| 摘 要： |
Multi-robot systems are expected to underpin the future of automated infrastructure in various fields, including logistics and transportation. Despite the importance of multi-robot research, operating a large number of physical robots presents nontrivial challenges that exceed those encountered in simulation environments. These include communication saturation, the difficulties in real-time localization, detection of abnormal robot behavior, and the need for user-friendly interfaces to allow for effective state management. Collectively, they create a significant gap in the assumptions between lab-scale studies and deployable technologies in this field. Therefore, we developed a versatile testbed for multi-robot studies that can accommodate up to 100 differential-drive robots, called the 100-Mouse System. Our hallmark is scalability, while maintaining flexibility to allow researchers to test their ideas with actual robot fleets. This is a result of deliberately and integrally designed modules from both software and hardware perspectives, as well as a user interface, which is essential in large-scale experiments. As an application of this platform, we present a pattern formation task in a centralized manner, demonstrating the ability of the system to effectively accommodate large robot fleets.
多机器人系统有望在物流和交通等多个领域支撑自动化基础设施的未来发展。尽管多机器人研究具有重要意义，但运行大量实体机器人仍面临诸多挑战，这些挑战远超仿真环境中的问题，包括通信饱和、实时定位困难、异常机器人行为检测，以及需要用户友好的界面以实现有效的状态管理。这些因素共同导致了实验室规模研究与实际可部署技术之间的假设存在显著差距。因此，我们开发了一套适用于多机器人研究的通用测试平台，名为“100-Mouse系统”，可容纳多达100台差动驱动机器人。该系统的突出特点是可扩展性，同时保持灵活性，使研究人员能够使用真实的机器人集群来验证其设想。这得益于从软件和硬件两个方面精心设计的模块，以及在大规模实验中至关重要的用户界面。作为该平台的应用，我们以集中化的方式提出一种模式形成任务，展示了系统有效容纳大型机器人集群的能力。 |
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