Kinodynamic comfort trajectory planning for car-like robots

Abstract

Personal autonomous mobility is getting to be more widely adopted, it is more important to consider comforta- bility of stuffs and persons carried by such mobility. In this work, we define the comfortability of a trajectory as forces, specifically, translational acceleration, received to objects carried by a robot while following the trajectory. To maximize such a comfortability, we propose a novel, kinodynamic comfort path planning method based on our definition of comfortability. Our work is based on the direct collocation method for handling our non-convex objective function. We also introduce bidirectional obstacle detection that identifies the distances along the perpendicular directions to the trajectory. This is mainly designed for avoiding obstacles while minimizing forces causing discomfort. Our experimental results show that our method can compute trajectories whose comfort measures can be up to 18 times higher than those computed by prior related objectives, e.g., squared velocity used for generating smooth trajectory.