Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm

This paper discusses odor source localization (OSL) using a mobile robot in an outdoor time-variant airflow environment. A novel OSL algorithm based on particle filters (PF) is proposed. When the odor plume clue is found, the robot performs an exploratory behavior, such as a plume-tracing strategy,...

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Bibliographic Details
Published in:Autonomous robots 2011-04, Vol.30 (3), p.281-292
Main Authors: Li, Ji-Gong, Meng, Qing-Hao, Wang, Yang, Zeng, Ming
Format: Article
Language:English
Subjects:
Air flow
Airflow
Algorithms
Artificial Intelligence
Bayesian analysis
Computer Imaging
Control
Engineering
Inference
Localization
Mechatronics
Odors
Outdoor
Pattern Recognition and Graphics
Position (location)
Robotics
Robotics and Automation
Robots
Vision
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Summary:This paper discusses odor source localization (OSL) using a mobile robot in an outdoor time-variant airflow environment. A novel OSL algorithm based on particle filters (PF) is proposed. When the odor plume clue is found, the robot performs an exploratory behavior, such as a plume-tracing strategy, to collect more information about the previously unknown odor source. In parallel, the information collected by the robot is exploited by the PF-based OSL algorithm to estimate the location of the odor source in real time. The process of the OSL is terminated if the estimated source locations converge within a given small area. The Bayesian-inference-based method is also performed for comparison. Experimental results indicate that the proposed PF-based OSL algorithm performs better than the Bayesian-inference-based OSL method.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-011-9219-2