Journal article
Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms
Applied soft computing, Vol.70, pp.929-945
2018
Abstract
In this study, a single autonomous underwater vehicle (AUV) aims to rendezvous with a submerged leader recovery vehicle through a cluttered and variable operating field. The rendezvous problem is transformed into a Nonlinear Optimal Control Problem (NOCP) and then numerical solutions are provided. A penalty function method is utilized to combine the boundary conditions, vehicular and environmental constraints with the performance index that is final rendezvous time. Four evolutionary based path planning methods namely Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), Differential Evolution (DE), and Firefly Algorithm (FA) are employed to establish a reactive planner module and provide a numerical solution for the proposed NOCP. The objective is to synthesize and analyze the performance and capability of the mentioned methods for guiding an AUV from an initial loitering point toward the rendezvous through a comprehensive simulation study. The proposed planner module entails a heuristic for refining the path considering situational awareness of environment, encompassing static and dynamic obstacles within a spatiotemporal current fields. The planner thus needs to accommodate the unforeseen changes in the operating field such as emergence of unpredicted obstacles or variability of current field and turbulent regions. The simulation results demonstrate the inherent robustness and efficiency of the proposed planner for enhancing a vehicle’s autonomy so as to enable it to reach the desired rendezvous. The advantages and shortcoming of all utilized methods are also presented based on the obtained results.
Details
- Title
- Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms
- Authors/Creators
- S. MahmoudZadeh - Flinders UniversityA. M. Yazdani - Flinders UniversityK. Sammut - Flinders UniversityD. M. W. Powers - Flinders University
- Publication Details
- Applied soft computing, Vol.70, pp.929-945
- Publisher
- Elsevier
- Number of pages
- 17
- Identifiers
- 991005592640007891
- Copyright
- © 2017 Elsevier B.V.
- Murdoch Affiliation
- School of Engineering and Energy; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.29 Automation & Control Systems
- 4.29.435 Multi Agent Systems
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- ESI research areas
- Computer Science