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UK Robotics and Artificial Intelligence Hub for Offshore Energy Asset Integrity Management

UK Robotics and Artificial Intelligence Hub for Offshore Energy Asset Integrity Management
英国海上能源资产完整性管理机器人和人工智能中心
批准号:
EP/R026173/1
负责人:
David Lane
金额:
$1939.74万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --

项目摘要

项目成果

David Lane的其他基金

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中文摘要
翻译
国际海上能源行业目前面临着三重挑战:预计油价将保持在每桶50美元以下,旧基础设施(特别是北海)的退役承诺成本高昂,海上可再生能源每千瓦时的交易商品价格利润微薄。此外,海外劳动力正在老龄化,因为新一代合适的毕业生不愿在海外危险的地方工作。因此,作业者寻求更具成本效益、更安全的方法和商业模式来检查、维修和维护其上层和海上基础设施。机器人和人工智能被视为这方面的关键推动因素,因为离岸人员减少可以降低成本,提高安全性和工作场所的吸引力。因此,行业的长期愿景是建立一个完全自主的海上能源领域,在岸上操作、检查和维护。现在是时候进一步开发、整合和降低这些可认证的评估原型的风险了,因为迫切需要保持英国海上石油和可再生能源领域的经济效益,并开发出英国初创企业、中小企业和供应链可以出口到国际的更高效、更敏捷的产品和服务。这将维持一个目前价值400亿英镑的关键经济部门,为英国经济提供44万个就业岗位,并为商品和服务出口增加60亿英镑的供应链。ORCA中心是一项雄心勃勃的计划,汇集了来自5所英国大学的国际领先专家和30多个行业合作伙伴(投资1,750万英镑)。由爱丁堡机器人中心(HWU/UoE)领导,与帝国理工学院,牛津大学和利物浦大学合作,这个多学科联盟带来了其独特的专业知识:海底(HWU),地面(UoE, Oxf)和空中机器人(ICL);以及人机交互(HWU, UoE),用于无损评估的创新传感器和低成本传感器网络(ICL, UoE);资产管理和认证(HWU, UoE, LIV)。Hub将使用机器人和人工智能提供改变游戏规则的远程解决方案,这些解决方案可以与现有和未来的资产和传感器集成,并且可以在复杂和混乱的环境中以自主或半自主模式安全地操作和交互。我们将开发机器人解决方案,实现海上资产的精确测绘、导航和交互,支持部署用于资产监控的传感器网络。人机系统将能够通过智能界面与远程操作人员合作,在这些复杂、高风险的情况下管理用户的认知负荷。机器人和传感器将集成到一个广泛的资产完整性信息和规划平台中,该平台支持资产和机器人的自我认证。
英文摘要
The international offshore energy industry currently faces the triple challenges of an oil price expected to remain less than $50 a barrel, significant expensive decommissioning commitments of old infrastructure (especially North Sea) and small margins on the traded commodity price per KWh of offshore renewable energy. Further, the offshore workforce is ageing as new generations of suitable graduates prefer not to work in hazardous places offshore. Operators therefore seek more cost effective, safe methods and business models for inspection, repair and maintenance of their topside and marine offshore infrastructure. Robotics and artificial intelligence are seen as key enablers in this regard as fewer staff offshore reduces cost, increases safety and workplace appeal. The long-term industry vision is thus for a completely autonomous offshore energy field, operated, inspected and maintained from the shore. The time is now right to further develop, integrate and de-risk these into certifiable evaluation prototypes because there is a pressing need to keep UK offshore oil and renewable energy fields economic, and to develop more productive and agile products and services that UK startups, SMEs and the supply chain can export internationally. This will maintain a key economic sector currently worth £40 billion and 440,000 jobs to the UK economy, and a supply chain adding a further £6 billion in exports of goods and services. The ORCA Hub is an ambitious initiative that brings together internationally leading experts from 5 UK universities with over 30 industry partners (>£17.5M investment). Led by the Edinburgh Centre of Robotics (HWU/UoE), in collaboration with Imperial College, Oxford and Liverpool Universities, this multi-disciplinary consortium brings its unique expertise in: Subsea (HWU), Ground (UoE, Oxf) and Aerial robotics (ICL); as well as human-machine interaction (HWU, UoE), innovative sensors for Non Destructive Evaluation and low-cost sensor networks (ICL, UoE); and asset management and certification (HWU, UoE, LIV). The Hub will provide game-changing, remote solutions using robotics and AI that are readily integratable with existing and future assets and sensors, and that can operate and interact safely in autonomous or semi-autonomous modes in complex and cluttered environments. We will develop robotics solutions enabling accurate mapping of, navigation around and interaction with offshore assets that support the deployment of sensors networks for asset monitoring. Human-machine systems will be able to co-operate with remotely located human operators through an intelligent interface that manages the cognitive load of users in these complex, high-risk situations. Robots and sensors will be integrated into a broad asset integrity information and planning platform that supports self-certification of the assets and robots.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
Online Optimal Impedance Planning for Legged Robots
足式机器人在线最优阻抗规划
DOI: 10.1109/iros40897.2019.8967696
发表时间: 2019
期刊:
影响因子: --
作者: [Angelini F]
通讯作者: Angelini F
DOI: 10.1089/soro.2020.0011
发表时间: 2021-12
期刊: Soft robotics
影响因子: 7.9
作者: [Aracri S, Giorgio-Serchi F, Suaria G, Sayed ME, Nemitz MP, Mahon S, Stokes AA]
通讯作者: Stokes AA
Integrated real-time, non-intrusive Measurements for Mental Load
集成的实时、非侵入式精神负荷测量
DOI: --
发表时间: 2019
期刊:
影响因子: --
作者: [Ahmad, M. I.]
通讯作者: Ahmad, M. I.
Using Causal Analysis to Learn Specifications from Task Demonstrations
使用因果分析从任务演示中了解规范
DOI: 10.48550/arxiv.1903.01267
发表时间: 2019
期刊: arXiv e-prints
影响因子: --
作者: [Angelov Daniel]
通讯作者: Angelov Daniel
共 8 条
    ORCA Stream B - Towards Resident Robots
    • 批准号:
      EP/W001136/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $244.06万
    • 财政年份:
      2021
    • 负责人:
      David Lane
    • 依托单位:
    Sustained Autonomy through Coupled Plan-based Control and World Modelling with Uncertainty
    • 批准号:
      EP/J012432/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $32.68万
    • 财政年份:
      2012
    • 负责人:
      David Lane
    • 依托单位:
    Collaborative Research: Online Statistics Education: An Interactive Multimedia Course of Study II
    • 批准号:
      0919818
    • 项目类别:
      Standard Grant
    • 资助金额:
      $20.47万
    • 财政年份:
      2009
    • 负责人:
      David Lane
    • 依托单位:
    Online Statistics Education: An Interactive Multimedia Course of Study
    • 批准号:
      0089435
    • 项目类别:
      Standard Grant
    • 资助金额:
      $40.2万
    • 财政年份:
      2001
    • 负责人:
      David Lane
    • 依托单位:
    海外基金