Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots
This paper addresses the collective odor source localization (OSL) problem in a time-varying airflow environment using mobile robots. A novel OSL methodology which combines odor-source probability estimation and multiple robots’ search is proposed. The estimation phase consists of two steps: firstly...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2011-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/11/11/10415/ |
| _version_ | 1828109181220028416 |
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| author | Wei-Xing Yang Yang Wang Ming Zeng Qing-Hao Meng |
| author_facet | Wei-Xing Yang Yang Wang Ming Zeng Qing-Hao Meng |
| author_sort | Wei-Xing Yang |
| collection | DOAJ |
| description | This paper addresses the collective odor source localization (OSL) problem in a time-varying airflow environment using mobile robots. A novel OSL methodology which combines odor-source probability estimation and multiple robots’ search is proposed. The estimation phase consists of two steps: firstly, the separate probability-distribution map of odor source is estimated via Bayesian rules and fuzzy inference based on a single robot’s detection events; secondly, the separate maps estimated by different robots at different times are fused into a combined map by way of distance based superposition. The multi-robot search behaviors are coordinated via a particle swarm optimization algorithm, where the estimated odor-source probability distribution is used to express the fitness functions. In the process of OSL, the estimation phase provides the prior knowledge for the searching while the searching verifies the estimation results, and both phases are implemented iteratively. The results of simulations for large-scale advection–diffusion plume environments and experiments using real robots in an indoor airflow environment validate the feasibility and robustness of the proposed OSL method. |
| first_indexed | 2024-04-11T10:58:44Z |
| format | Article |
| id | doaj.art-6fcdf7b7a0cf414aa8858dcb625ee70c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T10:58:44Z |
| publishDate | 2011-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6fcdf7b7a0cf414aa8858dcb625ee70c2022-12-22T04:28:41ZengMDPI AGSensors1424-82202011-11-011111104151044310.3390/s111110415Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile RobotsWei-Xing YangYang WangMing ZengQing-Hao MengThis paper addresses the collective odor source localization (OSL) problem in a time-varying airflow environment using mobile robots. A novel OSL methodology which combines odor-source probability estimation and multiple robots’ search is proposed. The estimation phase consists of two steps: firstly, the separate probability-distribution map of odor source is estimated via Bayesian rules and fuzzy inference based on a single robot’s detection events; secondly, the separate maps estimated by different robots at different times are fused into a combined map by way of distance based superposition. The multi-robot search behaviors are coordinated via a particle swarm optimization algorithm, where the estimated odor-source probability distribution is used to express the fitness functions. In the process of OSL, the estimation phase provides the prior knowledge for the searching while the searching verifies the estimation results, and both phases are implemented iteratively. The results of simulations for large-scale advection–diffusion plume environments and experiments using real robots in an indoor airflow environment validate the feasibility and robustness of the proposed OSL method.http://www.mdpi.com/1424-8220/11/11/10415/odor source localizationmulti-robotestimationsearchBayesian rulesfuzzy inferenceparticle swarm optimization |
| spellingShingle | Wei-Xing Yang Yang Wang Ming Zeng Qing-Hao Meng Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots Sensors odor source localization multi-robot estimation search Bayesian rules fuzzy inference particle swarm optimization |
| title | Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots |
| title_full | Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots |
| title_fullStr | Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots |
| title_full_unstemmed | Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots |
| title_short | Collective Odor Source Estimation and Search in Time-Variant Airflow Environments Using Mobile Robots |
| title_sort | collective odor source estimation and search in time variant airflow environments using mobile robots |
| topic | odor source localization multi-robot estimation search Bayesian rules fuzzy inference particle swarm optimization |
| url | http://www.mdpi.com/1424-8220/11/11/10415/ |
| work_keys_str_mv | AT weixingyang collectiveodorsourceestimationandsearchintimevariantairflowenvironmentsusingmobilerobots AT yangwang collectiveodorsourceestimationandsearchintimevariantairflowenvironmentsusingmobilerobots AT mingzeng collectiveodorsourceestimationandsearchintimevariantairflowenvironmentsusingmobilerobots AT qinghaomeng collectiveodorsourceestimationandsearchintimevariantairflowenvironmentsusingmobilerobots |