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Particle swarm optimization algorithms with selective differential evolution for AUV path planning

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posted on 2023-05-20, 14:38 authored by Lim, HS, Fan, S, Christopher ChinChristopher Chin, Shuhong ChaiShuhong Chai, Neil Bose
Particle swarm optimization (PSO)-based algorithms are suitable for path planning of the Autonomous Underwater Vehicle (AUV) due to their high computational efficiency. However, such algorithms may produce sub-optimal paths or require higher computational load to produce an optimal path. This paper proposed a new approach that improves the ability of PSO-based algorithms to search for the optimal path while maintaining a low computational requirement. By hybridizing with differential evolution (DE), the proposed algorithms carry out the DE operator selectively to improve the search ability. The algorithms were applied in an offline AUV path planner to generate a near-optimal path that safely guides the AUV through an environment with a priori known obstacles and time-invariant non-uniform currents. The algorithm performances were benchmarked against other algorithms in an offline path planner because if the proposed algorithms can provide better computational efficiency to demonstrate the minimum capability of a path planner, then they will outperform the tested algorithms in a realistic scenario. Through Monte Carlo simulations and Kruskal-Wallis test, SDEAPSO (selective DE-hybridized PSO with adaptive factor) and SDEQPSO (selective DE-hybridized Quantum-behaved PSO) were found to be capable of generating feasible AUV path with higher efficiency than other algorithms tested, as indicated by their lower computational requirement and excellent path quality.

History

Publication title

International Journal of Robotics and Automation

Volume

9

Pagination

94-112

ISSN

2089-4856

Department/School

School of Natural Sciences

Publisher

Indonesia

Place of publication

Institute of Advanced Engineering and Science

Rights statement

Copyright 2020 The Authors. Licensed under Creative Commons Attribution-ShareAlike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode

Repository Status

  • Open

Socio-economic Objectives

Intelligence, surveillance and space

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