Academic Journal
Multi-gas source localization and mapping by flocking robots
| Title: | Multi-gas source localization and mapping by flocking robots |
|---|---|
| Authors: | Tran, VP, Garratt, MA, Kasmarik, K, Anavatti, SG, Leong, AS, Zamani, M |
| Source: | Information Fusion. 91:665-680 |
| Publisher Information: | Elsevier BV, 2023. |
| Publication Year: | 2023 |
| Subject Terms: | 4605 Data Management and Data Science, 0209 industrial biotechnology, 46 Information and Computing Sciences, 4602 Artificial Intelligence, anzsrc-for: 4605 Data Management and Data Science, anzsrc-for: 46 Information and Computing Sciences, 0202 electrical engineering, electronic engineering, information engineering, anzsrc-for: 4603 Computer vision and multimedia computation, anzsrc-for: 0801 Artificial Intelligence and Image Processing, 02 engineering and technology, anzsrc-for: 4602 Artificial Intelligence |
| Description: | 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. |
| Document Type: | Article |
| Language: | English |
| ISSN: | 1566-2535 |
| DOI: | 10.1016/j.inffus.2022.11.001 |
| Rights: | Elsevier TDM CC BY |
| Accession Number: | edsair.doi.dedup.....526caa37c595f059ebbd2f17fe353d79 |
| Database: | OpenAIRE |
| ISSN: | 15662535 |
|---|---|
| DOI: | 10.1016/j.inffus.2022.11.001 |