Best Viewpoints for External Robots or Sensors Assisting Other Robots

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：主要机器人执行任务，但操作员使用第二个机器人来更好地了解主要机器人正在做什么。这两组操作员很难协调机器人。但最大的问题不是人工协调，而是操作员往往没有将次级机器人放置在它提供提高性能的最佳视角的地方。虽然从2001年开始的研究一直表明，操作员不会选择最佳观点，但对于不同类型的任务，什么是好的，还没有正式的理论。该项目将使用感知心理学创建形式理论，并将其应用于Endeavor Packbot 510移动机器人，该机器人已被改造为携带绳索Fotokite无人机(UAV)。使用形式理论，Packbot将能够执行远程任务，而无人机在杂乱的环境中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的任务所需的人力、效率和时间。该理论还可以应用于放置外部传感器或摄像头，在它们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。这个项目将提供对感知的基本、原则性的理解，以减少对机器人操作员的认知要求，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负担概念，即可以在不知道意图或模型的情况下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。该项目将从专业机器人操作员那里学习四种不同负担(通过性、可达性、可操纵性、可穿透性)的观点的价值，因为他们使用的是以前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集到一组等价的流形中，表示该区域中的视点对于启示的相对价值。这些排名将由一个基于风险的规划者使用，该规划者也是在之前的NSF资助下开发的，以考虑到通往该视图的路径的风险，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)上实施，并携带一个拴系的Fotokite(次要)空中助手执行两项任务：一是门打开和穿越测试，捕捉任何室内导航应用程序最常见的瓶颈；二是传感器插入任务，复制福岛特别困难的任务。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，加快福岛第一核电站的安全退役，并为NASA和NIH的辅助机器人任务提供帮助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.