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

基本信息

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

项目摘要

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台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，而是操作员通常没有将辅助机器人放置在提供提高性能的最佳视角的位置。虽然从 2001 年开始的研究一致表明，操作员不会选择最佳的观点，但目前还没有正式的理论来说明什么对不同类型的任务有利。该项目将利用感知心理学创建正式理论，并将其应用于 Endeavor Packbot 510 移动机器人，该机器人经过修改可携带系留的 Fotokite 无人机 (UAV)。使用形式理论，Packbot 将能够执行远程任务，同时无人机在环境混乱的情况下自主移动并保持最佳视角。这将减少机器人完成从公共安全到制造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机放置在最有助于控制远程办公机器人、建筑机器人或太空机器人的位置。该项目将为减少对机器人的认知需求所需的感知提供基本的、原则性的理解。操作员，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头自主定位，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可供性概念，在不知道意图或模型的情况下可以直接感知行动的潜力，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功能可供性（可通过性、可达性、可操作性、可遍历性）的视点价值，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为一组等效流形，代表该区域中观点的相对价值。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，考虑到视图路径的风险，将外部机器人移动到操作员的最佳视图。由此产生的理论将在 Packbot 地面机器人（主要）上实现，并携带系留的 Fotokite（辅助）空中助手来执行两项任务：开门和遍历测试，捕获任何室内导航应用最常见的瓶颈，以及复制传感器插入任务福岛的任务尤其艰巨。该项目将使机器人在灾难和公共安全事件中发挥更大作用，加速福岛第一核电站设施的安全退役，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，被认为值得支持通过使用基金会的智力优点和更广泛的影响审查标准进行评估。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

DOI：
发表时间：
2021-01
期刊：
Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
影响因子：
0
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