Zhang, Jian (University of New South Wales)

Path Planning for a Mobile Robot in Unknown Dynamic Environments Using Integrated Environment Representation and Reinforcement Learning

Scheduled for presentation during the Regular Session "Learning, Fuzzy and Neural Systems" (FC1), Friday, November 29, 2019, 15:45−17:45, WZ Building Room WZ416

2019 Australian & New Zealand Control Conference (ANZCC), November 27-29, 2019, Auckland, New Zealand

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

This study develops a new path planning method which utilizes integrated environment representation and reinforcement learning to control a mobile robot with non-holonomic constraints in unknown dynamic environments. With the control algorithm presented, no approximating the shapes of the obstacles or even any information about the obstacles' velocities is needed. Our novel approach enables to find the optimal path to the target efficiently and avoid collisions in a cluttered environment with steady and moving obstacles. We carry out extensive computer simulations to show the outstanding performance of our approach.