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

用于长期操作和维护的人机协作（O

• 批准号: 2738688
• 金额: --
• 依托单位: Plymouth University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2738688
• 关键词: Human; Robot; Teaming; Long; Term

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).

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，用于协调和合作无人水面车辆（USV）和无人机（USV），以实现海上风电场的长期运营和维护（O&M）。O&M活动占海上风电项目生命周期成本的近四分之一。英国拥有最大的海上风力发电装机容量，约为7.2GW。因此，降低海上风电生产成本是政府的首要任务。在研究和创新领域，该计划的主要目标是（1）限制叶片检查的人为干预，防止人员暴露于危险风险，降低干预成本并加快过程;（2）最大限度地减少检查海上资产所需的时间，以减少设施的闲置期;（3）提供对条件的准确分析以降低维护过程的成本，以及，（4）提供另一种风险评估方法，以降低可再生能源项目的风险管理服务成本。该计划利用普利茅斯大学提供的先进设施，位于布里斯托的ORE弹射器机器人和自主系统（RAS）测试和验证设施以及热机器人国家核用户设施的ORE弹射器平台的利文茅斯演示涡轮机。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。