A combined reactive and reinforcement learning controller for an autonomous tracked vehicle

Unmanned ground vehicles currently exhibit simple autonomous behaviours. This paper presents a control algorithmdeveloped for a tracked vehicle to autonomously climbobstacles by varying its front and back track orientations. A reactive controller computes a desired geometric configuration based on terrain information. A reinforcement learning algorithm enhances vehicle mobility by finding effective exit strategies in deadlock situations. It is capable of incorporating complex information including terrain and vehicle dynamics through learned experiences. Experiments illustrate the effectiveness of the proposed approach for climbing various obstacles. Crown Copyright© 2011 Published by Elsevier B.V. All rights reserved.