2results about How to "Reduce planning time" patented technology

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.

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.