科研之友
微信
新浪微博
Facebook
分享链接
摘要
Unmanned ground vehicles currently exhibit simple autonomous behaviours. This paper presents a control algorithm developed for a tracked vehicle to autonomously climb obstacles 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.
- 出版日期2012-4
