Using an evolutionary fuzzy neural network for sensor-based wall-following control of a mobile robot

Abstract

We propose an efficient evolutionary fuzzy neural network (EFNN) for mobile robot control. The proposed EFNN combines a fuzzy neural network (FNN) and an improved artificial bee colony (IABC) algorithm to implement the wall-following control of a mobile robot. To evaluate the wall-following control performance of the FNN, an efficient fitness function is defined. The three control factors (CFs) in the fitness function are the maintenance of the robot–wall distance, the avoidance of robot–wall collision, and the successful movement of the robot along a wall to travel around a stadium. The traditional ABC emulates the intelligent foraging behavior of honey bee swarms, but this algorithm performs favorably at exploration and poorly at exploitation. Therefore, the proposed IABC algorithm uses mutation strategies to balance exploration and exploitation. Furthermore, a new reward-based roulette wheel selection (RRWS) mechanism is adopted to obtain a more favorable solution during the learning process. Experimental results demonstrate that the proposed IABC obtains a smaller root mean square error (RMSE) than other methods in wall-following control.