An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning

In this paper, an alternative training approach to the EEM-based training method is presented and a fuzzy reactive navigation architecture is described. The new training method is 270 times faster in learning speed; and is only 4% of the learning cost of the EEM method. It also has very reliable convergence of learning; very high number of learned rules (98.8%); and high adaptability. Using the rule base learned from the new method, the proposed fuzzy reactive navigator fuses the obstacle avoidance behaviour and goal seeking behaviour to determine its control actions, where adaptability is achieved with the aid of an environment evaluator. A comparison of this navigator using the rule bases obtained from the new training method and the EEM method, shows that the new navigator guarantees a solution and its solution is more acceptable.