Mulit-scale semi autoomous teleoperation for passion remote handling

多尺度半自主远程操作，实现激情远程处理

• 批准号: 1932847
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-1932847
• 关键词: Mulit; scale; semi; autoomous; teleoperation

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

项目名称半自主机器人监督的信息表示总结这是与RACE合作的CASE学生资助（远程应用程序在存储环境中）（英国原子能管理局）。用于JET等聚变反应堆的远程监督维护机器人（欧洲联合圆环）目前需要许多专家操作员除了知道周围环境的状态之外还知道机器人的内部状态。为每个维护机器人培训和雇用多名专家的大量金钱和时间费用预计不会对拟议的聚变反应堆（如DEMO）进行扩展（DEMOnstration发电站），其中对维护机器人的需求（因此管理员）将大得多。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，同时还能够观察低级别信息，以在自主性失败时支持决策。提供对这些信息的不受约束的访问的接口可能会压倒人类管理员，因为与多机器人系统相关联的信息量是巨大且多样的。这些要求导致了关于界面内的信息显示应如何根据多机器人系统、环境和管理员的状态而改变的问题。研究问题和目标主要的研究问题是"沉浸式界面设计如何影响人类监督多个半自主机器人的性能和用户体验？本研究旨在探索沉浸式界面设计的新方法，这些方法将为多机器人系统的人类管理员提供更高的性能和/或减少工作量。方法与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（显示或隐藏信息的基础上过滤器）如何影响多个半自主地面机器人的遥操作员的感知工作量和性能。这项工作的新贡献是，这种信息过滤以前没有被评估为监督多个机器人。调查沉浸式多模态接口如何影响在监督多个地面操作机器人时的态势感知和感知工作量。这将比较一个纯粹的视觉沉浸式界面与视觉，声音和触觉反馈的界面。这是一个新的调查的影响，多模态沉浸式界面的背景下，多机器人监督的机器人在核环境中进行维护。研究如何使用机器学习来预测在监督多个地面操作机器人时向用户显示哪些信息最有用。调查这如何影响情景意识和感知工作量的主管在一个身临其境的界面。这是一个关于机器学习如何影响多机器人监管环境中监管者绩效的新研究。EPSRC研究领域被认为与本项目最相关，以下按相关性顺序列出（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

项目成果

Efficient Environment Guided Approach for Exploration of Complex Environments

用于探索复杂环境的高效环境引导方法

• DOI: 10.1109/iros40897.2019.8968563
• 发表时间: 2019
• 作者: Butters D