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

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，但它通常是分散的，对人类健康构成的风险很小。然而，过去与核武器生产、核电站燃料制造以及这些活动产生的放射性废物的管理有关的工业活动造成了大量高度污染的设施。污染程度可能很高，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水平辐射照射有关的长期风险，因此通常不鼓励进入污染较少的地方，以尽量减少可能存在的任何风险。事情变得更加复杂，因为很难进入这些地方，这使得我们更难理解需要做些什么来确保这些地方的安全。其中一些设施不安全，因为它们很旧，而且设计的寿命也没有这么长。重要的是，现在要使它们安全，以确保放射性不会泄漏，因为随着新问题的出现，这需要的时间越长，难度和成本就越高。然而，这将需要很长的时间才能完成：在塞拉菲尔德，完成这一工作所需的时间预计为120年。这意味着，如果现在不加以有效处理，这些问题将落到子孙后代的头上。因此，从伦理学的角度来看，当务之急是现在就采取行动加以预防。了解这些放射性危害的一种方法是派遣机器人。由于最近的研究，在这方面已经取得了很大的进展，部分是由领导这项建议的人完成的。然而，简单地将辐射探测器运送到一个地方并试图确定它在哪里检测到最多的辐射并不起作用，原因有两个：首先，这些地方的放射性通常是分散的，这意味着它不会集中在一个容易快速处理的地方。相反，污染来自容器中的泄漏、飞溅、潮痕，并且它迁移到多孔材料中，在空间中产生3D分布。辐射探测器系统和成像仪在这方面有困难，因为它们通常从特定的角度提供评估，而这些角度可能无法告诉我们需要知道的一切。其次，受污染的地方往往堆满了工艺设备、碎石和建筑材料。这会导致辐射散射和吸收，从而影响“图像”，并影响人们认为存在的辐射量。如果有一个人在“循环”中--在有污染的空间中--他们可以通过移动到不同的Vantage位置，将碎片移到一边，并通过他们所看到的来推断所涉及的内容。这是不可能的，使用商业机器人平台构成了一种可以复制的方式。例如，通过从一些互补的Vantage位置进行评估，并融合从各种角度获得的数据。然而，重要的是要保持这些操作的人的监督，通过驾驶机器人，而不是给它完全自主权的情况下出现的困难，在恢复它等，这就提出了一个问题：我们如何解释机器人衍生的信息，从各种角度来看，从一个混乱的空间被分散的放射性污染，以帮助我们了解可能存在的危险，快速和有效的？我们的研究直接呼吁这一要求：我们怀疑，检测器的响应是一个相对简单的子响应的组合，如果污染是由污染的像素。通过推进我们对这些因素对机器人配置的辐射探测器系统的综合影响的解释，我们希望将我们观察到的与存在的放射性的性质联系起来，从而使机器人能够更有效地帮助清理这些空间。

项目成果

Lessons learned: Symbiotic autonomous robot ecosystem for nuclear environments

• DOI: 10.1049/csy2.12103
• 发表时间: 2023-12-01
• 期刊: IET CYBER-SYSTEMS AND ROBOTICS
• 作者: Mitchell，Daniel;Emor Baniqued，Paul Dominick;Jiang，Zhengyi
• 通讯作者: Jiang，Zhengyi

Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot.

• DOI: 10.1038/s41598-021-93474-4
• 发表时间: 2021-07-07
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: West A;Tsitsimpelis I;Licata M;Jazbec AE;Snoj L;Joyce MJ;Lennox B
• 通讯作者: Lennox B

Improved localization of radioactivity with a normalized sinc transform

通过归一化 sinc 变换改进放射性定位

• DOI: 10.3389/fnuen.2022.989361
• 发表时间: 2022
• 期刊: Frontiers in Nuclear Engineering
• 作者: Tsitsimpelis I

A GPS-enabled seabed sediment sampler: Recovery efficiency and efficacy.

支持 GPS 的海底沉积物采样器：回收效率和功效。

• DOI: 10.1063/5.0077269
• 发表时间: 2022
• 期刊: The Review of scientific instruments
• 作者: Hunt WJ

CARMA II: A ground vehicle for autonomous surveying of alpha, beta and gamma radiation.

CARMA II：用于自主测量 α、β 和 γ 辐射的地面车辆。

• DOI: 10.3389/frobt.2023.1137750
• 发表时间: 2023
• 期刊: FRONTIERS IN ROBOTICS AND AI
• 影响因子: 3.4
• 作者: Nouri Rahmat Abadi, Bahman;West, Andrew;Nancekievill, Matthew;Ballard, Christopher;Lennox, Barry;Marjanovic, Ognjen;Groves, Keir
• 通讯作者: Groves, Keir