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)

基本信息

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

项目摘要

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，因此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管理相关的工业活动导致了大量高度污染的设施。污染程度可能非常严重，由于辐射水平太高，人们无法进入。此外，由于我们不了解与低水平辐射暴露相关的长期风险，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风险。事情变得更加复杂，因为进入内部的困难使我们无法准确理解需要采取哪些措施来确保这些地方的安全。其中一些设施并不安全，因为它们很旧，并且设计时无法持续这么长时间。现在重要的是确保它们的安全，以确保放射性不会泄漏，因为这需要的时间越长，随着新问题的出现，它就会变得越困难和昂贵。然而，这将需要很长时间才能完成：在塞拉菲尔德，完成这一任务预计需要 120 年。这意味着，如果现在不加以有效解决，这些问题将落到子孙后代身上；因此，从道德的角度来看，当务之急是立即采取行动来防止这种情况发生。了解这些放射性危害的一种方法是派遣机器人。最近的研究在这方面取得了巨大进展，部分是由领导该提案的人完成的。然而，简单地将辐射探测器运送到一个地方并试图确定它检测到最多辐射的位置是行不通的，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可能会被检测到的地方。轻松快速地处理。相反，污染是由容器中的泄漏、飞溅、潮汐痕迹引起的，并迁移到多孔材料中，在空间中产生 3D 分布。辐射探测器系统和成像仪在这方面遇到困难，因为它们经常从特定的角度提供评估，而这可能无法告诉我们需要知道的一切。其次，受污染的地方通常堆满了工艺设备、碎屑和建筑材料。这些会导致辐射散射并吸收辐射。这会影响“图片”，并可能影响被认为存在的放射性量。当人类“在环路中”时 - 在有污染的空间中 - 他们可以通过移动到不同的有利位置，将碎片移开来即兴发挥并根据他们所看到的推断出所涉及的内容。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这种情况的方式。例如，通过从多个互补的有利角度进行评估并融合从各种角度获得的数据。然而，重要的是通过驾驶机器人来维持人类对这些操作的监督，而不是在恢复机器人等过程中出现困难时为其提供完全的自主权。这就提出了一个问题：我们如何从不同的角度解释机器人衍生的信息，从被分散的放射性污染的杂乱空间中，帮助我们快速有效地了解可能存在哪些危险？我们的研究直接满足了这一要求：我们怀疑探测器的响应与相对简单的子响应组合有关，就好像污染是由污染像素组成的一样。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现象与存在的放射性性质联系起来，从而使机器人能够更多地协助清理这些空间高效。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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

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
影响因子：
0
作者：
Hunt WJ
通讯作者：
Hunt WJ

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
影响因子：
0
作者：
Tsitsimpelis I
通讯作者：
Tsitsimpelis I

Localising and identifying radionuclides via energy-resolved angular photon responses

DOI：
10.1016/j.nima.2023.168771
发表时间：
2023-10-20
期刊：
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
影响因子：
1.4
作者：
Tsitsimpelis，Ioannis;Alton，Tilly;Joyce，Malcolm J.
通讯作者：
Joyce，Malcolm J.