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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587947
• 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，我寻求开发算法来解决自主活动的观察者，以使其周围的世界无需明确的沟通或明确共享状态，这是其周围的世界。为了理解我们的环境，无论是评估的电厂eing中的一系列事件，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添加进展模型，以解释我们可以在哪些情况下对机器人进行措施理论开发的生产机器人系统的生产，这些机器人系统使用视频输入数据在实际e中重新培训，以记录其他机器人，人员或其环境。 随着机器人和机器开始成熟，我的研究计划的机器人YNCHPIN之间的研究专门用于托运的多代理合作互动 - “可以记录世界上进化事件的自动化系统的开发。 在过去的6年中在大约有50份被指导的出版物中，我可以在理论，I模拟和实践中获得解决多机器人相互作用问题的经验。 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方面：要观察什么视觉事件，以及何时计算最佳运动计划，以允许间歇性地进行持续的持久，而无需探索交流，并且如果代理人分离，则可以在实践中进行实施的实施方案的恢复。