@Article{cmes.2020.011648,
AUTHOR = {Hui Sheng Lim, Christopher K. H. Chin, Shuhong Chai,  Neil Bose},
TITLE = {Online AUV Path Replanning Using Quantum-Behaved Particle Swarm Optimization with Selective Differential Evolution},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {125},
YEAR = {2020},
NUMBER = {1},
PAGES = {33--50},
URL = {http://www.techscience.com/CMES/v125n1/40204},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2020.011648}
}