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, 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.

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Correspondence to Qing-Hao Meng.

Electronic Supplementary Material

Below are the links to the electronic supplementary material.

Exploration for OSL (WMV 4,08 MB)

Explanation to the three Videos (DOC 35,5 KB)

Exploration for OSL (WMV 4,08 MB)

Reconstructed Bayesian (WMV 1,83 MB)

Reconstructed PF (WMV 1,87 MB)

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Li, J., Meng, Q., Wang, Y. et al. Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm. Auton Robot 30, 281–292 (2011).

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  • Odor source localization
  • Olfaction
  • Mobile robot
  • Particle filter