The Cambridge RoboMaster: An Agile Multi-Robot Research Platform
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913565172039680 |
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| author | Blumenkamp, Jan Shankar, Ajay Bettini, Matteo Bird, Joshua Prorok, Amanda |
| author_facet | Blumenkamp, Jan Shankar, Ajay Bettini, Matteo Bird, Joshua Prorok, Amanda |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02198 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Cambridge RoboMaster: An Agile Multi-Robot Research Platform Blumenkamp, Jan Shankar, Ajay Bettini, Matteo Bird, Joshua Prorok, Amanda Robotics Multiagent Systems Systems and Control 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 |
| title | The Cambridge RoboMaster: An Agile Multi-Robot Research Platform |
| topic | Robotics Multiagent Systems Systems and Control |
| url | https://arxiv.org/abs/2405.02198 |