1524 results about "Path plan" patented technology

The invention discloses a double-layer planning method based on the combination of global path planning and local rolling prediction collision avoidance planning, so as to solve the problem of path planning for a mobile robot in a dynamic environment. The method mainly comprises two parts: the global path planning and the local rolling prediction collision avoidance planning. The path planning method can better realize robot navigation, and improve intelligence of the robot. The double planning method can be utilized to prevent the blindness of planning in the beginning, and searching space of the problem is reduced; based on the uncertainty of the moving direction of a dynamic barrier and by utilizing the two collision prediction strategies and two corresponding collision avoidance strategies, the dynamic barrier can be avoided well; and particularly, in order to adapt to the change of environment better, in the second layer of planning, a Follow_wall behavior based on behavior method is added, so that when the environment changes, the mobile robot can still arrive at the target without touching the barriers safely.

A suitable waypoint is selected using a goal score, a section from a start point to a goal point through the waypoint is divided into a plurality of sections based on the waypoint with a solution of inverse kinematics, and trees are simultaneously expanded in the sections using a Best First Search & Rapidly Random Tree (BF-RRT) algorithm so as to generate a path. By this configuration, a probability of local minima occurring is decreased compared with the case where the waypoint is randomly selected. In addition, since the trees are simultaneously expanded in the sections each having the waypoint with a solution of inverse kinematics, the solution may be rapidly obtained. A time consumed to search for an optimal motion path may be shortened and path plan performance may be improved.

The invention discloses a transformer station inspection tour robot positioning navigation system and a method. Confidence obtained by a laser range finding module and a dead reckoning module is outputted to a map creation module and a pose resolving module; the map creation module creates a laser road sign map and a laser grid map which show a robot operation environment and are inputted into a map storage module for storage; the posture resolving module utilizes map data in the map storage module and output data of the dead reckoning module to perform real-time calculation on the current position and the posture of the robot and accesses the obtained posture data into a path planning module and a motion control module; the path planning module generates robot operation path data which is outputted to the motion control module; the motion control module generates motion control quantity according to the current position and posture data of the robot and the path planning data, and then is driven to execute by a motor. The transformer station inspection tour robot positioning navigation system realizes the accuracy positioning of the inspection tour robot in the transformer station.

A perimeter training module establishes a perimeter path plan of a vehicle including a defined perimeter. A region-filling module establishes a region-filling path plan of the vehicle within the defined perimeter. A point-to-point planning module establishes a point-to-point path plan of a vehicle including a segment of at least one of the region-filling path plan and the perimeter path plan. The path planner forms a preferential composite path plan based on the established perimeter path plan, the region-filling plan, and the point-to-point path plan.

The invention discloses an autonomous location and navigation method and an autonomous location and navigation system of a robot. The method comprises the following steps: acquiring the RGB image of the robot in the current position; carrying out characteristic point matching on the RGB image of the current position and an indoor three-dimensional point cloud map, and determining the current position of the robot in the indoor 3D point cloud map; carrying out characteristic point matching on a pre-stored RGB image of a destination and the indoor three-dimensional point cloud map, and determining the position of the destination in the indoor 3D point cloud map; and searching an optimal path from the current position to the destination position in the indoor 3D point cloud map, and driving the robot to run according to the optimal path. The method and the system have the advantages of completion of autonomous location and navigation through using a visual sensor, simple device structure, low cost, simplicity in operation, and high path planning real-time property, and can be used in the fields of unmanned driving and indoor location and navigation.

A system and method for providing path planning and generation for automated lane centering and/or lane changing purposes for a vehicle traveling on a roadway, where the method employs roadway measurement values from a vision camera within an effective range of the camera and roadway measurement values from a map database beyond the range of the camera. The method uses the roadway measurement values from the camera to determine a desired path along a first segment of the roadway and identifies an end of the first segment based on how accurately the camera defines the roadway. The method then uses the roadway measurement values from the map database to determine the desired path along a second segment of the roadway that begins at the end of the first segment, where a transition from the first segment to the second segment is smooth.

The present invention relates to a controller for providing a vehicle with autonomous control. The controller preferably provides path planning to an autonomous vehicle.

A joint search method for UAV multiobjective path planning in an urban low altitude environment first constructs a static safety index map based on static known obstacles. Meanwhile, based on proactively detected obstacles that are not marked on a geographic map by a UAV, the method constructs online a dynamic safety index map. Second, a multiobjective path planning problem is solved using a joint offline and online search method. Moreover, this method first plans offline the least cost path from a starting point to an end point and then invokes the online search scheme to replan online a changed path when the UAV detects unknown obstacles. Thus, the UAV can avoid dynamic obstacles effectively. The online search scheme has a small search space and can quickly replan a safe path for the UAV, thus satisfying the requirement of UAV on the real-time path planning.

The invention discloses a real-time guidance method for bus taking navigation and belongs to the technical field of intelligent traffic. The method comprises the following steps: determining an origin, a destination and departing time; carrying out path planning according to the coordinates of the origin and the destination, and obtaining the time cost of each road section in the path planning according to the departing time and a path planning result in combination with dynamic traffic data; correcting the time cost of each road section according to the actual departing time and the current position of a user and utilizing characters, voice and/or graphical representations to carry out real-time guidance at each road section. On the basis of providing the character and map demonstration of the path planning result, the real-time guidance method can further provide the progress bar of journey stages and expected time for arriving at each stage point, and can also provide a whole-journey real-time guidance function. The real-time guidance method has the advantages of simple and visual journey demonstration, timely and accurate prompt, and safety and convenience in use. The use convenience of passengers in navigation is greatly improved, and the occurrence of accidents is reduced when navigation equipment is used during motion.

The invention discloses a reinforcing learning initial method of a mobile robot based on an artificial potential field and relates to a path planning Q-learning initial method of the mobile robot. The working environment of the robot is virtualized to an artificial potential field. The potential values of all the states are confirmed by utilizing priori knowledge, so that the potential value of an obstacle area is zero, and a target point has the biggest potential value of the whole field; and at the moment, the potential value of each state of the artificial potential field stands for the biggest cumulative return obtained by following the best strategy of the corresponding state. Then a Q initial value is defined to the sum of the instant return of the current state and the maximum equivalent cumulative return of the following state. Known environmental information is mapped to a Q function initial value by the artificial potential field so as to integrate the priori knowledge into a learning system of the robot, so that the learning ability of the robot is improved in the reinforcing learning initial stage. Compared with the traditional Q-learning algorithm, the reinforcing learning initial method can efficiently improve the learning efficiency in the initial stage and speed up the algorithm convergence speed, and the algorithm convergence process is more stable.

The process of programming local path of mobile robot based on binary environmental information includes the following steps: 1. calculating the coordinates of the target point in the local path programming coordinate system; 2. calculating the default path without barrier; 3. initializing the intermediate variables; 4. obtaining binary environmental information description based on the output of the sensor and classifying the barriers; 5. selecting current optimal arc section; 6. calculating the relative position relation between carriers and the current optimal arc section, and re-classifying the barriers; 7. renewing the right and left barrier bypassing arc sections, and backing to the step 4 for the next loop; and 8. outputting the local path programming results. The present invention is simple and high in efficiency, and can meet the real-time programming requirement for continuous walking of mobile robot.

Embodiments of the present invention pertain to methods and systems for planning the path of an agricultural vehicle. In one embodiment, a first point of a first planned path and a second point of a second planned path are determined. A path plan is then automatically generated connecting the first point and the second point.

A method and system that facilitates operation of autonomous equipment by providing a mission planner to maintain line-of-sight contact between a plurality of coordinated machines, including a method for maintaining line-of-sight (LoS) communication between a plurality of machines that creates a mission plan for a work site that includes a path plan for each of the plurality of machines that maintains the line-of-sight communication between the plurality of machines by taking into account a topography for the work site; and loads the path plan for each respective one of the plurality of machines into each respective one of the plurality of machines, wherein the path plan specifies a machine travel path for each respective one of the plurality of machines.

The different illustrative embodiments provide a method for generating an area coverage path plan using sector decomposition. A starting point is identified on a worksite map having a number of landmarks. A first landmark in the number of landmarks is identified. A path is generated around the first landmark until an obstacle is detected. In response to detecting the obstacle, the path is made linear to a next landmark. The path is generated around the next landmark.

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 the technology of UAV (unmanned aerial vehicle) control, aims at proposing a UAV locus and attitude cooperative control method and ironing out a defect in the locus planning for each UAV of a conventional UAV cluster, and can effectively reduce the requirements for the positioning precision of the UAV. Therefore, the invention provides a cluster UAV locus and attitude cooperative control method for a safety domain, and the method comprises the following steps: 1, control-oriented four-rotor-wing UAV locus and attitude model building: giving full consideration to the inherent characteristics of a four-rotor-wing UAV and the dynamic factors in flight, and building a locus and attitude mathematic model for the four-rotor-wing UAV; 2, cluster center locus planning: achieving the obstacle avoidance path planning for a cluster UAV central point based on a pseudo-spectral method; 3, safety flight region optimization: completing the optimization of a cluster UAV safetyflight envelope and a cluster UAV optimal formation configuration; 4, distributed cooperative controller design. The method is mainly used in a UAV control occasion.

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.

This invention concerns the control of rotating excavation machinery, for instance to avoid collisions with obstacles. In a first aspect the invention is a control system for autonomous path planning in excavation machinery, comprising: A map generation subsystem to receive data from an array of disparate and complementary sensors to generate a 3-Dimensional digital terrain and obstacle map referenced to a coordinate frame related to the machine's geometry, during normal operation of the machine. An obstacle detection subsystem to find and identify obstacles in the digital terrain and obstacle map, and then to refine the map by identifying exclusion zones that are within reach of the machine during operation. A collision detection subsystem that uses knowledge of the machine's position and movements, as well as the digital terrain and obstacle map, to identify and predict possible collisions with itself or other obstacles, and then uses a forward motion planner to predict collisions in a planned path. And, a path planning subsystem that uses information from the other subsystems to vary planned paths to avoid obstacles and collisions. In other aspects the invention is excavation machinery including the control system; a method for control of excavation machinery; and firmware and software versions of the control system.

The invention discloses a shortest path planning method for dynamic origins, and belongs to the technical field of dynamic path guidance in an intelligent traffic system. The shortest path planning method is used for optimizing vehicle traveling routes. The shortest path planning method always takes a current vehicle position as a starting point and predicts road condition information such as traffic flow of relevant road sections and road intersections, and the like for many times according to vehicle running time to calculate a shortest path according to a path planning algorithm in a no-request mode at regular time, thereby acquiring a shortest time path in accordance with the real-time conditions of a road network better and directly displaying the shortest time path on the screen of a vehicle navigation system; and meanwhile, by the method, the update operation amount is less so as to shorten calculation time.

The invention discloses a robot path planning method and system based on an improved artificial potential field method, and the method comprises the steps: firstly finding local target points in a visual scope of a laser radar; secondly planning a reachable path from the current position of a robot to the local target points; and finally controlling and driving the robot to move to circularly detect the local target points till the robot reaches a final target point. The method employs the artificial potential field method to plan the path of the robot, solves a problem of a local minimal point appearing in path planning through a conventional artificial potential field method, and improves the conventional artificial potential field method, i.e., improving a gravitational potential function. Meanwhile, the method enables the whole task to be divided into a plurality of local target points, thereby achieving the optimal path. The method improves the instantaneity of path planning, environmental suitability and efficiency.

The invention belongs to the field of intelligent unmanned intelligent ships, and relates to an unmanned ship water surface target detection, identification and positioning method based on monocular camera and lidar information fusion. The detection, identification and positioning of a water surface target by an unmanned ship are influenced by distances and the fluctuation of the target, so that alidar and a monocular camera are integrated to accurately detect, identify and position the target within a sensing range. According to the method, acquired water surface target images are adopted totrain a neural network-based target detection and recognition model; the lidar employs a conditional removal filter and Euclidean clustering to obtain the position of the water surface target in a world coordinate system; and finally, a camera image information and lidar information fusion method is designed, and therefore, the method is highly robust to uncertain factors. With the method adopted, the unmanned ship is capable of accurately detecting, identifying and positioning the water surface target; and good environment perception can be realized for the target tracking, path planning andautonomous navigation of the unmanned ship. The method has a broad application prospect.

Control handover planning for an unmanned aerial vehicle (UAV) is provided that includes determining, by a planning system, a current location of a mobile control system and a current location of the UAV. A target location is identified. A control handover zone is determined based on a communication range constraint between the mobile control system and the UAV. The control handover zone is located between the current location of the mobile control system, the current location of the UAV, and the target location. A mobile control system path plan and a UAV path plan are created that each includes a control handover waypoint in the control handover zone at the same time. The control handover waypoint defines a planned location to place the mobile control system in control of the UAV.