Best Viewpoints for External Robots or Sensors Assisting Other Robots

外部机器人或传感器协助其他机器人的最佳视角

• 批准号: 1945105
• 负责人: Robin Murphy
• 金额: $ 20万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945105&HistoricalAwards=false
• 关键词: Best; Viewpoints; External; Robots; Sensors

Bomb squads, SWAT teams, nuclear workers, and first responders typically use two robots to complete a single task: The primary robot performs the task but the operators use a second robot to get a better view of what the primary robot is doing. It is hard for the two sets of operators to coordinate the robots. But the biggest problem is not manual coordination, it is that operators often do not place the secondary robot where it provides the best viewpoint for increasing performance. While studies starting in 2001 have consistently shown that operators do not pick optimal viewpoints, there has been no formal theory of what is good for different types of tasks. This project will create the formal theory using perceptual psychology and apply it to an Endeavor Packbot 510 mobile robot that has been modified to carry a tethered Fotokite unmanned aerial vehicle (UAV). Using the formal theory, the Packbot will be able to perform a remote task while the UAV autonomously moves and maintains the optimal viewpoint given the clutter in the environment. This will reduce the manpower, inefficiency, and time it takes for robots to accomplish tasks in all domains, from public safety to manufacturing. The theory can also be applied to placing external sensors or cameras where they will be most helpful in controlling telecommuting robots, construction robots, or space robotics.This project will provide a fundamental, principled understanding of the perception needed to reduce the cognitive demands on robot operators, thereby increasing productivity while reducing costly errors. The model will allow ground robots, aerial robots, or external cameras to autonomously position themselves to give the best viewpoint for the current task. The project's approach is to use cognitive science concept of affordances, where the potential for an action can be directly perceived without knowing intent or models, and thus is universal to all robots and tasks. The project will learn from expert robot operators the value of viewpoints for four different affordances (passability, reachability, manipulability, traversability) as they use a computer simulator developed under previous NSF funding to perform tasks. The project will then use machine learning to cluster the performance into a set of equivalent manifolds representing the relative value of viewpoints in that region for an affordance. These rankings will be used by a risk-based planner, also developed under previous NSF funding, to move the external robot to the best view for the operator, given the risk of the path to the view. The resulting theory will be implemented on a Packbot ground robot (primary) and carrying a tethered Fotokite (secondary) aerial assistant for two tasks: a door opening and traversal test capturing the most common bottleneck for any indoor navigation application and a sensor insertion task duplicating an especially difficult mission at Fukushima. The project will enable robots to be more useful during disasters and public safety incidents, accelerate the safe decommissioning of the Fukushima Daiichi facility, and aid with NASA and NIH missions for assistive robots.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

拆弹小组、特警队、核工作人员和急救人员通常使用两个机器人来完成一项任务：主机器人执行任务，但操作员使用第二个机器人来更好地了解主机器人正在做什么。两组操作员很难协调机器人。但最大的问题不是手动协调，而是操作员通常不会将辅助机器人放置在它提供最佳视角以提高性能的地方。 虽然从2001年开始的研究一直表明，操作员不会选择最佳视角，但对于不同类型的任务，没有正式的理论。这个项目将使用感知心理学创建正式的理论，并将其应用于奋进Packbot 510移动的机器人，该机器人已被修改为携带系留Fotokite无人机（UAV）。 使用正式的理论，Packbot将能够执行远程任务，而无人机自主移动，并在环境中的混乱情况下保持最佳视角。这将减少机器人完成从公共安全到制造业等所有领域任务所需的人力、效率和时间。该理论还可以应用于将外部传感器或摄像头放置在最有助于控制远程办公机器人、建筑机器人或空间机器人的地方。该项目将提供对感知的基本原理性理解，以减少对机器人操作员的认知需求，从而提高生产率，同时减少代价高昂的错误。该模型将允许地面机器人、空中机器人或外部摄像机自主定位，为当前任务提供最佳视角。该项目的方法是使用认知科学的启示概念，其中可以直接感知动作的潜力，而无需知道意图或模型，因此对所有机器人和任务都是通用的。 该项目将从专家机器人操作员那里学习四种不同启示（可通过性，可达性，可操作性，可穿越性）的观点的价值，因为他们使用在以前的NSF资助下开发的计算机模拟器来执行任务。然后，该项目将使用机器学习将性能聚类到一组等效流形中，这些流形表示该区域中视角的相对价值，以获得启示。这些排名将由基于风险的规划器使用，该规划器也是在之前的NSF资助下开发的，以将外部机器人移动到操作员的最佳视图，考虑到视图路径的风险。由此产生的理论将实施Packbot地面机器人（主要），并进行拴Fotokite（次要）空中助理两项任务：门打开和遍历测试捕捉任何室内导航应用程序和传感器插入任务的最常见的瓶颈复制一个特别困难的使命在福岛。该项目将使机器人在灾难和公共安全事件中发挥更大的作用，加速福岛第一核电站的安全退役，并协助NASA和NIH的辅助机器人任务。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Best and Worst External Viewpoints for Teleoperation Visual Assistance

远程操作视觉辅助的最佳和最差外部视角

• 发表时间: 2021
• 期刊: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Shilleh, Mahmood M.;Amer, Qusai A.;Dufek, Jan;Murphy, Robin R.
• 通讯作者: Murphy, Robin R.

Autonomous Visual Assistance for Robot Operations Using a Tethered UAV

使用系留无人机为机器人操作提供自主视觉辅助

• DOI: 10.1007/978-981-15-9460-1_2
• 发表时间: 2021
• 期刊: Field and Service Robotics. Springer Proceedings in Advanced Robotics
• 作者: Xiao, X.;Dufek, J.;Murphy, R.R.
• 通讯作者: Murphy, R.R.

Best Viewpoints for External Robots or Sensors Assisting Other Robots

• DOI: 10.1109/thms.2021.3090765
• 发表时间: 2021-08-01
• 期刊: IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
• 影响因子: 3.6
• 作者: Dufek, Jan;Xiao, Xues;Murphy, Robin R.
• 通讯作者: Murphy, Robin R.