Open Access
ARTICLE
Online AUV Path Replanning Using Quantum-Behaved Particle Swarm Optimization with Selective Differential Evolution
Hui Sheng Lim1,*, Christopher K. H. Chin1, Shuhong Chai1, Neil Bose1,2
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.
Keywords
Cite This Article
Lim, H. S., K., C., Chai, S., Bose, N. (2020). Online AUV Path Replanning Using Quantum-Behaved Particle Swarm Optimization with Selective Differential Evolution.
CMES-Computer Modeling in Engineering & Sciences, 125(1), 33–50.