Mulit-scale semi autoomous teleoperation for passion remote handling

项目标题监管半自主机器人的信息表示摘要这是与英国原子能管理局(UKAEA)内的RACE(具有挑战性的环境中的远程应用)合作的一个案例学生。远程监管聚变反应堆的维护机器人，如JET(联合欧洲环)，目前需要许多专家操作员除了了解周围环境的状态外，还需要了解机器人的内部状态。为每个维护机器人培训和雇用多名专家的巨大金钱和时间成本预计不会扩展到拟议的聚变反应堆，如DEMO(示范电站)，那里对维护机器人的需求将会更大。在使用自主机器人进行远程维护操作期间，用户必须具有一般的态势感知，同时还能够在自主失败时观察低级信息以支持决策。提供不受限制地访问这些信息的界面可能会让人类主管不堪重负，因为与多机器人系统相关的信息量很大且多样化。这些要求导致了界面内的信息显示应该如何根据多机器人系统的状态、环境和监督者而改变的问题。研究问题和目的本研究的主要问题是：沉浸式界面设计如何影响人类管理多个半自主机器人的性能和用户体验？本研究旨在探索新的沉浸式界面设计方法，以适应多机器人系统中人类监管者更高的性能和/或更少的工作量。方法和新颖性本研究旨在通过实验回答三个主要问题。不同类型的信息过滤(基于过滤器显示或隐藏信息)如何影响多个半自主地面机器人的遥操作器的感知工作量和性能。这项工作的新贡献在于，这种信息过滤以前没有被评估过用于监督多个机器人。研究当监控多个地面操作机器人时，沉浸式多模式界面如何影响情景感知和感知工作量。这将把纯视觉沉浸式界面与具有视觉、声音和触觉反馈的界面进行比较。这是一项新的研究，在多机器人监督的背景下，对在核环境中执行维护的机器人的多模式沉浸式界面的影响进行了研究。研究在监督多个地面操作机器人时，如何使用机器学习来预测向用户显示哪些信息最有用。调查这对身临其境界面中主管的情景感知和感知工作负荷有何影响。这是一项关于机器学习如何在多机器人监督的背景下影响主管绩效的新调查。EPSRC被认为与该项目最相关的研究领域如下(1为最相关)：1.人机交互2.机器人3.图形和可视化4.英国磁聚变研究计划

Project TitleInformation Representation for Supervision of Semi-Autonomous RobotsSummaryThis is a CASE Studentship in collaboration with RACE (Remote Applications in Challenging Environments) within UKAEA (UK Atomic Energy Authority).Remotely supervising maintenance robots for fusion reactors such as JET (Joint European Torus) currently requires many expert operators to have knowledge of the internal state of the robots in addition to the state of the surrounding environment. The large monetary and temporal expense of training and employing multiple experts for each maintenance robot are not expected to be scalable for proposed fusion reactors such as DEMO (DEMOnstration Power Station), where the demand for maintenance robotics (and therefore supervisors) will be much greater.During remote maintenance operations with autonomous robots, users must have general situational awareness while also being able to observe low level information to support decision making when autonomy fails. An interface that provides unfettered access to this information is likely to overwhelm a human supervisor, as the amount of information associated with a multi-robot system is large and diverse. These requirements lead to questions about how the display of information within an interface should change based on the state of the multi robot system, the environment and the supervisor. Research Questions and ObjectivesThe primary research question is "How does immersive interface design affect the performance and user experience of a human supervising multiple semi-autonomous robots?".This research aims to explore novel approaches to immersive interface design which will accommodate greater performance and/or reduced workload for human supervisors of multi-robot systems. Approach and NoveltyThis research aims to answer three main questions through experimentation.1. How different types of information filtering (displaying or hiding information based on a filter) affect the perceived workload and performance of a teleoperator of multiple semi-autonomous ground robots. The novel contribution of this work is that information filtering of this kind has not previously been evaluated for supervising multiple robots.2. Investigate how immersive multi-modal interfaces can affect situational awareness and perceived workload when supervising multiple ground-based manipulation robots. This will compare a purely visual immersive interface to an interface with visual, sound and haptic feedback. This is a novel investigation into the effects of a multi-modal immersive interface in the context of multi-robot supervision for robots performing maintenance in a Nuclear environment.3. Investigate how machine learning can be used to predict which information is most useful to be displayed to a user when supervising multiple ground-based manipulation robots. Investigate how this affects the situational awareness and perceived workload of the supervisor within an immersive interface. This is a novel investigation into how machine learning can affect the performance of a supervisor in the context of multi-robot supervision.EPSRC Research Areas that are believed to be most relevant to this project are listed here in order of relevance (1 being most relevant):1. Human-computer interaction2. Robotics3. Graphics and visualisation4. UK Magnetic Fusion Research Programme