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 reliabilty 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：紧凑型且具备强大计算与驱动能力的机器人平台是实现多智能体研究在实际世界中部署的关键推动因素。本文介绍了一套紧密集成的硬件、控制与仿真软件栈，应用于一系列全向地面机器人平台，其设计正是基于上述动机。我们的机器人平台——一组定制的DJI Robomaster S1车辆——在小型机器人（计算或驱动能力不足）与大型机器人（不适合室内多机器人测试）之间取得了平衡。这些机器人运行基于ROS2的模块化最优估计与控制栈，可实现完全机载自主；内置自组织点对点通信基础设施；并能够零样本运行为我们在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。本文深入评述了现有其他平台，展示了我们系统能力的最新实验验证，并引入案例研究，突显了该系统作为广泛研究展示试验台的通用性与可靠性。我们的系统及补充材料可在线获取：https://proroklab.github.io/cambridge-robomaster