Mobile robot path planning using fuzzy enhanced improved Multi-Objective particle swarm optimization (FIMOPSO)

Abstract

This paper introduces a method for car-like mobile robot path planning (CRPP). The robot works in both dynamic and static situations. The aim of this method is to explore the best safe path with minimum path length, minimum motor torque, minimum travel time, minimum robot acceleration and maximum obstacle avoidance. Kinodynamic and non-holonomic constraints related with car-like robot are considered. Fuzzy enhanced Improved Multi-objective Particle Swarm Optimization (FIMOPSO) algorithm is proposed to solve the CRPP problem. Fuzzy inference system is used for obstacle avoidance. In the proposed FIMOPSO, five improvements are made. Proposed technique is compared with Multi-objective Strength Pareto Evolutionary Algorithm 2 (MOSPEA2) technique. Experiments on a custom-made car-like robot are ensuring the quality of proposed technique. This research works show that proposed FIMOPSO is another alternative technique to CRPP problems. Paths dictated by FIMOPSO are safe, collision free, feasible, and possible and can be practically implemented. Fuzzy inference system works well for safe robot travel. FIMOPSO simulation paths are acceptable. Since, the deviation between experiment and simulation is less than 2%.