Mobile Autonomous Sort and Segregate System

安装在移动模块化系统上的机器人可以被部署来识别、分类和分离放射性废物，以便安全地回收或处置。机器人将面对大量的放射性废物，从金属到塑料、电气设备、土壤等等。为了对其进行处理，机器人将首先使用其视觉系统识别单个垃圾。机器人将识别一些垃圾，但除此之外，它可以由操作员训练，通过视觉识别新的垃圾类型。通过机器学习，视觉系统被使用得越多，这个过程就变得越自主。该系统亦会量度每件物品的重量、大小、形状、表面积和成分，以便进行有效的分类和包装，并结合视觉识别废物的辐射和化学特性，将废物分类。在视觉识别之后，每一件物品都被机械臂拿起，其放射性水平被监测并进行化学分析。有关物品的物理特性、材料类型和放射性水平的信息被用来将物品分类到正确的废物流中，以便安全回收或储存。这些信息的记录以及物品的图像将被存储为记录哪些物品已被放置到每个废物流中。该项目通过将人员从过程中删除来创新当前的最新技术。这意味着操作员在危险环境中工作的风险较小。在这项重复性的任务中，人为错误的风险较小。这一过程也将比人工系统更快、更便宜，为英国纳税人节省了退役多余核设备和设施的成本。将机器人手臂与视觉系统、机器学习、核和化学表征系统相结合，将标志着核退役的新发展。机械臂可以是任何型号和大小，以适应垃圾类型。另一项关键创新来自智能视觉系统，该系统通过机器学习自动识别不同形式的废物。最大限度地减少了人工交互，创建了一个高效的、最大限度地减少浪费的工作流程，该工作流程可以适应地点，通过各种可衡量的标准将垃圾分类，并将部署得越多就越好。因此，核退役产生的废物得到安全、快速和廉价的处理，最大限度地减少人与人的互动，有效地包装废物容器，并采用勤奋的回收过程。

A robot mounted on a mobile modular system that can be deployed to identify, sort and segregate radioactive waste for safe recycling or disposal.The robot will confront a mass of radioactive waste ranging from metals, to plastics, electrical equipment, soil and more. In order to process it, the robot will first identify an individual waste item using its vision system. The robot will recognise some waste, but otherwise it can be trained by an operative to identify a new waste type by sight. Through machine learning, the more the vision system is used, the more autonomous the process becomes. The system will also measure each item's weight, size, shape, surface area and composition for efficient sorting and packing.Visual recognition of waste is combined with radiometric and chemical characterisation to classify the waste for sorting. After visual identification, each item is picked up by the robotic arm and its level of radioactivity is monitored and it is chemically analysed. The information on the item's physical characteristics, material type and radioactivity level is used to sort the item into the correct waste-stream for safe recycling or storage.Records of this information, together with images of the items will be stored as a record of what items have been placed into each waste-stream.This project innovates on current state-of-the-art by removing the person from the process. This means that there is less risk to the operators from working in hazardous environments. There is less risk of human error in this repetitive task. The process will also be quicker and cheaper than a manual system, offering savings to the UK taxpayer on the cost of decommissioning redundant nuclear equipment and facilities.Combining a robot arm with vision systems, machine learning, nuclear and chemical characterisation systems will mark a new development for nuclear decommissioning. The robot arm can be of any model and size to suit the waste type. A further key innovation comes via the intelligent vision system, which automates the recognition of different forms of waste through machine learning. Human interaction is minimised, creating an efficient, waste-minimising workflow that can adapt to location, segregate waste by various measurable criteria, and will improve the more it is deployed. Waste generated by nuclear decommissioning is therefore dealt with safely, quickly and cheaply, with minimal human interaction, efficiently packing waste containers, and with a diligent recycling process.