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Voronoi-Visibility Roadmap-based Path Planning Algorithm for Unmanned Surface Vehicles

Published online by Cambridge University Press:  24 January 2019

Hanlin Niu ,
Al Savvaris ,
Antonios Tsourdos  and
Ze Ji
Hanlin Niu*
Affiliation:
(School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford, MK43 0AL, UK) (School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK)
Al Savvaris
Affiliation:
(School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford, MK43 0AL, UK)
Antonios Tsourdos
Affiliation:
(School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford, MK43 0AL, UK)
Ze Ji
Affiliation:
(School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK)
*
(E-mail: [email protected])
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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.

Keywords

Unmanned Surface VehiclePath PlanningVoronoi DiagramVisibility Graph
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 4 , July 2019 , pp. 850 - 874
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

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