Neuro-fuzzy systems have been used in past years for robot navigation applications because of their ability to learn human expertise and to utilize this knowledge to develop autonomous navigation strategies. In this paper, neuro-fuzzy based systems are developed for behavior based control of a mobile robot for reactive navigation. The proposed systems transform sensors' input to yield wheel velocities. Novel algorithms are proposed for a) to find the range of the mobile robots from nearby obstacles and b) to generate training pairs for neural network, optimally. With a view to ascertain the efficacy of proposed system; developed neuro-fuzzy system's performance is compared to neural and fuzzy based approaches. Simulation results show effectiveness of proposed system in all kind of obstacle environments.
['Maulin Joshi', 'Mukesh A. Zaveri']
Neuro-fuzzy based autonomous mobile robot navigation system