With the increasing scale of offshore wind farm development, maintaining farms efficiently and safely becomes a necessity. The length of turbine downtime and the logistics for human technician transfer make up a significant proportion of the operation and maintenance (O&M) costs. To reduce such costs, future O&M infrastructures will increasingly rely on offshore autonomous robotic solutions that are capable of co-managing wind farms with human operators located onshore. In particular, unmanned aerial vehicles, autonomous surface vessels and crawling robots are expected to play important roles not only to bring down costs but also to significantly reduce the health and safety risks by assisting (or replacing) human operators in performing the most hazardous tasks. This paper portrays a visionary view in which heterogeneous robotic assets, underpinned by AI agent technology, coordinate their behavior to autonomously inspect, maintain and repair offshore wind farms over long periods of time and unstable weather conditions. They cooperate with onshore human operators, who supervise the mission at a distance, via the use of shared deliberation techniques. We highlight several challenging research directions in this context and offer ambitious ideas to tackle them as well as initial solutions.

A multi-robot platform for the autonomous operation and maintenance of offshore wind farms blue sky ideas track / Bernardini, S.; Jovan, F.; Jiang, Z.; Watson, S.; Moradi, P.; Richardson, T.; Sadeghian, R.; Sareh, S.. - (2020), pp. 1696-1700. (Intervento presentato al convegno International Joint Conference on Autonomous Agents and Multiagent Systems (previously the International Conference on Multiagent Systems, ICMAS, changed in 2000) tenutosi a Auckland, New Zealand).

A multi-robot platform for the autonomous operation and maintenance of offshore wind farms blue sky ideas track

Bernardini S.
;
2020

Abstract

With the increasing scale of offshore wind farm development, maintaining farms efficiently and safely becomes a necessity. The length of turbine downtime and the logistics for human technician transfer make up a significant proportion of the operation and maintenance (O&M) costs. To reduce such costs, future O&M infrastructures will increasingly rely on offshore autonomous robotic solutions that are capable of co-managing wind farms with human operators located onshore. In particular, unmanned aerial vehicles, autonomous surface vessels and crawling robots are expected to play important roles not only to bring down costs but also to significantly reduce the health and safety risks by assisting (or replacing) human operators in performing the most hazardous tasks. This paper portrays a visionary view in which heterogeneous robotic assets, underpinned by AI agent technology, coordinate their behavior to autonomously inspect, maintain and repair offshore wind farms over long periods of time and unstable weather conditions. They cooperate with onshore human operators, who supervise the mission at a distance, via the use of shared deliberation techniques. We highlight several challenging research directions in this context and offer ambitious ideas to tackle them as well as initial solutions.
2020
International Joint Conference on Autonomous Agents and Multiagent Systems (previously the International Conference on Multiagent Systems, ICMAS, changed in 2000)
AI Planning; Autonomy; Explainability; Extreme Environments; Multi-agency; Robotics; Wind Farms
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A multi-robot platform for the autonomous operation and maintenance of offshore wind farms blue sky ideas track / Bernardini, S.; Jovan, F.; Jiang, Z.; Watson, S.; Moradi, P.; Richardson, T.; Sadeghian, R.; Sareh, S.. - (2020), pp. 1696-1700. (Intervento presentato al convegno International Joint Conference on Autonomous Agents and Multiagent Systems (previously the International Conference on Multiagent Systems, ICMAS, changed in 2000) tenutosi a Auckland, New Zealand).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1708634
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