Robotic teamwork and collaboration

机器人团队合作

• 批准号: RGPIN-2014-04674
• 负责人: Dudek, Gregory
• 金额: $ 3.93万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566668
• 关键词: Robotic; teamwork; collaboration

In the short term, I seek to develop algorithms to address problem of developing autonomous active observers: systems that can automatically select and collect information regarding the world around them. I will do this big focussing, in particular, on aspects of this problem where a target agent that wants its activities to be documented is being followed (or preceded) by one or more documentarians with which it seeks to cooperate, but without explicit communication or explicitly shared state. The observation or recording of events in the world is critical to our understanding of our environment and our management of it, be it a sequence of events in a power plant, the interactions of a biologist in an ecosystem that in being evaluated, the activities of an automated system at work, or the activities of the police for during a major event. This grant will address this problem on the theoretical front by building computational models that explain under what circumstances we can compute actions can be planned to allow a robot to follow a sequence of events associated with another moving agent, even allowing the use of multiple observers and multiple targets. This theoretical development will be coupled with the production of robotic systems that employ this theory to operate in real time using video input data to document other robots, people and/or their environment. As robotics and machine sensing have started to mature, the study of interactions between robots, and between robots and their environment has become the lynchpin of practical applications. My research program addresses multi- agent cooperative interaction specifically in the context of information-seeking tasks. These can be informally envisioned as the creation of principles supporting the development of intelligent observers, or more colloquially a class of “robotic video documentarians.” That is, the development of automated systems that can document evolving events in the world. To fully accomplish this kind of task, a robot must develop a plan that gets it in the right vantage points at the right times by predicting the movement of the dynamically changing subject(s), obstacles and occlusions. In the last 6 years my lab has made progress on both the theoretical constraints that govern robotic systems, as well as the experimental development and evaluation of robotic systems that walk, swim, and fly, with variants that operate indoors or outdoors, and even interact with one another across these disparate domains. This work has resulted in the last 6 years in roughly 50 refereed publications, and that base has allowed me to acquire experience solving multi-robot interaction problems in both theory, i simulation and in practice. To address the problem of developing robotic documentarians, I will begin by developing algorithms to address three inter-related aspects of the larger problem: determination of what visual events to observe and when, computation of optimal motion plans to allow intermittent observation of these visual events without explicit communication, and computing plans for the recovery of visual contact if the agents have become separated. Each of these algorithmic stages will then find embodiment in functioning laboratory systems where their performance in practice can be evaluated in controlled circumstances and compared with theoretical predictions and bounds.

在短期内，我试图开发算法来解决开发自主活动观察者的问题：可以自动选择和收集有关周围世界的信息的系统。我将特别关注这个问题的方面，在这个问题的方面，希望将其活动记录在（或之前）的目标代理人都会遵循其寻求协调但没有明确交流或明确共享状态的一位或多个纪录片。对世界上事件的观察或记录对于我们对环境的理解和对环境的管理至关重要，无论是发电厂中的一系列事件，正在评估的生态系统中生物学家的互动，工作中自动化系统的活动还是警察在重大事件中的活动。该赠款将通过构建计算模型来解决理论方面的这个问题，这些计算模型可以在哪些情况下可以计划采取行动，以允许机器人遵循与另一个移动代理相关的一系列事件，甚至允许使用多个观察者和多个目标。这种理论发展将与机器人系统的生产相结合，这些机器人系统采用该理论使用视频输入数据来实时运行，以记录其他机器人，人员和/或其环境。随着机器人技术和机器的感知开始成熟，机器人之间以及机器人与其环境之间的相互作用的研究。环境已成为实际应用的林奇宾。我的研究计划在信息寻求任务的背景下专门针对多机构合作互动。这些可以非正式地设想为建立支持智能观察家发展的原则，或者更俗称一类“机器人视频纪录片”。也就是说，可以记录世界上不断发展的事件的自动化系统的开发。为了完全完成此类任务，机器人必须通过预测动态变化的主题，障碍和遮挡的运动来制定一个计划，该计划在正确的时代正确的有利位置。在过去的六年中，我的实验室在控制机器人系统的理论约束方面取得了进步，以及对行走，游泳和飞行的机器人系统的实验开发和评估，并以在室内或室外运行的变体，甚至在这些不同领域相互作用。这项工作已导致了过去6年的大约50个推荐出版物，这一工作使我能够在理论，I模拟和实践中获得解决多机器人互动问题的经验。为了解决发展机器人纪录片的问题，我将首先开发算法以解决较大问题的三个相互关联的方面：确定要观察的视觉事件以及何时计算最佳运动计划，以允许无明确沟通的视觉事件进行间歇性观察，以及如果特工分开了视觉接触的恢复计划，则可以分间观察这些视觉事件。然后，这些算法阶段中的每一个都将在运行实验室系统中找到实施方案，在这些实验室系统中，可以在受控的情况下评估其在实践中的性能，并将其与理论预测和界限进行比较。