The Cambridge RoboMaster: An Agile Multi-Robot Research Platform

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 at this https URL.

Publication
In Preprint
Jan Blumenkamp
Jan Blumenkamp
PhD Candidate

Jan’s research is about transferring Multi-Agent control policies trained in simulation to the real world (sim-to-real transfer), using Multi-Agent Reinforcement Learning and Graph Neural Networks. He is also interested in interpretability, resilience and robustness of such control policies, particularly in the context of real-world systems.

Ajay Shankar
Ajay Shankar
Postdoctoral Researcher

Ajay’s research is that of a full-stack roboticist – with a focus on robust, optimal, and agile control + planning for various robots and robotic teams. Current focus is on scalable and learnt multi-robot coordination.

Matteo Bettini
Matteo Bettini
PhD Candidate

Matteo’s research is focused on studying heterogeneity and resilience in multi-agent and multi-robot systems.

Amanda Prorok
Amanda Prorok
Professor

Amanda’s research focuses on multi-agent and multi-robot systems. Our mission is to find new ways of coordinating artificially intelligent agents (e.g., robots, vehicles, machines) to achieve common goals in shared physical and virtual spaces.

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