Robotics for carbon-neutral fusion-energy - Understanding, modelling, and predicting the human tele-robotics operator for intelligent shared control

碳中性聚变能源机器人 - 理解、建模和预测人类远程机器人操作员以实现智能共享控制

• 批准号: 2509290
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2509290
• 关键词: Robotics; carbon; neutral; fusion; energy

This project will investigate the use of the perception of dynamic decisions and actions of operators of telerobotic systems through body motion and physiological data analysis. The models obtained through this analysis will be used to understand and predict the operator with the aim to isolate operator failure and improve mutual trust between the operator and the tele-operation system. This may also result in the support of the operator through intelligent, shared control schemes. The major use case for this problem is the MASCOT tele-manipulator system of the UK Atomic Energy Authority (UKAEA). MASCOT is a two-armed master-slave robot established and proven through thousands of successful hours of remote handling operations in reconfiguration and repair tasks within the JET (Joint European Torus) fusion reactor. We propose a system for task prediction of the MASCOT Operators using body tracking devices. A machine learning approach for suitable decision and action modelling (e.g. using Markov models) will be deployed to classify and predict future tasks of the operator. In a second step, this project may look into the adaptation of haptic feedback. It is believed that this appropriate and task-specific feedback may promote safety and efficiency of MASCOT operators during remote handling tasks and reduce human errors by actively introducing augmented haptic effects and other actions for safety and efficiency of MASCOT. This PhD will contribute to the general understanding of human-robot interaction, introduce new results on human decision and action models in such processes and may contribute to principles of shared control and semi-autonomy.

本项目将研究通过身体运动和生理数据分析来感知远程机器人系统操作员的动态决策和行动。通过这种分析获得的模型将被用来理解和预测操作员，目的是隔离操作员故障，提高操作员和远程操作系统之间的相互信任。这也可以通过智能的、共享的控制方案得到运营商的支持。这个问题的主要用例是英国原子能管理局（UKAEA）的MASCOT远程机械手系统。MASCOT是一种双臂主从机器人，经过数千小时的成功远程操作，在JET（欧洲联合环）聚变反应堆内完成了重新配置和维修任务。我们提出了一个系统的任务预测的MASCOT运营商使用身体跟踪设备。将采用机器学习方法进行适当的决策和行动建模（例如使用马尔可夫模型），以分类和预测操作员的未来任务。在第二步中，该项目可能会研究触觉反馈的适应性。据信，这种适当的和特定于任务的反馈可以促进MASCOT操作者在远程处理任务期间的安全性和效率，并且通过主动引入增强的触觉效果和用于MASCOT的安全性和效率的其他动作来减少人为错误。该博士学位将有助于对人机交互的一般理解，介绍人类决策和行动模型在这些过程中的新成果，并可能有助于共享控制和半自主的原则。