Simulation of mobile robot navigation utilizing reinforcement and unsupervised weightless neural network learning algorithm

The approach of transforming human expert knowledge into computer program only allow a system to solve foreseen and tested outcomes compared to a system having self-learning capabilities. This paper will summarize and discuss the research, design and implementation of a novel self-learning algorithm which combines: (a) Q-Learning - A reinforcement learning algorithm; and (b) AutoWiSARD - An unsupervised weightless neural network learning algorithm. The self-learning algorithm was implemented in an autonomous mobile robot navigation and obstacle avoidance system in a simulated environment. The AutoWiSARD algorithm identifies, differentiates and classifies the obstacles and the Q-learning algorithm learns and tries to maneuver through these obstacles. This novel hybrid technique allows the autonomous system to acquire knowledge, learn and record experience thus attaining self-learning state. The final result shows the simulated mobile robot was able to differentiate various shapes of obstacles such as corners and walls; and create complex control sequences of movements to maneuver through these obstacles.