Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Voronoi-visibility roadmap-based path planning algorithm for unmanned surface vehicles

Niu, Hanlin, Savvaris, Al, Tsourdos, Antonios and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2019. Voronoi-visibility roadmap-based path planning algorithm for unmanned surface vehicles. Journal of Navigation 72 (4) , pp. 850-874. 10.1017/S0373463318001005

[thumbnail of Voronoi-Visibility Roadmap-based Path Planning Algorithm for Unmanned Su.._.pdf]
Preview
PDF - Accepted Post-Print Version
Download (4MB) | Preview

Abstract

In this paper, a novel Voronoi-Visibility (VV) path planning algorithm, which integrates the merits of a Voronoi diagram and a Visibility graph, is proposed for solving the Unmanned Surface Vehicle (USV) path planning problem. The VM (Voronoi shortest path refined by Minimising the number of waypoints) algorithm was applied for performance comparison. The VV and VM algorithms were compared in ten Singapore Strait missions and five Croatian missions. To test the computational time, a high-resolution, large spatial dataset was used. It was demonstrated that the proposed algorithm not only improved the quality of the Voronoi shortest path but also maintained the computational efficiency of the Voronoi diagram in dealing with different geographical scenarios, while also keeping the USV at a configurable clearance distance c from coastlines. Quantitative results were generated by comparing the Voronoi, VM and VV algorithms in 2,000 randomly generated missions using the Singapore dataset.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Cambridge University Press
ISSN: 0373-4633
Date of First Compliant Deposit: 14 January 2019
Date of Acceptance: 24 November 2018
Last Modified: 06 Nov 2023 14:30
URI: https://orca.cardiff.ac.uk/id/eprint/118170

Citation Data

Cited 30 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics