Reinforcement Extreme Learning Machine for Mobile Robot Navigation

Obstacle avoidance is a very important problem for autonomous navigation of mobile robot. However, most of existing work regards the obstacle detection and control as separate problem. In this paper, we solve the joint learning problem of perception and control using the reinforcement learning framework. To address this problem, we propose an effective Reinforcement Extreme Learning Machine architecture, while maintaining ELM's advantages of training efficiency. In this structure, the Extreme Learning Machine (ELM) is used as supervised laserscan classier for specified action. And then, the reward function we designed will give a reward to mobile robot according to the results of navigation. The Reinforcement Extreme Learning Machine is then conducted for updating the expected output weights for the final decision.