Performance evaluation of particle swarm intelligence based optimization techniques in a novel AUV path planner
Lim, HS and Fan, S and Chin, CKH and Chai, S, Performance evaluation of particle swarm intelligence based optimization techniques in a novel AUV path planner, Proceedings of the 2018 IEEE OES Autonomous Underwater Vehicle Symposium, 06-09 November 2018, Porto, Portugal, pp. 1-7. ISBN 9781728102535 (2018) [Refereed Conference Paper]
Over years of development, many optimization
techniques have been proposed for the path planning of the
Autonomous Underwater Vehicle (AUV). The development in
swarm intelligence optimization, particularly the particle swarm
optimization (PSO), has significantly improved the performance
of the AUV path planner. This study presents 12 variants of
particle swarm intelligence (PSI)-based algorithms, which were
applied to evaluate their performances in solving the optimal path
planning problem of an AUV operating in 2D and 3D ocean
environments with obstacles and non-uniform currents.
Throughout the structure of the optimization problem, the
practicability of the path planning algorithms were considered by
taking into account the physical limitations of the AUV actuations.
To compare the performances of these PSI-based algorithms,
extensive Monte Carlo simulations were conducted to evaluate
these algorithms based on their respective solution qualities,
stabilities and computational efficiencies. Ultimately, the strengths
and weaknesses of these algorithms were comprehensively
analyzed, in order to identify the most appropriate optimization
algorithm for AUV path planning in dynamic environments.
Refereed Conference Paper
autonomous underwater vehicle, dynamic modeling, control and estimation, path planning, optimization, swarm intelligence, particle swarm optimization