Using RRTs to Plan Low-vibration Trajectories for Flexible Mobile Robots

Abstract

Path planning for mobile robots involves finding feasible trajectories through a workspace from an initial state to a final, desired state while avoiding workspace obstacles. Due to the variety of mobile robots and the environments in which they can operate, various path-planning methods have been developed. However, the majority of these planning methods have been designed for rigid systems. When applied to flexible systems, these methods typically produce unwanted vibration, which contributes to trajectory-tracking error. Therefore, trajectory tracking for flexible, mobile systems typically involves sequentially planning a path using algorithms designed for rigid systems, then applying vibration control methods to track the trajectory. This paper proposes a modified Rapidly-exploring Random Tree (RRT) algorithm that plans feasible paths that limit the vibration amplitude induced in flexible systems. The algorithm incrementally generates trajectories that minimize deflection cost and path length. Simulations were performed to compare standard RRT to the proposed algorithm. The proposed algorithm generated shorter trajectories with less deflection than those of standard RRT as well as generated trees which utilized a greater amount of the workspace.