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

• Published in: Journal of marine science and engineering Vol. 11; no. 4; p. 761
• Main Authors: Sahoo, Sarada Prasanna, Das, Bikramaditya, Pati, Bibhuti Bhusan, Garcia Marquez, Fausto Pedro, Segovia Ramirez, Isaac
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Algorithms; autonomous underwater vehicle (AUV); Autonomous underwater vehicles; Barriers; Bionics; Collision avoidance; Communication; cooperative path planning; Distance; Dynamic programming; Fines & penalties; genetic algorithm (GA); Genetic algorithms; grey wolf optimization (GWO); Mathematical optimization; Optimization; Path planning; Planning; Proportional derivative; proportional-derivative controller; Remote submersibles; Statistical analysis; Statistical methods; Underwater vehicles
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse11040761

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