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

该跨学科项目的主要目的是开发一个基于深度学习和群优化的框架，用于海上风电场长期运营和维护（O&M）的无人水面车辆（usv）和无人飞行器（usv）车队的协调与合作。运维活动几乎占海上风电项目生命周期成本的四分之一。英国拥有最大的海上风电装机容量，约为7.2吉瓦。因此，降低海上风力发电的成本在政府的议程上极为重要。在研究和创新领域，该计划的主要目标是：(1)限制人为干预叶片检查，防止人类暴露于危险风险，降低干预成本，加快过程；(2)最大限度地减少检查海上资产所需的时间，减少设备的闲置时间；(3)提供准确的条件分析，以降低维护过程的成本；(4)提供一种替代的风险评估方法，以降低可再生能源项目的风险管理服务成本。该项目利用了普利茅斯大学提供的尖端设备，在机器人和自主系统（RAS）测试和验证设施的ORE弹射器平台的利文茅斯演示涡轮机，以及布里斯托尔的热机器人国家核用户设施。该团队还设想与泰雷兹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).