# Demo: Vega – Turning a Toy into a Ready-to-Use Robotic Platform

Narmin Elkilani, Baptiste Carbillet, Geovane Fedrecheski, Trifun Savić, Thomas Watteyne

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## To cite this version:

Narmin Elkilani, Baptiste Carbillet, Geovane Fedrecheski, Trifun Savić, Thomas Watteyne. Demo: Vega – Turning a Toy into a Ready-to-Use Robotic Platform. EWSN 2025 - European Conference on Wireless Sensor Networks, Sep 2025, Leuven, Belgium. hal-05286515

HAL Id: hal-05286515

https://inria.hal.science/hal-05286515v1

Submitted on 27 Sep 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Demo: Vega – Turning a Toy into a Ready-to-Use Robotic Platform

**Narmin Elkilani**

narmin.elkilani@inria.fr

Inria

Paris, France

**Baptiste Carbillet**

baptiste.carbillet@inria.fr

Inria

Paris, France

**Geovane Fedrecheski**

geovane.fedrecheski@inria.fr

Inria

Paris, France

**Trifun Savic**

trifun.savic@udg.edu.me

University of Donja Gorica

Podgorica, Montenegro

**Thomas Watteyne**

thomas.watteyne@inria.fr

Inria

Paris, France

## ABSTRACT

Robotic platforms that are customizable, mobile, and compact are crucial for testing algorithms in dynamic settings and enabling small-scale swarm robotics. In this paper we introduce Vega, a robotic platform that augments the DJI RoboMaster S1 with a Raspberry Pi-based computer, allowing full control of its hardware capabilities and immediate deployment for various swarm applications.

## CCS CONCEPTS

* **Networks;** Computer systems organization → Robotic control;

## KEYWORDS

Swarm robots, RoboMaster S1, CAN protocol, MQTT

<figure><img src="image_3.png" /><figcaption>Figure 1: The Vega robotic platform</figcaption></figure>

## 1 INTRODUCTION

A versatile and ready-to-use platform that can be used both as a testbed and as a deployable unit provides significant benefits for development and experimentation within robotics teams.

With this motivation, we develop Vega, a customizable and affordable robotic platform that closely follows the design of the Cambridge RoboMaster [2]. Vega is fully controllable from a remote computer via MQTT with keyboard input, or through custom firmware running on its onboard Raspberry Pi. All components can be individually controlled over the internal CAN bus. In addition, a camera provides live video streaming feedback.

In this paper, we describe Vega's system overview, present experimental results, and showcase a practical use case. We provide complete reproduction instructions in our open-source repository<fnref n="1" />,

## 2 BACKGROUND

Robotic platforms with characteristics similar to those of Vega present trade-offs between speed, cost, and control flexibility. For example, the HiWonder TurboPi<fnref n="2" /> shares a comparable design (Raspberry Pi-based computer, Mecanum wheels) but suffers from significant speed limitations (maximum 0.3 m/s). Other platforms, such as the TurtleBot4³ provide robust integration with ROS, but often

restrict developers to high-level abstractions without exposing low-level hardware control. In contrast, Vega offers a unique balance between speed (max 4.5 m/s), price (549 €), and its support of direct low-level command access, making it a highly suitable choice for research and development.

## 3 VEGA

This section presents Vega's system overview. We showcase its performance through various experimental tests and discuss some research challenges.

DJI RoboMaster. The RoboMaster S1 is an educational robot developed by DJI, featuring four Mecanum wheels (four brushless motors, 1000 rpm max) attached to an omnidirectional chassis, along with a gimbal, a blaster and an intelligent controller. Its compact form (3.3 kg, 320×240×270 mm<fnref n="3" />) incorporates many sensors, including hit detection sensors, wheel encoders, and an Inertial Measurement Unit (IMU). The RoboMaster is powered by a 2.4 Ah smart battery with intelligent energy management, delivering 35 mins of runtime.

<footnote n="1">¹ https://github.com/DotBots/Vega</footnote>

<footnote n="2">² https://www.hiwonder.com/products/turbopi</footnote>

<footnote n="3">³ https://turtlebot.github.io/turtlebot4-user-manual/</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 2: MQTT and CAN Message Flow</figcaption></figure>

*CAN Protocol.* DJI uses Controller Area Network (CAN) as a data transport layer between its components. CAN operates on the principle of decentralized networking, where all devices on the CAN bus can transmit data equally, while offering strong fault-detection mechanisms. This protocol ensures robust, real-time, and noise-resistant data transmission suitable for mobile robotic platforms.

*A Custom Controller.* We replace the internal RoboMaster controller with a Raspberry Pi equipped with a CAN HAT module to gain low-level control over the robot, as the original controller relies on high-level programming interfaces that limit flexibility and customization. We used the C++ implementation of Cambridge proroklab<fnref n="4" /> to communicate over the CAN bus and added gimbal control. An external camera is attached as the RoboMaster camera lacks USB-C to the Raspberry Pi. It streams video via RTSP (Real-Time Streaming Protocol), a low-latency transmission protocol for audio and video that does not require users to download the media. Additionally, we integrate MQTT as detailed below.

*MQTT Architecture.* MQTT is a lightweight, publish-subscribe communication protocol commonly used to connect devices with limited network bandwidth. Fig. 2 illustrates how control commands are transmitted from the remote computer to Vega using MQTT, and then sent through the CAN bus to the target component.

*Differences with the Cambridge RoboMaster.* The Cambridge RoboMaster is designed with a focus on multi-agent research, which is why it uses ROS 2 for communication and control. In contrast, our objective is to provide a flexible and accessible testbed for algorithm development, so we chose MQTT and RTSP for communication instead. MQTT is significantly lighter, consuming fewer computational resources (approximately 100 MB of RAM for MQTT versus around 4 GB for a full ROS 2 setup). It is also much simpler to deploy, avoiding the complexity of the ROS ecosystem, which can be challenging for new users and can slow down deployment. Similarly, RTSP offers a lightweight solution for video streaming that is independent of any specific system.

*Results.* Table 1 shows Vega's performance metrics, including communication latency, battery life, and throughput.

*Research Challenges.* One limitation we encountered with Vega is rapid battery drain (35 mins under load). Scalability in the future

<table><caption>Table 1: Experimental tests</caption><thead><tr><td>Experiment</td><td>Results</td></tr></thead><tbody><tr><td>MQTT end-to-end latency</td><td>3.3 ms (10,000 msgs)</td></tr><tr><td>Battery life</td><td>58 min (Vega), 1 h 07 (RoboMaster)</td></tr><tr><td>Camera frame rate</td><td>31 FPS</td></tr><tr><td>CAN speed</td><td>213 kbit/s</td></tr></tbody></table>

may also be limited by the Raspberry Pi's computational power and the increased load from managing multiple robots through MQTT.

## 4 OPENSWARM TESTBED

As part of the OpenSwarm project<fnref n="5" />, we are developing a large-scale testbed consisting of 1,000 swarm robots called DotBots [1]. Each DotBot carries an nRF5340 chip that supports Bluetooth Low Energy (BLE) for connection to the testbed gateways. Since typical BLE implementations supports only up to 20 simultaneous connections, we have developed a custom protocol, called Mari<fnref n="6" />, that implements a novel link layer over the BLE physical layer. We will use Vega to explore and validate the dynamic behaviour of Mari in mobile contexts. For example, we will place several Mari nodes on Vega, and have it move across a predetermined path. Since we have full control over the robot, we can easily collect logs and correlate movement patterns and network metrics. This setup allows us to design reproducible networking experiments by observing how the performance changes as the nodes move.

## 5 CONCLUSION & FUTURE WORK

Vega's combination of low-level hardware control, high mobility, and low cost makes it a great tool for experimental applications and robotics research. Our future work includes making Vega fully autonomous to navigate on its own, and developing a multi-robot system of Vega platforms that can collaborate on a variety tasks.

## ACKNOWLEDGMENT

This project has received funding from the European Union's Horizon Europe Framework Program, project "OpenSwarm", under Grant Agreement No. 101093046.

## REFERENCES

[1] Said Alvarado-Marin, Martina Balbi, Alexandre Abadie, Trifun Savić, Filip Maksić-movic, and Thomas Watteyne. 2024. DotBot, a cm-Scale, Easy-to-Use Micro-Robot for Swarm Research.

[2] Jan Blumenkamp, Ajay Shankar, Matteo Bettini, Joshua Bird, and Amanda Prorok. 2024. The Cambridge RoboMaster: An Agile Multi-Robot Research Platform.

<footnote n="4">⁴ https://github.com/proroklab/robomaster_SDK_can</footnote>

<footnote n="5">⁵ https://www.openswarm.eu/</footnote>

<footnote n="6">⁶ https://github.com/DotBots/mari</footnote>