Robust remote sensing for multi-modal characterisation in nuclear and other extreme environments

该项目通过部署和组合来自各种不同遥感模式的信息，解决极端环境(EE)的“特征”问题。我们的主要应用领域是核退役，但我们的研究成果将与其他EE相关。在进行核退役干预之前，被退役的设施/工厂必须具有“特性”，以了解：物理布局和3D几何形状；结构完整性；包括特定感兴趣对象(例如，燃料棒碎片)的内容。3D工厂模型必须进一步使用其他传感数据进行注释：热信息；污染的类型/水平/位置(辐射、化学等)。可能需要在POCO(清理行动后)之前、期间或之后进行表征。“静止的建筑物”可能有超过半个世纪的历史，内部布局和内容不确定。干燥环境(例如受污染的混凝土“洞穴”)和潮湿环境(例如遗留的储水池)需要具有这种特征。洞穴可能没有灯光，导致机器人安装的聚光灯出现困难的视觉问题(阴影、对比度、饱和度)。水下环境会导致RGB相机的能见度显著下降，并使大多数深度/距离传感器无法使用。新技术，如声学摄像机，在开发处理这些新型图像数据的算法方面提出了有趣的新挑战。在许多情况下，需要机器人将远程传感器部署到极端环境中，并将它们移动到期望的位置和查看姿势。在某些情况下，机器人还必须通过检索受污染材料的样本来帮助确定特征。在许多情况下，还必须应用实时遥感数据来通知和控制机器人的行动，同时在欧洲执行远程干预任务。该项目汇集了横跨三大洲的一支独特的、跨学科的国际研究人员和研究所团队，以应对这些挑战。最终用户NNL和日本宇宙航空研究开发机构将就核环境中遥感的情景和挑战提出建议。喷气推进实验室的现役设施将用于测量传感器、芯片和软件在各种辐射类型和剂量下的退化。喷气推进实验室和埃塞克斯大学的研究人员将利用这些数据来开发预测这种退化的新模型。埃塞克斯大学的研究人员将开发新的软件和嵌入式硬件设计方法，通过采用新的故障检测、容错和恢复方法来克服辐射损害。合作伙伴提供的情景和喷气推进实验室测量的退化数据将被用于开发新的基准数据集，包括来自多种传感模式(RGB相机、深度/距离相机、红外热成像、水声成像)的数据，具有各种核场景和物体的特征。伦敦大学和埃塞克斯大学的研究人员将开发新的算法，用于场景的实时3D表征，以及多种传感模式的智能和自适应融合。首先，将开发新的多传感器融合方法，用于三维建模、语义/元数据标记、场景和对象的识别和理解。其次，这些方法将被扩展到包括新的算法，以克服图像和传感器数据中的极端噪声和其他类型的退化。第三，我们将开发所需的机器人和机器人控制方法：i)在极端环境中部署远程传感器；ii)利用远程传感器数据来指导机器人在这些环境中的干预和行动。最后，我们将对这些新技术进行试验性部署。由埃塞克斯公司开发的强大的硬件和软件解决方案将在喷气推进实验室的主动辐射环境中进行测试。我们还将在NNL Workington和日本的Naraha Fukushima模拟测试设施将传感器有效载荷部署到不活跃但具有工厂代表性的核环境中进行试验性机器人部署。

This project addresses the problem of "characterisation" of Extreme Environments (EE), by deploying and combining information from a variety of different Remote Sensing modalities. Our principle application area is nuclear decommissioning, however our research outputs will be relevant to other EE.Before nuclear decommissioning interventions can happen, the facility/plant being decommissioned must be "characterised", to understand: physical layout and 3D geometry; structural integrity; contents including particular objects of interest (e.g. fuel rod debris). 3D plant models must further be annotated with additional sensed data: thermal information; types/levels/locations of contamination (radiological, chemical etc.). Characterisation may be needed before, during or after POCO (Post Operation Clean Out). "Quiescent buildings" may be over half a century old, with uncertain internal layout and contents.Characterisation is needed in dry environments (e.g. contaminated concrete "caves") and wet environments (e.g. legacy storage ponds). Caves may be unlit, causing difficult vision problems (shadows, contrast, saturation) with robot-mounted spotlights. Underwater environments cause significant visibility degradation for RGB cameras, and render most depth/range sensors unusable. New technologies, e.g. acoustic cameras, engender interesting new challenges in developing algorithms to process these new kinds of image data.In many cases, robots are needed to deploy Remote Sensors into Extreme Environments and move them to desired locations and viewing poses. In some cases, robots must also assist characterisation by retrieving samples of contaminated materials. In many case real-time Remote Sensing data must also be applied to inform and control the actions of robots, while performing remote intervention tasks in EE.This project brings together a unique, cross-disciplinary and international team of researchers and institutes, spanning three continents, to address these challenges. End-users NNL and JAEA will advise on scenarios and challenges for Remote Sensing in nuclear environments. Active facilities at JPL will be used to measure degradation of sensors, chips and software under a variety of radiation types and doses. JPL and Essex researchers will use this data to develop new models for predicting such degradation. Essex researchers will then develop new methods for software and embedded hardware design, which overcome radiation damage by incorporating new approaches to fault detection, tolerance and recovery.The scenarios provided by the partners, and the degradation data measured by JPL, will be used to develop new benchmark data-sets comprising data from multiple sensing modalities (RGB cameras, depth/range cameras, IR thermal imaging, underwater acoustic imaging), featuring a vairiety of nuclear scenes and objects.UoB and Essex researchers will develop new algorithms for real-time 3D characterisation of scenes, with intelligent and adaptive fusion of multiple sensing modalities. First, new multi-sensor fusion methods will be developed for 3D modelling, semantic/meta-data labelling, recognition and understanding of scenes and objects. Second, these methods will be extended to incorporate new algorithms for overcoming extreme noise and other kinds of degradation in images and sensor data. Third, we will develop the robots and robot control methods needed to: i) deploy remote sensors into extreme environments; ii) exploit remote sensor data to guide robotic interventions and actions in these environments.Finally, we will carry out experimental deployments of these new technologies. Robust hardware and software solutions, developed by Essex, will be tested in active radiation environments at JPL. We will also carry out experimental robotic deployments of sensor payloads into inactive but plant-representative nuclear environments at NNL Workington and the Naraha Fukushima mock-up testing facilities in Japan.