Exploration and Mapping using a Sparse Robot Swarm: Simulation Results

Razanne Abu-Aisheh (1, 2) , Francesco Bronzino (1) , Myriana Rifai (1) , Brian Kilberg (3) , Kris Pister (3) , Thomas Watteyne (2)

1 Nokia Bell Labs [Paris-Saclay]
2 EVA - Wireless Networking for Evolving & Adaptive Applications
3 Berkeley EECS - Department of Electrical Engineering and Computer Sciences

This research report is a companion to the following paper: Atlas: Exploration and Mapping with a Sparse Swarm of Networked IoT Robots. Razanne Abu-Aisheh, Francesco Bronzino, Myriana Rifai, Brian Kilberg, Kris Pister, Thomas Watteyne. Workshop on Wireless Sensors and Drones in Internet of Things (Wi-DroIT), part of DCOSS, 2020. It expands that paper by providing more detailed explanations and more complete results. Exploration and mapping is a fundamental capability of a swarm of robots: robots enter an unknown area, explore it, and collectively build a map of it. This capability is important regard- less of whether the robots are crawling, flying, or swimming. Existing exploration and mapping algorithms tend to either be inefficient, or rely on having a dense swarm of robots. This paper introduces Atlas, an exploration and mapping algorithm for sparse swarms of robots, which com- pletes a full exploration even in the extreme case of a single robot. We develop an open-source simulator and show that Atlas outperforms the state-of-the-art in terms of exploration speed and completeness of the resulting map.

Mots clés: IoT; Micro-Robots; Swarm; Exploration; Mapping

Domaines: Réseaux et télécommunications [cs.NI]; Systèmes embarqués

Razanne Abu-Aisheh, Francesco Bronzino, Myriana Rifai, Brian Kilberg, Kris Pister, et al.. Exploration and Mapping using a Sparse Robot Swarm: Simulation Results. [Research Report] RR-9349, Inria. 2020, pp.17. ⟨hal-02864274⟩