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 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.

► A simple algorithm is developed to extract terrain information from laser range data.
► A reactive controller is designed for shape-shifting tracked robots.
► Reinforcement learning is used to improve the mobility of the robot.
► Validations are conducted using the Vortex simulator.