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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611175
• 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开发自主主动观察者的问题：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各个方面，其中希望其活动被记录的目标代理被一个或多个文档管理员跟随（或之前），它试图与之合作，但没有明确的通信或明确的共享状态。观察或记录世界上的事件对于我们理解我们的环境和管理环境至关重要，无论是发电厂的一系列事件，生物学家在正在评估的生态系统中的相互作用，工作中的自动化系统的活动，还是重大事件期间警察的活动。 这项资助将通过建立计算模型来解决这个理论前沿的问题，这些模型解释了在什么情况下我们可以计算可以计划的动作，以允许机器人遵循与另一个移动代理相关的事件序列，甚至允许使用多个观察者和多个目标。这一理论的发展将与机器人系统的生产相结合，这些机器人系统采用这一理论，使用视频输入数据来记录其他机器人、人和/或他们的环境，从而在真实的时间内操作。 随着机器人技术和机器传感技术的成熟，机器人之间以及机器人与环境之间的交互研究已经成为实际应用的关键。我的研究计划解决多智能体合作互动，特别是在信息寻求任务的背景下。这些可以被非正式地设想为支持智能观察者发展的原则的创建，或者更通俗地说是一类“机器人视频纪录片”。也就是说，开发能够记录世界上不断演变的事件的自动化系统。为了完全完成这类任务，机器人必须制定一个计划，通过预测动态变化的主体、障碍物和遮挡物的运动，在正确的时间将其置于正确的Vantage。 在过去的6年里，我的实验室在管理机器人系统的理论约束以及行走，游泳和飞行的机器人系统的实验开发和评估方面取得了进展，这些机器人系统具有室内或室外操作的变体，甚至在这些不同的领域相互作用。这项工作在过去的6年中已经产生了大约50篇参考出版物，并且这个基础使我能够在理论，仿真和实践中获得解决多机器人交互问题的经验。 为了解决开发机器人纪录片的问题，我将开始通过开发算法来解决更大问题的三个相互关联的方面：确定观察什么视觉事件以及何时观察，计算最佳运动计划以允许间歇性观察这些视觉事件而无需明确的通信，以及计算恢复视觉接触的计划，如果代理人已经分离。然后，这些算法阶段中的每一个都将在功能实验室系统中找到实施例，其中它们在实践中的性能可以在受控环境中进行评估，并与理论预测和界限进行比较。