Abstract
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: this https URL
Abstract (translated)
紧凑型机器人平台具有强大的计算和执行能力是多智能体研究实际应用的关键推动力。本文介绍了一种基于holonomic地面机器人平台的设计，该平台具有强大的计算和执行能力，以实现多智能体研究的实际应用。我们的机器人，是一支由DJI Robomaster S1车辆组成的定制车队，提供小机器人不具备足够的计算或执行能力，以及不适合室内多机器人测试的较大机器人的平衡。它们运行了一个基于ROS2的模块化最优估计和控制栈来实现全车载自主，包含一个自适应的点对点通信基础设施，并且可以通过我们的向量式多智能体仿真框架训练的多智能体强化学习（MARL）策略实现零 shots。我们深入研究了其他可用的平台，展示了我们系统能力的实验验证，并引入了案例研究，突出了我们系统作为各种研究展示平台的多样性和可靠性。我们的系统和补充材料均可在线获取：https://www. this URL
URL
https://arxiv.org/abs/2405.02198
