17results about How to "Reduce planning time" patented technology

The invention relates to a robot intelligent sorting path planning method and a robot intelligent sorting path planning device for stacked parts in a charging basket, an RRT * algorithm is combined with an artificial potential field algorithm, random search of the RRT * algorithm in a barrier-free free space can be reduced by utilizing an artificial potential field, and meanwhile, in a complex barrier environment close to a part pile, the robot intelligent sorting path planning method and the robot intelligent sorting path planning device have the advantages that the random search of the RRT * algorithm in the barrier-free free space can be reduced; and an RRT * algorithm is used for accurate collision-free path planning, so that the problems of low path planning precision and local minimum of an artificial potential field algorithm are avoided. On the basis, the RRT * algorithm is improved, the problems of many redundant nodes, low planning efficiency and poor path quality existing in path planning of the RRT * algorithm are solved, meanwhile, the improved RRT * algorithm is combined with the artificial potential field algorithm, the planning scale is further reduced, and random search of the RRT * algorithm in a free space is reduced. According to the method and device provided by the invention, an efficient and accurate collision-free picking path planning scheme can be provided for industrial application of mechanical arm material frame sorting.

The invention discloses an unmanned aerial vehicle flight path planning method which comprises the following steps: (1) modeling a flight area by using a grid method, and initializing basic parameters; (2) planning a flight path of the unmanned aerial vehicle based on an improved double-layer ant colony algorithm; (3) outputting an optimal planning path; according to the method, the flight path planning efficiency of the unmanned aerial vehicle is improved, the planning time is shortened, the planned path is smoother, and the result is better.

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

The invention relates to a cruise low-altitude penetration task route planning method based on an improved A * algorithm, and belongs to the field of route planning of unmanned aerial vehicles. The method comprises the steps of firstly planning and generating a track result for avoiding enemy ground radar threats in a battlefield environment through an improved A * algorithm; secondly, on the basis of the two-dimensional flight path planning result, height planning meeting the performance constraint of the fixed-wing unmanned aerial vehicle is achieved through a particle sedimentation method; and finally obtaining a three-dimensional air route of the fixed-wing unmanned aerial vehicle for completing the established combat task. According to the method, off-line and on-line planning of the reference flight path in a combat mission scene of the fixed-wing unmanned aerial vehicle is realized, the planning time before launching is shortened, the airborne flight path calculation efficiency is improved, the reaction time is shortened, and thus the adaptive capacity of the fixed-wing unmanned aerial vehicle to a complex battlefield environment is improved.

The invention relates to the technical field of power grid planning, in particular to a rule engine driven power distribution network planning problem library construction method. The method comprises the following steps: acquiring environment information data of the power distribution network; performing data preprocessing on the power distribution network environment information data to generate standard power distribution network environment information data; performing substation site selection on the power distribution network environment information data to obtain power distribution network substation site selection data; carrying out transformer substation capacity distribution on the power distribution network transformer substation site selection data to generate transformer substation capacity distribution data; performing transformer substation layout according to the transformer substation capacity distribution data and the power distribution network transformer substation site selection data so as to generate transformer substation layout data; and carrying out power transmission line path selection on the standard power distribution network environment information data based on the substation layout data to generate power transmission line route data. Through systematic data integration, flexible rule extraction, dynamic decision monitoring and knowledge management, the accuracy and adaptability of power distribution network planning are improved.

The invention relates to a constraint processing planning method and system based on an artificial potential field for visual servo of a moving operation arm, and the method comprises the following steps: obtaining initial parameters, and carrying out the system modeling; on the basis of system modeling, global path planning based on sampling is carried out, and an initial feasible path is obtained; verifying the initial feasible path based on a local planner of the APF and a preset multi-constraint requirement to obtain a discrete feature path; performing trajectory smoothing and time parameterization on the discrete feature path to obtain a final image feature trajectory; and on the basis of the final image feature track, carrying out online image servo execution and tracking on the mobile operation arm. The robot can be widely applied to the technical field of robots.

The invention relates to a redundant snakelike mechanical arm path planning method based on an AC-RRT (Angle Constraint Rapidly-Exploring Random Tree) algorithm, and provides an AC-RRT (Angle Constraint Rapidly-Exploring Random Tree) algorithm. A step length and target offset probability determination method is provided according to the obstacle distribution relation in the space, and the adaptive capacity of the algorithm is improved; a random tree extension angle constraint model is provided according to the mechanical structure of the snakelike mechanical arm, so that a planned path better conforms to the spatial motion characteristics of the mechanical arm; and finally, a target point greedy query method is introduced, the path search time is shortened, and redundant nodes are reduced. Matlab simulation is carried out, performance differences between RRT and RRT-connect algorithms are compared, V-REP software is combined with a mechanical arm model to carry out analog simulation on the algorithms, and the feasibility of the algorithms is verified.

The present application relates to the field of intelligent parking, and provides an automatic parking trajectory planning method, electronic device and vehicle, the method comprising: determining the parking inflection point interval and the trajectory end point based on the acquired parking starting point; constructing multiple initial splicing trajectories based on the trajectory end point and at least one parking inflection point in the parking inflection point interval, and in response to determining that there is an initial splicing trajectory that passes the test verification, determining the initial splicing trajectory that passes the test verification as the target splicing trajectory; controlling the vehicle to park based on the target splicing trajectory; wherein the initial splicing trajectory is formed by smooth splicing of at least two circular arcs. The present application uses a pure geometric algorithm to design a splicing trajectory, and does not require smoothing, which reduces the amount of calculation, saves parking trajectory planning time, and makes the parking process smooth, improving the user experience.

The present invention discloses a method and system for constructing a lifting path planning model, and relates to the technical field of lifting path planning. The method comprises: collecting historical cargo loading and unloading task data and historical status information of obstacles; wherein the historical cargo loading and unloading task data includes the starting point data and the landing point data of the cargo; obtaining the first operation data of the first crane on four degrees of freedom during the execution of the historical cargo loading and unloading task; wherein the four degrees of freedom include lifting, amplitude change, rotation and telescopic; obtaining the second operation data of the second crane on four degrees of freedom during the execution of the historical cargo loading and unloading task; constructing a lifting path planning model according to the starting point data, the landing point data, the first operation data and the second operation data; the lifting path planning model is constructed based on a deep reinforcement learning network. The present application realizes the accuracy of cargo loading path planning.

The application provides a kind of mechanical arm motion planning method and system based on deep neural network.The method includes processing obstacle space point cloud data collected by depth camera to obtain processed point cloud data;Convert the processed point cloud data into octomap chart and import into moveit mechanical arm workspace;In moveit mechanical arm workspace, the complete collision-free path from the initial configuration state to the target configuration state is output by the mechanical arm motion planning algorithm based on deep neural network;Optimize the complete collision-free path to generate the optimal complete collision-free path;Control the mechanical arm to move according to the generated optimal complete collision-free path.The application proposes that the deep neural network predicts the configuration state of the mechanical arm at a certain time, reduces the calculation amount in the process of mechanical arm obstacle avoidance planning through collision detection and bidirectional iterative exploration, greatly speeds up the path planning speed, shortens the path planning time, and at the same time ensures that the planned path is close to the optimal path.

The present invention discloses a path planning method, device and computer storage medium for a mobile robot. The path planning method for a mobile robot of the present invention adopts a two-way sampling strategy to generate four search trees, and uses the four search trees to search simultaneously, thereby shortening the path planning time and improving the path planning efficiency; when the search tree expands the node, the repulsive field generated by the obstacle is used to constrain the generation of the sampling point, so that the generation of the sampling point has directionality, so that the sampling point is far away from the obstacle, and the obstacle is expanded at the same time, so as to avoid the generated path from being close to the obstacle; in addition, the gradient descent optimization strategy is used to smoothly optimize the generated path, so that the path is more in line with the dynamic model of the mobile robot, thereby making the robot move more smoothly.

This invention provides a path planning method belonging to the field of robot path planning, comprising: constructing a non-uniform dynamic partitioning strategy based on the obstacle distribution density within the workspace, prioritizing the selection of key areas that the path may traverse, and reducing the generation of redundant nodes and edges; constructing a weighted sampling strategy based on the comprehensive distance between sampling points and obstacles, increasing the sampling density of obstacle boundaries and narrow passage areas, and reducing invalid sampling in free space; constructing an adaptive variable step-size connection strategy based on the regional obstacle distribution characteristics, dynamically adjusting the node connection step size to enhance the connectivity of the route graph and reduce redundant edges; and performing path querying using a graph search algorithm based on the constructed probabilistic route graph, and performing cubic spline interpolation smoothing on the generated original path to obtain a smooth final path. This method is applicable to path planning in two-dimensional and three-dimensional spaces and for multi-degree-of-freedom robotic arms, and can effectively shorten path length and reduce planning time.

The invention provides a path planning method based on spatial transformation, and belongs to the technical field of unmanned vehicle path planning. Comprising the following steps: pre-processing a real map, and converting the real map with obstacles into a barrier-free space through spatial transformation; establishing and storing a dynamic mapping relation between the barrier-free space and a route area of the aircraft; acquiring a starting point and an ending point in the barrier-free space, and acquiring path discrete points between the starting point and the ending point; and converting the path discrete points into a real map to obtain path planning points in a real map coordinate system. The path planning method based on spatial transformation provided by the invention has the characteristics of few iteration and search processes, less calculation resource occupation and high calculation efficiency.

The invention discloses a ship search and rescue path planning method, device and equipment and a storage medium. Comprising the steps that a multi-ship search and rescue mathematical model is constructed, and a ship set, a waypoint set and an unpassed waypoint set are defined in the multi-ship search and rescue mathematical model; constructing a state parameter model graph covering the search and rescue area based on the multi-ship search and rescue mathematical model, and constructing a search model graph according to the state parameter model graph; and carrying out search and rescue path planning according to the state parameter modeling graph and the search model graph. By constructing the mathematical model and the two model diagrams, the complex search and rescue path planning process is simplified, the planning efficiency is remarkably improved, and the planning time is shortened. According to the method, multiple factors such as a ship set and a waypoint set are comprehensively considered, so that the planned search and rescue path is more reasonable, the coverage integrity and accuracy of a search area are improved, search and rescue tasks and paths of multiple ships can be reasonably allocated, optimal configuration of search and rescue resources is realized, and the resource utilization efficiency is improved.

The present application relates to the field of parking, and provides an automatic parking trajectory planning method, electronic device, and vehicle, the method comprising: determining a parking inflection point interval based on an acquired parking starting point, wherein the parking inflection point interval includes multiple parking inflection points; determining a target spliced ​​trajectory formed by splicing a first parking arc trajectory and a second parking arc trajectory based on at least one of the parking inflection points; and controlling the vehicle to park based on the target spliced ​​trajectory. The present application uses a pure geometric algorithm to design an elliptical arc parking trajectory, without the need for smoothing, which reduces the amount of calculation, saves parking trajectory planning time, and makes the parking process smooth, thereby improving the user experience.

The invention provides an off-line sign-in code creating method and platform based on intelligentization, relates to a data processing technology, and aims to identify off-line edge equipment, process data and store corresponding data when a terminal is in an off-line state, so that the data can be synchronized to a server when the terminal is in a wired state, and the user experience is improved. According to the invention, smooth completion of the exhibition and effective maintenance of the exhibition order can be ensured in response to sudden network problems, and offline payment and offline functional customization can be carried out according to offline requirements when payment requirements are met, so that the method is suitable for more application scenes and has high stability, and in conclusion, the method is worthy of popularization and application. According to the invention, the equipment can realize personnel sign-in and improve the sign-in efficiency under any condition that the equipment is off-line due to accidental network disconnection, and can realize payment items according to requirements and reduce economic loss caused by the off-line of the equipment.

The present application relates to the field of intelligent parking, and provides an automatic parking trajectory planning method, electronic device, and vehicle, the method comprising: determining the trajectory end point based on the target parking space, and constructing multiple first parking arc trajectories based on the trajectory end point; in response to determining that there is no first parking arc trajectory tangent to the vehicle centerline, determining a second parking arc trajectory that has a tangent point with the first parking arc trajectory based on the parking starting point and posture data; and outputting the spliced ​​trajectory of the first parking arc trajectory and the second parking arc trajectory as the target trajectory. The present application uses a pure geometric algorithm to design a spliced ​​trajectory, without the need for smoothing, which reduces the amount of calculation, saves parking trajectory planning time, and makes the parking process smooth, improving the user experience.