Motion control and navigation of multiple mobile robots for obstacle avoidance and target seeking: A rule-based neuro-fuzzy technique

Abstract

This paper describes a rule-based neuro-fuzzy technique for the navigation of 1000 robots in a cluttered environment. The planning and coordination between the mobile robots is extremely difficult. In the present analysis rule-based and rule-based neuro-fuzzy techniques are used to navigate multiple mobile robots in unknown and partially known environments. The aim of the robots is to reach a predefined goal. Based upon a reference motion, direction, distances between the robots and obstacles, and distances between the robots and targets, different types of rules are defined heuristically and refined later to find the steering angle. The control system combines a repelling influence related to the distance between robots and nearby obstacles and an attracting influence between the robots and targets. Then a hybrid rule-based neuro-fuzzy technique is analysed to find the steering angle of the robots. Simulation and experimental results show that the proposed rule-based neuro-fuzzy technique shows a better navigation performance in complex and unknown environments than the simple rule-based technique.