Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles

This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During planning, static obstacles affect the desired and distance which result in collision penalties. In this study, merits grey wolf optimization (GWO) genetic algorithm (GA) bionic-inspired algorithms are integrated to implement (HGWO) allows AUVs reach their destination safely an obstacle rich environment. The proposed planner is employed single AUV based on avoidance. It uses GA as initialization generator overcome random problem GWO. research, total cost considered be function Further, application extended cooperative while avoiding using communication consensus. Simulation results obtained both multiple 3D environment proportional-derivative controller. Kruskal–Wallis test non-parametric statistical analysis, where independence given by demonstrated.