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LARS: Light Augmented Reality System for Swarm

Mohsen Raoufi, Pawel Romanczuk, Heiko Hamann

TL;DR

The Light Augmented Reality System LARS leverages light-projected visual scenes for indirect robot-robot and human-robot interaction through the real environment and operates in real-time and is compatible with a range of robotic platforms.

Abstract

We present the Light Augmented Reality System LARS as an open-source and cost-effective tool. LARS leverages light-projected visual scenes for indirect robot-robot and human-robot interaction through the real environment. It operates in real-time and is compatible with a range of robotic platforms, from miniature to middle-sized robots. LARS can support researchers in conducting experiments with increased freedom, reliability, and reproducibility. This XR tool makes it possible to enrich the environment with full control by adding complex and dynamic objects while keeping the properties of robots as realistic as they are.

LARS: Light Augmented Reality System for Swarm

TL;DR

The Light Augmented Reality System LARS leverages light-projected visual scenes for indirect robot-robot and human-robot interaction through the real environment and operates in real-time and is compatible with a range of robotic platforms.

Abstract

We present the Light Augmented Reality System LARS as an open-source and cost-effective tool. LARS leverages light-projected visual scenes for indirect robot-robot and human-robot interaction through the real environment. It operates in real-time and is compatible with a range of robotic platforms, from miniature to middle-sized robots. LARS can support researchers in conducting experiments with increased freedom, reliability, and reproducibility. This XR tool makes it possible to enrich the environment with full control by adding complex and dynamic objects while keeping the properties of robots as realistic as they are.

Paper Structure

This paper contains 1 section, 1 figure.

Table of Contents

  1. Acknowledgements.

Figures (1)

  • Figure 1: Example scenarios with (a) two Thymio robots with different ring colors, (b) 109 Kilobots cleaning up the environment, and (c) 63 Kilobots making a collective decision on a tiled environment with dynamic noise.