An incremental machine learning mechanism applied to robot navigation

We apply an incremental machine learning algorithm to the problem of robot navigation. The learning algorithm is applied to a simple robot simulation to automatically induce a list of declarative rules. The rules are pruned in order to remove the rules that are operationally useless. The final set is initially used to control the robot navigating an obstacle-free path planned in a polygonal environment with satisfactory results. Crisp conditions used in the rules are then replaced by fuzzy conditions fashioned by a human expert. The new set of rules are shown to produce better results