Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Path Planning in Case of Swarm Unmanned Surface Vehicles for Visiting Multiple Targets

Version 1 : Received: 10 February 2023 / Approved: 16 February 2023 / Online: 16 February 2023 (06:23:13 CET)

A peer-reviewed article of this Preprint also exists.

Ntakolia, C.; Papaleonidas, C.; Lyruidis, D.V. Swarm Unmanned Surface Vehicle Path Planning for Visiting Multiple Targets. Transportation Research Procedia 2023, 72, 1507–1514, doi:10.1016/j.trpro.2023.11.617. Ntakolia, C.; Papaleonidas, C.; Lyruidis, D.V. Swarm Unmanned Surface Vehicle Path Planning for Visiting Multiple Targets. Transportation Research Procedia 2023, 72, 1507–1514, doi:10.1016/j.trpro.2023.11.617.

Abstract

In this study we present a hybrid approach of ACO with fuzzy logic and clustering methods to solve multi-objective path planning problems in case of swarm USVs. This study aims to enhance the performance of ACO algorithm by integrating fuzzy logic in order to cope with the multiple contradicting objectives and generate quality solutions by in parallel identifying the mission areas of each USV to reach the desired targets. The objectives that are taken into account are the minimization of traveled distance and energy consumption, and the maximization of path smoothness. A comparative evaluation is conducted among ACO and fuzzy inference systems, Mamdani (ACO-M) and Takagi–Sugeno–Kang (ACO-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. ACO-M generates better paths but ACO-TSK presents higher computation efficiency.

Keywords

Ant colony optimization; Fuzzy logic; Multi-objective path planning; Swarm USV; Metaheuristics

Subject

Engineering, Marine Engineering

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