Mobile Robot Navigation in Static and Dynamic Environments using Various Soft Computing Techniques

Abstract

The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. The present research focuses on the design and implementation of the intelligent navigation algorithms, which is capable of navigating a mobile robot autonomously in static as well as dynamic environments. Navigation and obstacle avoidance are one of the most important tasks for any mobile robots. The primary objective of this research work is to improve the navigation accuracy and efficiency of the mobile robot using various soft computing techniques. In this research work, Hybrid Fuzzy (H-Fuzzy) architecture, Cascade Neuro-Fuzzy (CN-Fuzzy) architecture, Fuzzy-Simulated Annealing (Fuzzy-SA) algorithm, Wind Driven Optimization (WDO) algorithm, and Fuzzy-Wind Driven Optimization (Fuzzy-WDO) algorithm have been designed and implemented to solve the navigation problems of a mobile robot in different static and dynamic environments. The performances of these proposed techniques are demonstrated through computer simulations using MATLAB software and implemented in real time by using experimental mobile robots. Furthermore, the performances of Wind Driven Optimization algorithm and Fuzzy-Wind Driven Optimization algorithm are found to be most efficient (in terms of path length and navigation time) as compared to rest of the techniques, which verifies the effectiveness and efficiency of these newly built techniques for mobile robot navigation. The results obtained from the proposed techniques are compared with other developed techniques such as Fuzzy Logics, Genetic algorithm (GA), Neural Network, and Particle Swarm Optimization (PSO) algorithm, etc. to prove the authenticity of the proposed developed techniques.