Open Access

ARTICLE

Online AUV Path Replanning Using Quantum-Behaved Particle Swarm Optimization with Selective Differential Evolution

Hui Sheng Lim^1,*, Christopher K. H. Chin¹, Shuhong Chai¹, Neil Bose^1,2
1 National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Launceston, TAS, 7250, Australia
2 Memorial University of Newfoundland, St. John’s, NL, A1C 5S7, Canada
* Corresponding Author: Hui Sheng Lim. Email:

Computer Modeling in Engineering & Sciences 2020, 125(1), 33-50. https://doi.org/10.32604/cmes.2020.011648

Received 29 May 2020; Accepted 27 July 2020; Issue published 18 September 2020

Abstract

This paper presents an online AUV (autonomous underwater vehicle) path planner that employs path replanning approach and the SDEQPSO (selective differential evolution-hybridized quantum-behaved particle swarm optimization) algorithm to optimize an AUV mission conducted in an unknown, dynamic and cluttered ocean environment. The proposed path replanner considered the effect of ocean currents in path optimization to generate a Pareto-optimal path that guides the AUV to its target within minimum time. The optimization was based on the onboard sensor data measured from the environment, which consists of a priori unknown dynamic obstacles and spatiotemporal currents. Different sensor arrangements for the forward-looking sonar and horizontal acoustic Doppler current profiler (H-ADCP) were considered in 2D and 3D simulations. Based on the simulation results, the SDEQPSO path replanner was found to be capable of generating a time-optimal path that offered up to 13% reduction in travel time compared to the situation where the vehicle simply followed a path with the shortest distance. The proposed replanning technique also showed consistently better performance over a reactive path planner in terms of solution quality, stability, and computational efficiency. Robustness of the replanner was verified under stochastic process using the Monte Carlo method. The generated path fulfilled the vehicle’s safety and physical constraints, while intelligently exploiting ocean currents to improve the vehicle’s efficiency.

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Keywords

Autonomous underwater vehicle; path planning; particle swarm optimization; sonar detection; Monte Carlo methods

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