and effective under the complex obstacle environment. Simulation results obtained under the U-shaped obstacle environment further show the high efficiency of planning results obtained by the method of the invention under the complex environment.

The invention belongs to the technical field of robot navigation, automatic control and pattern recognition, and discloses an application method of adaptive ant colony algorithm (AACO) in mobile robotpath planning. The method comprises steps of constructing a point-to-point adaptive path selection strategy to select the best path point by the transfer optimization mode in the segmentation combination state; using an obstacle avoidance planning strategy to identify the nature of the obstacle, and selecting different local obstacle avoidance points to avoid obstacles; and applying a mixed deadlock processing strategy in the deadlock environment, and counting deadlock point and its fallback path point distribution information to guide ants to jump out of the deadlock environment. The data inthe embodiment shows that the AACO described in the present invention has better optimization ability than the basic ant colony algorithm (ACO), has better overall performance than the ACO, and can be effectively applied to the global process of robot path planning.