Complex Narrow Scene Path Planning for Robotic Arm Based on DBS-RRT?Algorithm
Aiming at the problems of long planning time,lengthy path and low success rate of narrow envi-ronment planning in current RRT∗ algorithm in complex narrow scene path planning of robot arm,a dy-namic biased sampling RRT∗(DBS-RRT∗)algorithm is proposed.The DBS-RRT∗algorithm adopts a dy-namic bias rate,designs intelligent ellipsoid subset sampling as a biased sampling method,and utilizes an a-daptive growth strategy to adjust the growth direction and step size of new nodes to achieve the effect of dynamically selecting the sampling method,improving the sampling efficiency,reducing the ineffective spa-tial exploration,and improving the search orientation.Then,the effectiveness of the algorithm is verified by designing two-dimensional experiments,which prove that the DBS-RRT∗algorithm has higher planning ef-ficiency and shorter planning paths compared with the RRT∗algorithm;finally,the DBS-RRT∗algorithm is applied to a robotic arm simulation experiment in a complex narrow scene.The experimental data show that the DBS-RRT∗algorithm reduces the planning path length by 26%,reduces the planning time by 22.6%,and improves the success rate by 32%compared with the RRT∗algorithm.The DBS-RRT∗algorithm is a-ble to realize the obstacle-avoidance path planning of the robotic arm more efficiently compared with the RRT∗algorithm in the complex and narrow scene.
DBS-RRT∗ algorithmdynamic biasing ratiorobotic armspath planningcomplex narrow scene
NETL
NSTL
万方数据
