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Chemical plume source localization.

Shuo Pang1, Jay A Farrell

  • 1Department of Electrical Engineering, University of California, Riverside, CA 92521, USA. shuo.pang@erau.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 14, 2006
PubMed
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This study introduces a new Bayesian method for autonomous vehicles to map chemical plume sources in turbulent, high-Reynolds-number flows. The approach effectively updates source likelihood using detection and non-detection data.

Area of Science:

  • Robotics
  • Fluid Dynamics
  • Chemical Sensing

Background:

  • Chemical plume tracing is crucial for environmental monitoring and source localization.
  • High Reynolds number fluid flow leads to turbulent dispersion and intermittent chemical signals.
  • Previous vehicle-based plume tracing strategies have limitations in dynamic environments.

Purpose of the Study:

  • To develop an advanced source-likelihood mapping approach for autonomous chemical plume tracing.
  • To utilize Bayesian inference for real-time map updating based on sensor data.
  • To compare the novel Bayesian method with existing techniques using experimental data.

Main Methods:

  • Employing an autonomous vehicle equipped with chemical concentration and flow velocity sensors.

Related Experiment Videos

  • Implementing a Bayesian inference framework to estimate and propagate a source-likelihood map.
  • Updating the map dynamically based on both positive (detection) and negative (non-detection) chemical signal events.
  • Main Results:

    • The proposed Bayesian methodology provides a robust method for estimating chemical plume source locations.
    • The time-propagated source-likelihood map demonstrates effective updates with intermittent detection data.
    • In-water testing validated the superior performance of the new mapping approach compared to prior techniques.

    Conclusions:

    • The developed Bayesian approach enhances autonomous chemical plume source localization in turbulent flows.
    • This method offers improved accuracy and adaptability for environmental monitoring and search operations.
    • The study highlights the potential of autonomous sensing and Bayesian inference for complex environmental challenges.