Research on robotic arm path planning method based on two-stage RRT* optimization algorithm

Abstract

Abstract Aiming at the problems such as high computation cost and slow convergence speed in dynamic path planning of robotic arm, this paper proposes a two-stage RRT* (rapidly-exploring random tree*) optimization algorithm. In the exploration phase, in order to reduce the randomness of RRT* path exploration, a heuristic sampling strategy with priority queue is used to reduce 62.9% of invalid path exploration; in the optimization phase, in order to improve the convergence speed of the paths, the improved algorithm’s convergence speed is improved by 2.5 times compared to RRT* through the immediate propagation of the cost update strategy. Experiments show that the improved RRT* path cost undergoes a decrease in 17.24% and the running time is shortened to 30.09% of RRT* in the dynamic obstacle avoidance scenario of a 6-degree-of-freedom robotic arm by the above two-stage optimization framework. The algorithm provides a reference value for the path planning of the robotic arm.