Blended Intelligence for Safe and Efficient Nuclear Sort & Segmentation

混合智能实现安全高效的核排序

• 批准号: 99534
• 金额: $ 7.65万
• 依托单位: VEOLIA NUCLEAR SOLUTIONS (UK), LIMITED
• 依托单位国家: 英国
• 项目类别: Small Business Research Initiative
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=99534
• 关键词: Blended; Intelligence; Safe; Efficient; Nuclear

Veolia Nuclear Solutions' consortium brings together proven technologies and deep domain expertise from leading nuclear, agritech., space and artificial intelligence practitioners to meet the challenge of nuclear waste sort and segmentation. Our vision is a system that uses a series of tele-manipulators, analogous to a human's arms, that work together with advanced characterisation and tracking technologies to identify, classify, pick up, transfer, trace and package nuclear waste.Operation of the manipulator arms can be under the control of computer intelligence and operate autonomously, or for more complex grabs and tangled wastes, call on the expertise of a skilled operator to take over the task. By sharing the task of the sorting and segmentation of highly heterogeneous wastes between human and computer intelligence, we aim to offer a step change in the safety and efficiency of the current process; only limited intervention by skilled operators will be needed for complex tasks.Our modular approach is focused around building the right intelligence and decision making frameworks to decide how to manage and classify the waste, and the right waste manipulation capability to separate the waste and get it where it needs to go. Input sensors for characterisation, and configuration of the manipulator platform, will be designed to be easily changed to suit the waste being sorted making the approach transferable across waste streams and sorting locations.The technology looks to enhance operator capability with autonomous operation whilst removing them from the hazardous environment. Efficiency scales as the number of manipulator units deployed per operator. Importantly this builds in flexibility for waste practitioners to determine the right mix of human/autonomous capability by stream and improve with time as the system and operators learn.Leading the project is Veolia Nuclear Solutions UK (VNS), an expert in nuclear robotics and developer of DEXTER TM. VNS will be supported by the University of Lincoln providing automation and force guidance technology, Faculty providing the artificial intelligence for waste tracking, packing and physico-chemical characterisation, and Createc, a specialist in radiological detection equipment and characterisation.

威立雅核能解决方案联盟汇集了来自领先的核能、农业技术、空间和人工智能从业者，以满足核废物分类和分割的挑战。我们的设想是一个系统，它使用一系列类似于人类手臂的远程机械手，与先进的特征和跟踪技术一起工作，以识别，分类，拾取，转移，跟踪和包装核废料。机械手手臂的操作可以在计算机智能的控制下自主操作，或者对于更复杂的抓取和缠结的废物，请一位熟练的操作员来接管这项任务。通过在人类和计算机智能之间共享高度异质废物的分类和分割任务，我们的目标是在当前过程的安全性和效率方面提供一个台阶式的变化;对于复杂的任务，只需要熟练的操作员进行有限的干预。我们的模块化方法专注于建立正确的智能和决策框架，以决定如何管理和分类废物，以及正确的废物处理能力来分离废物并将其送到需要的地方。用于表征的输入传感器和机械手平台的配置将被设计为易于更改，以适应被分类的废物，从而使该方法可在废物流和分类位置之间转移。该技术旨在提高操作员的自主操作能力，同时将其从危险环境中移除。效率与每个操作员部署的操纵器单元的数量成比例。重要的是，这为废物处理从业者提供了灵活性，可以通过流确定人类/自主能力的正确组合，并随着系统和操作员的学习而不断改进。领导该项目的是威立雅核解决方案英国（VNS），核机器人专家和DEXTER TM的开发者。VNS将得到林肯大学的支持，该大学提供自动化和力引导技术，学院提供废物跟踪，包装和物理化学表征的人工智能，以及放射性检测设备和表征专家Createc。