Multi-Robot Coverage Path Planning for the Inspection of Offshore Wind Farms: A Review

Foster, Ashley JI ORCID: 0009-0005-8996-5953, Gianni, Mario ORCID: 0000-0001-5410-2377, Aly, Amir ORCID: 0000-0001-5169-0679, Samani, Hooman ORCID: 0000-0003-1494-2798 and Sharma, Sanjay ORCID: 0000-0002-5062-3199 (2024) Multi-Robot Coverage Path Planning for the Inspection of Offshore Wind Farms: A Review. Drones, 8 (1). p. 10.

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Official URL: http://dx.doi.org/10.3390/drones8010010

Abstract

<jats:p>Offshore wind turbine (OWT) inspection research is receiving increasing interest as the sector grows worldwide. Wind farms are far from emergency services and experience extreme weather and winds. This hazardous environment lends itself to unmanned approaches, reducing human exposure to risk. Increasing automation in inspections can reduce human effort and financial costs. Despite the benefits, research on automating inspection is sparse. This work proposes that OWT inspection can be described as a multi-robot coverage path planning problem. Reviews of multi-robot coverage exist, but to the best of our knowledge, none captures the domain-specific aspects of an OWT inspection. In this paper, we present a review on the current state of the art of multi-robot coverage to identify gaps in research relating to coverage for OWT inspection. To perform a qualitative study, the PICo (population, intervention, and context) framework was used. The retrieved works are analysed according to three aspects of coverage approaches: environmental modelling, decision making, and coordination. Based on the reviewed studies and the conducted analysis, candidate approaches are proposed for the structural coverage of an OWT. Future research should involve the adaptation of voxel-based ray-tracing pose generation to UAVs and exploration, applying semantic labels to tasks to facilitate heterogeneous coverage and semantic online task decomposition to identify the coverage target during the run time.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	7 Affordable and Clean Energy
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	05 Jan 2024 15:46
Last Modified:	15 Mar 2024 20:03
DOI:	10.3390/drones8010010
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177700