Amir Aly
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Lecturer in computer science (AI and Robotics)
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Recent Grants
Grant: Open
Multimodal AI-based Diagnosis of Autism Spectrum Disorder
Open Date: 2023-10-01
Close Date: 2027-04-01
Grant: Close
Intergenerational Codesign of Novel Technologies in Coastal Communities (ICONIC)
Open Date: 2022-11-01
Close Date: 2025-05-01
Grant: Close
Human-Robot Teaming for Long-Term Operations and Maintenance (O&M) of Offshore Renewable Energy Devices
Open Date: 2022-10-01
Close Date: 2026-04-01
Grant: Close
Computational Modelling of Language Compositionality through Human-Robot Interaction
Open Date: 2022-10-01
Close Date: 2025-09-01
Grant: Close
Language Learning for Human-Robot Collaboration
Open Date: 2020-06-01
Close Date: 2021-03-01
Articles (3)
Year:
2024
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.
Year:
2023
Year:
2023
Collaborators (3)
Sanjay Sharma
University of Plymouth
Hooman Samani
University of Hertfordshire
Mario Gianni
Senior Lecturer in Robotics and Autonomous Systems
University of Liverpool
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