Multi-gas source localization and mapping by flocking robots

Multi-Gas source localization and mapping is a challenging problem because multiple measurements must be taken to ensure accurate localization. This paper presents a novel flocking control strategy for multi-robot exploration and gas field mapping to address this problem. The algorithm includes an a...

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Bibliographic Details
Published in:Information fusion 2023-03, Vol.91, p.665-680
Main Authors: Tran, Vu Phi, Garratt, Matthew A., Kasmarik, Kathryn, Anavatti, Sreenatha G., Leong, Alex S., Zamani, Mohammad
Format: Article
Language:English
Subjects:
Gas source localization
Obstacle avoidance
Particle filter
Robotic flock
Sensor fusion
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Summary:Multi-Gas source localization and mapping is a challenging problem because multiple measurements must be taken to ensure accurate localization. This paper presents a novel flocking control strategy for multi-robot exploration and gas field mapping to address this problem. The algorithm includes an active sensing mechanism for driving a flock of agents towards target measurement locations that optimize the posterior probability density and a collaborative sequential Monte Carlo information fusion approach for estimating gas fields. We tested the performance of our system on Jackal mobile robots in a chemical leak scenario with two gas leakage sources. Through a series of comparison experiments, we demonstrate that our proposed strategy has superior performance to recent single-agent and centralized sequential Monte Carlo-based gas concentration mapping in terms of the estimate accuracy, the convergence time, and the mapping error. •Present a collaborative Sequential Monte Carlo method for estimating gas fields.•Propose a flocking model to keep the robots close enough to avoid disconnections.•Include an active sensing method for moving a robot swarm towards measurement locations.•Construct an evaluation of our algorithm in a setting with multiple leakage plumes.•Conduct a comparative study benchmarking our algorithm against other strategies.
ISSN:1566-2535
1872-6305
DOI:10.1016/j.inffus.2022.11.001