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Robotics and Artificial Intelligence for Nuclear (RAIN)

Robotics and Artificial Intelligence for Nuclear (RAIN)
核工业机器人和人工智能 (RAIN)
批准号:
EP/R026084/1
负责人:
Barry Lennox
金额:
$1631.98万
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 --

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中文摘要
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英文摘要
The nuclear industry has some of the most extreme environments in the world, with radiation levels and other hazards frequently restricting human access to facilities. Even when human entry is possible, the risks can be significant and very low levels of productivity. To date, robotic systems have had limited impact on the nuclear industry, but it is clear that they offer considerable opportunities for improved productivity and significantly reduced human risk. The nuclear industry has a vast array of highly complex and diverse challenges that span the entire industry: decommissioning and waste management, Plant Life Extension (PLEX), Nuclear New Build (NNB), small modular reactors (SMRs) and fusion.Whilst the challenges across the nuclear industry are varied, they share many similarities that relate to the extreme conditions that are present. Vitally these similarities also translate across into other environments, such as space, oil and gas and mining, all of which, for example, have challenges associated with radiation (high energy cosmic rays in space and the presence of naturally occurring radioactive materials (NORM) in mining and oil and gas). Major hazards associated with the nuclear industry include radiation; storage media (for example water, air, vacuum); lack of utilities (such as lighting, power or communications); restricted access; unstructured environments.These hazards mean that some challenges are currently intractable in the absence of solutions that will rely on future capabilities in Robotics and Artificial Intelligence (RAI). Reliable robotic systems are not just essential for future operations in the nuclear industry, but they also offer the potential to transform the industry globally. In decommissioning, robots will be required to characterise facilities (e.g. map dose rates, generate topographical maps and identify materials), inspect vessels and infrastructure, move, manipulate, cut, sort and segregate waste and assist operations staff. To support the life extension of existing nuclear power plants, robotic systems will be required to inspect and assess the integrity and condition of equipment and facilities and might even be used to implement urgent repairs in hard to reach areas of the plant. Similar systems will be required in NNB, fusion reactors and SMRs. Furthermore, it is essential that past mistakes in the design of nuclear facilities, which makes the deployment of robotic systems highly challenging, do not perpetuate into future builds. Even newly constructed facilities such as CERN, which now has many areas that are inaccessible to humans because of high radioactive dose rates, has been designed for human, rather than robotic intervention. Another major challenge that RAIN will grapple with is the use of digital technologies within the nuclear sector. Virtual and Augmented Reality, AI and machine learning have arrived but the nuclear sector is poorly positioned to understand and use these rapidly emerging technologies. RAIN will deliver the necessary step changes in fundamental robotics science and establish the pathways to impact that will enable the creation of a research and innovation ecosystem with the capability to lead the world in nuclear robotics. While our centre of gravity is around nuclear we have a keen focus on applications and exploitation in a much wider range of challenging environments.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
Input Shaping Predictive Functional Control for Different Types of Challenging Dynamics Processes
针对不同类型的挑战性动力学过程的输入整形预测功能控制
DOI: 10.3390/pr6080118
发表时间: 2018
期刊: Processes
影响因子: 3.5
作者: [Abdullah M]
通讯作者: Abdullah M
The effect of model structure on the noise and disturbance sensitivity of Predictive Functional Control
模型结构对预测函数控制噪声和扰动灵敏度的影响
DOI: --
发表时间: 2018
期刊:
影响因子: --
作者: [Abdullah, M.]
通讯作者: Abdullah, M.
Alternative Method for Predictive Functional Control to Handle an Integrating Process
处理积分过程的预测功能控制的替代方法
DOI: --
发表时间: 2017
期刊:
影响因子: --
作者: [Abdullah, M.]
通讯作者: Abdullah, M.
DOI: 10.1109/mis.2018.111144814
发表时间: 2018-11-01
期刊: IEEE INTELLIGENT SYSTEMS
影响因子: 6.4
作者: [Aitken, Jonathan M., Veres, Sandor M., Mort, Paul E.]
通讯作者: Mort, Paul E.
7
    Centre for Robotic Autonomy in Demanding and Long-lasting Environments (CRADLE)
    • 批准号:
      EP/X02489X/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $469.17万
    • 财政年份:
      2023
    • 负责人:
      Barry Lennox
    • 依托单位:
    Robotics and Artificial Intelligence for Nuclear Plus (RAIN+)
    • 批准号:
      EP/W001128/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $251.71万
    • 财政年份:
      2021
    • 负责人:
      Barry Lennox
    • 依托单位:
    Advancing Location Accuracy via Collimated Nuclear Assay for Decommissioning Robotic Applications (ALACANDRA)
    • 批准号:
      EP/V026925/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $67.19万
    • 财政年份:
      2021
    • 负责人:
      Barry Lennox
    • 依托单位:
    Robotics for Nuclear Environments
    • 批准号:
      EP/P01366X/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $592.54万
    • 财政年份:
      2017
    • 负责人:
      Barry Lennox
    • 依托单位:
    海外基金