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Overwatch: An Educational Testbed for Multi-Robot Experimentation

机译:守望先锋:多机器人实验的教育测试平台

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Educators who wish to engage their students in multi-agent experimentation and learning need an inexpensive multi-robot system that leverages existing equipment and open-source software. This paper proposes Over-watch as an inexpensive educational tool for teaching and experimenting in multi-robot systems. The interaction of multiple agents within a single environment is an important area of study. It is vital that agents within the environment perceive other agents as intelligent, acting within the environment as cooperative teammates or as competitive members of another team. To do so, the system must meet three goals: first, to allow multiple robots to communicate and coordinate; second, to localize within a shared global coordinate system; third, to recognize their teammates and other teams. The cost and scale of such experimental platforms places them outside the reach of many educational institutions or limits the number of agents that are interacting within the system (Liu and Winfield 2011). The goal of Over-watch is to create an experimental platform for multi-agent systems that is comprised of much smaller, albeit less capable, robots, many of which are prevalent in academic institutions already. Making use of available open-source libraries and utilizing lower cost robots, such as Scribblers, allows for experiments with many agents. This enables Overwatch to fit into the budget limitations of an academic setting. The Overwatch platform provides the Scribblers with global localization capabilities. This paper presents the system in detail and includes experiments to show its ability to localize, interact with other agents, and coordinate behaviors with these other agents. Additionally, the details to setup this system are also included.
机译:希望让学生参与多主体实验和学习的教育者需要一种廉价的多机器人系统,该系统可以利用现有设备和开源软件。本文提出了“守望先锋”作为在多机器人系统中进行教学和实验的廉价教育工具。在单个环境中多个代理的交互是一个重要的研究领域。至关重要的是,环境中的代理必须将其他代理视为聪明的,在环境中以合作伙伴或其他团队的竞争成员的身份行事。为此,系统必须满足三个目标:首先,允许多个机器人进行通信和协调;第二,在共享的全局坐标系中定位;第三,认识他们的队友和其他团队。这种实验平台的成本和规模使它们无法被许多教育机构所接受,或者限制了系统内进行交互的代理的数量(Liu和Winfield 2011)。监视的目标是为多智能体系统创建一个实验平台,该平台由体积更小但功能不强的机器人组成,其中许多机器人已经在学术机构中盛行。利用可用的开源库并利用成本较低的机器人(例如Scribblers)可以进行许多代理的实验。这使《守望先锋》能够适应学术环境的预算限制。守望先锋平台为Scribblers提供了全球本地化功能。本文详细介绍了该系统,并进行了实验以显示其定位,与其他代理交互以及与其他代理协调行为的能力。此外,还包括设置此系统的详细信息。

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