Grey wolf optimization for global path planning of autonomous underwater vehicle

Abstract

Path planning problem (PPP) deals with finding an optimized path between a source and a goal point. Global path planning (GPP) for Autonomous underwater vehicle (AUV), provides an optimized predefined path to reach the desired destination of the AUV. AUVs are largely useful in missions involving marine geoscience, scientific research, military warfare, along with commercial sectors of oil and gas industries. A time optimized path that can avoid collision helps in reducing time and energy expenses of such real time missions. Grey Wolf Optimization (GWO) is a nature inspired metaheuristic algorithm based on hunting behavior of the grey wolves. GWO provides better exploration of the solution space and good at avoiding local minima. This research presents an overview of GWO with its mathematical modelling. The research mainly contributes in applying GWO for path planning of an AUV to generate a global path in a two-dimensional underwater environment with static obstacles. Simulation results are obtained using MATLAB. The resultant path is optimized in time, distance travel and requires less processing time as compared to results obtained by applying Ant colony Optimization (ACO) for the same problem.