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 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.