狭窄空间下基于高维构型快速碰撞检测的机械臂路径规划OA

Aiming at the problems of low efficiency and success rate,high percentage of time-consuming collision detection in robotic arm path planning within narrow spaces,this paper proposes a path planning algorithm for nar-row spaces utilizing online fast collision detection in high-dimensional configuration space.The algorithm takes RRT-Connect as the baseline path search framework,and runs the fast collision detection module based on high-dimen-sional configurations online clustering in parallel.The module includes two stages:Balanced sampling of the high-di-mensional configuration dataset and online training of the fast collision detection model.Specifically,in the phase of dataset online construction,a heuristic strategy is introduced to fully explore the free configurations in narrow space,overcoming the problem that uniform sampling leads to an uneven number of collision and free configura-tions in the dataset,and to provide reliable data support for the subsequent fast collision detection model training;After the dataset construction,the distribution of the two types of configurations in the high-dimensional space is characterized in the form of clusters by online clustering of collision and free configurations;Using the trained cluster model,the collision detection based on bounding box in the baseline algorithm can be transformed into the calculation of the distance between the sampled configuration and the cluster,which greatly reduces the time con-suming for a single collision detection and effectively improves the overall search efficiency of proposed algorithm.Through simulation tests and experimental verification in simple,opened and closed three types of narrow environ-ments,the results show that the proposed algorithm has significant advantages in terms of path searching effi-ciency and success rate.