Robotic arm path planning under static obstacles based on improved Informed RRT* algorithm

Abstract

A well-planned trajectory is crucial for the smooth operation of a robot arm. Informed RRT* is a commonly used path-planning algorithm with asymptotic optimality, which is an improvement of the Rapidly-exploring Random Tree algorithm. Plain Informed RRT* has the problems of long initial path searching time and unsmooth trajectory, which make it difficult to directly apply to robotic arms. This paper proposes an improved Informed RRT* algorithm in which a second-step path refinement is introduced to reduce the distance cost of the final path. The commonly used trajectory optimization method is minimum-snap, but the optimized smooth trajectory still has the probability of collision with obstacles. To reduce the probability of collision between smooth trajectory and obstacles, the Safe Flight Corridor is introduced to limit the optimization range of the trajectory. The experiments of the plain Informed-RRT* and the improved algorithm in the simulation environment verify that the improved algorithm can reduce the distance cost of the final path. The simulation results also verify that the number of collisions between the smoothed trajectory and the obstacles is significantly reduced after adding the Safe Flight Corridor, which indicates that the improved algorithm is effective.