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Exploration and Mapping using a Sparse Robot Swarm: Simulation Results

Abstract : 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.
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https://hal.inria.fr/hal-02864274
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Submitted on : Thursday, June 11, 2020 - 12:24:06 AM
Last modification on : Sunday, November 29, 2020 - 4:18:02 PM

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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⟩

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