Robot Motion Planning with Neuro-Genetic-Fuzzy Approach in Dynamic Environment

To find an optimal path for robots in an environment that is only partially known and continuously changing is a difficult problem. This paper presents a new method for generating a collision-free near-optimal path for an autonomous mobile robot in a dynamic environment containing moving and static obstacles using neural network and fuzzy logic with genetic algorithm. The mobile robot selects a collision-free local path using the neural network (ANN). A supervised learning-rule is used for the neural network using the gradient descent method. The fuzzy logic system with genetic algorithm comes into play when finding a local path by neural network becomes impossible. Fuzzy logic (FL) is used to avoid collisions when all the paths are blocked by obstacles. Genetic Algorithm (GA) is used as optimizer to find optimal locations along the obstacle-free directions and positions by selecting a set of fuzzy rules for the fuzzy logic system from a large rule base. Experimental results show that the method is efficient and gives near-optimal path reaching the target position of the mobile robot. Keyword: Mobile robots, dynamic environment, motion planning, artificial neural network, fuzzy logic, genetic algorithm, obstacle avoidance, collision-free path, supervised learning, static obstacles. Robot Motion Planning with Neuro-Genetic-Fuzzy Approach in Dynamic Environment