Human-Robot Teaming for Long-Term Operations and Maintenance (O&M) of Offshore Renewable Energy Devices

这一跨学科计划的主要目的是开发一个基于深度学习和群体优化的框架，用于海上风电场的长期运营和维护(O&M)的无人水面航行器(USV)和无人驾驶飞行器(USV)机队的协调和合作。O&M活动几乎占海上风电项目生命周期成本的四分之一。英国的海上风力发电装机容量最大，约为7.2千兆瓦。因此，降低海上风能发电成本在政府的议程上是非常高的。在研究和创新领域，该计划的主要目标是(1)限制对叶片检查的人为干预，防止人类暴露在危险风险中，降低干预成本并加快进程；(2)最大限度地减少检查海上资产所需的时间，以缩短安装的不活动时间；(3)提供准确的条件分析以降低维护过程的成本，以及(4)提供一种替代的风险评估方法，以降低可再生能源项目在风险管理服务方面的成本。该计划利用了普利茅斯大学提供的尖端设施，位于机器人和自主系统(RAS)的矿石弹射器(RAS)测试和验证设施，以及布里斯托尔的国家核用户设施，用于热机器人。该团队还设想与泰利斯和应用自动化有限公司(普利茅斯)合作。

The main aim of this interdisciplinary program is to develop a framework based on Deep Learning and Swarm Optimization for the coordination and cooperation of a fleet of Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (USVs) for long-term Operation and Maintenance (O&M) of offshore wind farms.O&M activities make up almost a quarter of the lifetime costs of an offshore wind project. The UK has the largest installed capacity of offshore wind generating around 7.2GW capacity. Therefore, reducing the cost of electricity production by offshore wind is extremely high on the Government's agenda.In the research and innovation area the main objectives of the program are to (1) limit human intervention for blade inspections preventing human exposure to dangerous risks, reducing the cost of the intervention and speeding up the process; (2) minimize the amount of time needed to inspect an offshore asset to reduce the inactivity period of the installation; (3) provide an accurate analysis of the conditions to reduce the cost of the maintenance process, and, (4) provide an alternative method for risk assessment which would reduce the costs incurred by the renewable energy projects for risk management services.The program exploits the cutting-edge facilities offered by the University of Plymouth, the Levenmouth Demonstration Turbine of ORE Catapult platform at the Robotics and Autonomous Systems (RAS) testing and validation facilities of ORE Catapult and the National Nuclear User Facility for Hot Robotics in Bristol. The team also envisages a collaboration with Thales and Applied Automation Ltd (Plymouth).