Wall-following control of a mobile robot with fuzzy Q-learning

Abstract

The Q-learning was introduced into navigation control of the wall-following task of mobile robots where there was no enough priori knowledge available.The Q-value function was approached directly u-sing Fuzzy Neural Network(FNN).The optimization method was used to search the greedy action with maximum Q-value.The nodes of FNN were created incrementally and adaptively according to every ele-ment of the current pair of state-action and Temporal Difference(TD),which overcame the difficulties of the choice of nodes and ensured an economic size of the network.Moreover the parameters of the FNN were updated using Extended Kalman Filter(EKF).The results of the wall-following task of Khepera 2 mobile robot demonstrate the superiority and validity of the proposed method.