Heuristic-rule-based hybrid neural network for navigation of a mobile robot

Abstract

The current paper presents a novel technique for a mobile robot to navigate in a real-world dynamic environment. When an autonomous mobile robot navigates in an unknown environment it is required to plan a path based on the information gathered from sensors in order to avoid obstacles and reach a target. This research idea is related to the basis of human perception, by using heuristic information for the navigation of mobile robots in cluttered dynamic environments which provides a general, robust, safe, and optimized path. The heuristic-rule-based network (HRBN) consists of a simple algorithm which makes the predefined estimation function much smaller. The estimation function should be adequately defined for desired movement in the environment. A navigation system using the rule-based technique allows a mobile robot to travel in an environment about which the robot has no prior knowledge. This heuristic rule is applied in conjunction with an artificial neural network (ANN). The ANN is trained by back-propagation algorithms. The HRBN provides an optimum trajectory which increases the effectiveness of a mobile robot. In a multiple-robot environment, a Petri net model (PNM) is used to prevent inter-robot collision during navigation. A series of simulations and experiments is conducted using mobile robots to show the effectiveness of the proposed algorithm.