Compact robotic platforms with powerful compute and actuation capabilities are key enablers for practical, real-world deployments of multi-agent research. This article introduces a tightly integrated hardware, control, and simulation software stack on a fleet of holonomic ground robot platforms designed with this motivation. Our robots, a fleet of customised DJI Robomaster S1 vehicles, offer a balance between small robots that do not possess sufficient compute or actuation capabilities and larger robots that are unsuitable for indoor multi-robot tests. They run a modular ROS2-based optimal estimation and control stack for full onboard autonomy, contain ad-hoc peer-to-peer communication infrastructure, and can zero-shot run multi-agent reinforcement learning (MARL) policies trained in our vectorized multi-agent simulation framework. We present an in-depth review of other platforms currently available, showcase new experimental validation of our system's capabilities, and introduce case studies that highlight the versatility and reliability of our system as a testbed for a wide range of research demonstrations. Our system as well as supplementary material is available online. https://proroklab.github.io/cambridge-robomaster

翻译：具备强大计算与驱动能力的紧凑型机器人平台是实现多智能体研究在实际场景中部署的关键使能技术。本文介绍了一套紧密集成的硬件、控制与仿真软件栈，该软件栈部署于专为此目标设计的全向地面机器人集群之上。我们的机器人系统基于定制的DJI Robomaster S1车辆集群，在小尺寸机器人（计算与驱动能力不足）与大尺寸机器人（不适用于室内多机器人测试）之间取得了良好平衡。该平台运行基于ROS2的模块化最优估计与控制软件栈以实现完全机载自主，包含自组织点对点通信基础设施，并能够零样本运行在我们向量化多智能体仿真框架中训练的多智能体强化学习（MARL）策略。本文深入评述了当前可用的其他平台，展示了系统能力的新实验验证，并通过案例研究凸显了本系统作为广泛研究演示测试平台的通用性与可靠性。我们的系统及补充材料已在线发布。https://proroklab.github.io/cambridge-robomaster