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Multi-gas source localization and mapping by flocking robots
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AI
DOI：10.1016/j.inffus.2022.11.001.png)
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摘要
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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.
Keyword:
Robotic flock
Obstacle avoidance
Gas source localization
Particle filter
Sensor fusion
期刊
IF:
15.5
论文数:
4.2K
被引数:
2.7W
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