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

用于核和其他极端环境中多模态表征的鲁棒遥感

• 批准号: EP/P017487/1
• 负责人: Rustam Stolkin
• 金额: $ 178.14万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP017487%2F1
• 关键词: Robust; remote; sensing; multi; modal

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.

该项目通过部署和组合来自各种不同遥感模式的信息，解决极端环境的“特征化”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。在核退役干预措施发生之前，正在退役的设施/工厂必须“表征”，以了解：物理布局和3D几何形状;结构完整性;内容，包括感兴趣的特定对象（例如燃料棒碎片）。3D工厂模型必须进一步标注额外的传感数据：热信息;污染的类型/水平/位置（放射性，化学等）。可能需要在POCO（操作后清洁）之前、期间或之后进行表征。“安静的建筑物”可能有超过半个世纪的历史，内部布局和内容不确定。需要在干燥环境（如受污染的混凝土“洞穴”）和潮湿环境（如遗留的储存池塘）中进行表征。洞穴可能没有照明，导致视觉困难的问题（阴影，对比度，饱和度）与机器人安装的聚光灯。水下环境会导致RGB相机的可见度显著下降，并使大多数深度/距离传感器无法使用。新技术，例如声学相机，在开发算法来处理这些新类型的图像数据方面带来了有趣的新挑战。在许多情况下，需要机器人将遥感器部署到极端环境中，并将其移动到所需的位置和查看姿势。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协助表征。在许多情况下，实时遥感数据还必须应用于通知和控制机器人的行动，同时在EE中执行远程干预任务。该项目汇集了一个独特的，跨学科的国际研究人员和机构团队，跨越三大洲，以应对这些挑战。最终用户NNL和JAEA将就核环境中遥感的情景和挑战提供建议。喷气推进实验室的活动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的研究人员将利用这些数据开发新的模型来预测这种退化。然后，埃塞克斯大学的研究人员将开发软件和嵌入式硬件设计的新方法，通过结合新的故障检测、容错和恢复方法来克服辐射损伤。合作伙伴提供的场景和喷气推进实验室测量的退化数据将用于开发新的基准数据集，该数据集包括来自多种传感模式的数据（RGB相机、深度/距离相机、红外热成像、水下声学成像），以各种核场景和物体为特色。不列颠哥伦比亚大学和埃塞克斯大学的研究人员将开发用于场景实时3D特征化的新算法，具有多种感测模态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/元数据标签、识别和理解场景和物体。第二，这些方法将被扩展，以纳入新的算法，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）将遥感器部署到极端环境中; ii）利用遥感器数据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实验部署。由埃塞克斯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沃金顿和日本奈良福岛模拟试验设施的非活动但具有工厂代表性的核环境中进行传感器有效载荷的实验性机器人部署。

项目成果

An enhanced AODV protocol for external communication in self-driving vehicles

• DOI: 10.1109/est.2017.8090420
• 发表时间: 2017-09
• 期刊: 2017 Seventh International Conference on Emerging Security Technologies (EST)
• 作者: K. Alheeti;K. Mcdonald-Maier

Haptic-guided assisted telemanipulation approach for grasping desired objects from heaps

用于从堆中抓取所需物体的触觉引导辅助远程操作方法

• DOI: 10.48550/arxiv.2307.07053
• 发表时间: 2023
• 作者: Adjigble M

Local Region-to-Region Mapping-based Approach to Classify Articulated Objects

基于局部区域到区域映射的铰接物体分类方法

• DOI: 10.1109/crv60082.2023.00030
• 发表时间: 2023
• 作者: Aggarwal A

Proxy Circuits for Fault-Tolerant Primitive Interfacing in Reconfigurable Devices Targeting Extreme Environments

针对极端环境的可重构设备中容错原语接口的代理电路

• DOI: 10.1109/iscas45731.2020.9181282
• 发表时间: 2020
• 作者: Adetomi A

Unsupervised learning-based approach for detecting 3D edges in depth maps.

• DOI: 10.1038/s41598-023-50899-3
• 发表时间: 2024-01-08
• 期刊: Scientific reports
• 影响因子: 4.6