Advancing Location Accuracy via Collimated Nuclear Assay for Decommissioning Robotic Applications (ALACANDRA)

通过用于退役机器人应用的准直核分析提高定位精度 (ALACANDRA)

• 批准号: EP/V026941/1
• 负责人: Malcolm Joyce
• 金额: $ 86.79万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV026941%2F1
• 关键词: Advancing; Location; Accuracy; via; Collimated; Advancing; Location; Accuracy; via; Collimated

Radioactivity is all around us but it is usually dispersed such that it poses little risk to human health. However, past industrial activities associated with nuclear weapons production, the manufacture of fuel for nuclear power stations and the management of radioactive waste from these activities have resulted in a significant number of highly contaminated facilities. The level of contamination can be so great that people cannot enter because the radiation level is too high. Further, because we do not understand the long-term risks associated with low-level radiation exposures, entry to place contaminated less is often discouraged to minimise any risk that there might be. Matters are complicated further because difficulty getting inside complicates our ability to understand exactly what needs to be done to make these places safe.Some of these facilities are not safe because they are old and were not designed to last this long. It is important to make them safe now to ensure radioactivity does not get out, and because the longer this takes the more difficult and expensive it becomes as new problems arise. However, this will take a long time to complete: at Sellafield, the time needed to complete this is forecast to be 120 years. This means that if they are not dealt with effectively now, these problems will fall to future generations; hence, from an ethical standpoint, the imperative is to prevent this by action now.One way to understand these radiological hazards is to send in a robot. Great advances have been made in this regard as a result of recent research, done in part by the people leading this proposal. However, simply transporting a radiation detector into a place and trying to determine where it detects the most radiation does not work for two important reasons: Firstly, radioactivity in these places is often dispersed, meaning that it is not concentrated in one place that might be dealt with easily and quickly. Instead, contamination arises from leaks, splashes, tide marks in vessels and it migrates into porous materials, yielding a 3D distribution in space. Radiation detector systems and imagers have difficulty with this because they often provide an assessment from a particular perspective that may not tell us everything we need to know. Secondly, contaminated places are often cluttered with process equipment, detritus and construction materials. These can cause the radiation to scatter and also absorb it. This influences the 'picture' and can influence how much radioactivity is thought to be present.With a human 'in the loop' - in the space with the contamination - they could improvise by moving to different vantage points, moving debris out of the way and by inferring what is involved from what they see. This not being possible, the use of a commercial robotic platform constitutes a way by which this might be replicated. For example, by assessments from a number of complementary vantage points and fusing the data obtained from this variety of perspectives. However, it is important to maintain human oversight of these operations by driving the robot rather than affording it full autonomy in case difficulties arise in recovering it etc. This raises the question: How can we interpret robot-derived information from a variety of perspectives, from a cluttered space contaminated with dispersed radioactivity, to help us understand what hazards may exist, quickly and effectively? Our research appeals directly to this requirement: we suspect that a detector's response is related to a relatively simple combination of sub-responses, as if the contamination were comprised of pixels of contamination. By advancing our interpretation of the combined influence of these on a radiation detector system configured by a robot, we hope to connect what we observe with nature of the radioactivity that is present, hence enabling robots to assist in the clean-up of these spaces more efficiently.

放射性无处不在，但通常被分散，因此对人类健康的风险很小。但是，过去与核武器生产有关的工业活动，核电站的燃料生产以及从这些活动中的放射性废物管理导致了大量高度污染的设施。污染的水平是如此之大，以至于人们无法进入，因为辐射水平太高。此外，由于我们不了解与低级辐射暴露相关的长期风险，因此通常不鼓励进入被污染的污染物少，以最大程度地降低可能存在的任何风险。事情进一步复杂，因为难以进入内部使我们能够确切地了解使这些地方安全需要做的事情的能力变得复杂。这些设施中的某些设施不安全，因为它们很老，并且并没有持续这么长时间。现在重要的是要确保它们安全以确保放射性不会脱颖而出，而且因为这种情况的时间越长，随着新问题的出现，它就会变得更加困难和昂贵。但是，这将需要很长时间才能完成：在Sellafield，完成此任务所需的时间将是120年。这意味着，如果他们现在没有有效处理，这些问题将落到后代。因此，从道德的角度来看，必须立即采取行动来防止这种情况。理解这些放射学危害的一种方法是发送机器人。由于最近的研究，这方面已经取得了巨大的进步，部分是由领导该建议的人们所做的。但是，只需将辐射探测器运送到一个地方，并试图确定其检测到最大的辐射在哪里不起作用，这两个重要原因：首先，这些地方的放射性经常被分散，这意味着它不集中在一个可能容易，快速处理的地方。取而代之的是，污染源于泄漏，飞溅，潮汐痕迹，并迁移到多孔材料中，在太空中产生3D分布。辐射探测器系统和成像器对此有困难，因为它们通常从特定的角度提供评估，这些角度可能无法告诉我们我们需要知道的一切。其次，受污染的地方通常用工艺设备，碎屑和建筑材料混乱。这些会导致辐射散射并吸收它。这会影响“图片”，并可以影响人们认为存在多少放射性。在循环中的人类“在污染的空间中”，它们可以通过移动到不同的有利位置，将碎屑移开并从所看到的东西中推断出涉及的内容来即兴创造。这是不可能的，商业机器人平台的使用构成了一种可以复制的方式。例如，通过从多个互补的有利位点进行评估，并融合从这种各种角度获得的数据。但是，重要的是要通过驱动机器人来维持对这些操作的监督，而不是在恢复它的困难时提供全部自主权。这引发了一个问题：我们如何从各种角度来解释机器人衍生的信息，从杂乱无章的空间中受到分散的放射性污染的杂物，以帮助我们快速地了解什么危害，并有效地了解，可以快速和有效地了解？我们的研究直接提出了这一要求：我们怀疑探测器的响应与亚子子的相对简单组合有关，好像污染物由污染像素组成。通过推进我们对这些机器人配置的辐射探测器系统的综合影响的解释，我们希望将观察到的内容与存在的放射性性质联系起来，从而使机器人能够更有效地帮助清理这些空间。