This study describes the implementation of an online path planner in an autonomous underwater vehicle (AUV) system by using an open-source system architecture, MOOS-IvP. The path planner employed a path replanning scheme and the selective differential evolution quantum-behaved particle swarm optimization (SDEQPSO) algorithm. The implementation was based on a modular framework to ensure the robustness of the path replanner during a mission. The performance of the path replanner was evaluated and verified under stochastic processes in hardware-in-the-loop (HIL) tests, in which the replanner interacted with the onboard controllers and actuators of an Explorer AUV. The experimental results showed the path replanner can be run seamlessly with the hardware onboard an Explorer AUV in real time to generate and continuously refine a safe and feasible path for a dynamic and unexplored environment.

Item Type:	Refereed Article
Keywords:	autonomous underwater vehicle, path planning, particle swarm optimization, hardware-in-the-loop simulation, real-time systems
Research Division:	Engineering
Research Group:	Maritime engineering
Research Field:	Ocean engineering
Objective Division:	Defence
Objective Group:	Defence
Objective Field:	Maritime
UTAS Author:	Lim, HS (Mr Hui Lim)
UTAS Author:	King, PD (Mr Peter King)
UTAS Author:	Chin, CKH (Dr Chris Chin)
UTAS Author:	Chai, S (Professor Shuhong Chai)
UTAS Author:	Bose, N (Professor Neil Bose)
ID Code:	148293
Year Published:	2022
Web of Science® Times Cited:	5
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2021-12-16
Last Modified:	2022-06-02
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