Exploration and Mapping using a Sparse Robot Swarm: Simulation Results

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.