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

放射性在我们周围无处不在，但它通常是分散的，因此对人类健康几乎没有危险。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有关的工业活动造成了大量严重污染的设施。由于辐射水平太高，污染程度可能会非常严重，以至于人们无法进入。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，以将可能存在的任何风险降至最低。事情变得更加复杂，因为进入这些设施的困难使我们无法准确地了解需要做些什么来确保这些地方的安全。其中一些设施并不安全，因为它们太旧了，而且设计不会持续这么长时间。重要的是现在确保它们的安全，以确保放射性物质不会泄漏出去，因为这需要的时间越长，随着新问题的出现，它就变得越困难和昂贵。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年的时间。这意味着，如果现在不能有效地处理这些问题，这些问题将落到子孙后代身上；因此，从伦理的角度来看，当务之急是现在就用行动来防止这种情况。了解这些辐射危害的方法之一是派遣一个机器人。由于最近的研究，在这方面已经取得了很大的进展，部分是由领导这项提议的人所做的。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探测到最多的辐射是行不通的，原因有两个：首先，这些地方的放射性活动往往是分散的，这意味着它不会集中在一个可能容易和快速处理的地方。相反，污染产生于容器中的泄漏、飞溅和潮汐痕迹，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了困难，因为它们往往从一个特定的角度提供评估，可能没有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。这些都会导致辐射的散射和吸收。这会影响“画面”，也会影响人们认为存在多少放射性物质。有人在环路中--在有污染的空间里--他们可以即兴行动，移动到不同的有利位置，将碎片移开，并根据他们看到的推断所涉及的内容。这是不可能的，使用商业机器人平台构成了一种可能复制这一点的方式。例如，通过从多个互补的优势点进行评估，并融合从各种不同角度获得的数据。然而，重要的是通过驾驶机器人来保持人类对这些操作的监督，而不是在回收机器人等方面出现困难的情况下给予它完全的自主性。这提出了一个问题：我们如何从各种角度，从被分散的放射性污染的混乱空间解读机器人获得的信息，以帮助我们快速有效地了解可能存在的危险？我们的研究直接呼吁这一要求：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的像素组成的。通过推进我们对这些因素对由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东西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协助清理这些空间。

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.