Bézier curve based dynamic obstacle avoidance and trajectory learning for autonomous mobile robots

Abstract

This paper addresses the problem of avoiding dynamic obstacles while following the learned trajectory through non-point based maps directly through laser data. The geometric representation of free configuration area changes while a moving obstacle enters into the safety region of autonomous mobile robot. We have applied the Bézier curve properties to the free configuration eigenspaces to satisfy the dynamic obstacle avoidance path constraints. The algorithm is designed to accurately represent the mobile robot's characteristics while avoiding obstacle such as minimum turning radius. Moreover, we also discuss the obstacle avoided path feasibility as a vectorial combination of free configuration eigen-vectors at discrete time scan-frames to manifest a trajectory, which once followed and mapped onto the two control signals of mobile robot will enable it to build an efficient and accurate online environment map. Preliminary results in Matlab have been shown to validate the idea, while the same has been implemented in Player/stage (robotics real-time software) to analyze the performance of the proposed system.