Learning Wall Following Behaviour in Robotics through Reinforcement and Image-based States

In this work, a visual and reactive wall following behaviour is learned by reinforcement. With artificial vision the environment is perceived in 3D, and it is possible to avoid obstacles that are invisible to other sensors that are more common in mobile robotics. Reinforcement learning reduces the need for intervention in behaviour design, and simplifies its adjustment to the environment, the robot and the task. In order to facilitate its generalization to other behaviours and to reduce the role of the designer, we propose a regular image-based codification of states. Even though this is much more difficult, our implementation converges and is robust. Results are presented with a Pioneer 2 AT. Learning phase has been realized on the Gazebo 3D simulator and the test phase has been proved in simulated and real environments to demonstrate the correct design and robustness of our algorithms.