Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles
Philip Dames, Mac Schwager, Daniela Rus, Vijay Kumar

Citation
Philip Dames, Mac Schwager, Daniela Rus, Vijay Kumar. "Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles". submitted to the IEEE Robotics and Automation Letters (R-AL), November 2015.

Abstract
Magnetic Anomaly Detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.

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Citation formats

• HTML

  Philip Dames, Mac Schwager, Daniela Rus, Vijay Kumar. <a
  href="http://www.terraswarm.org/pubs/695.html"
  >Active Magnetic Anomaly Detection Using Multiple Micro
  Aerial Vehicles</a>, <i>submitted to the IEEE
  Robotics and Automation Letters (R-AL)</i>, November
  2015.

• Plain text

  Philip Dames, Mac Schwager, Daniela Rus, Vijay Kumar.
  "Active Magnetic Anomaly Detection Using Multiple Micro
  Aerial Vehicles". <i>submitted to the IEEE
  Robotics and Automation Letters (R-AL)</i>, November
  2015.

• BibTeX

  @article{DamesSchwagerRusKumar15_ActiveMagneticAnomalyDetectionUsingMultipleMicroAerial,
      author = {Philip Dames and Mac Schwager and Daniela Rus and
                Vijay Kumar},
      title = {Active Magnetic Anomaly Detection Using Multiple
                Micro Aerial Vehicles},
      journal = {submitted to the IEEE Robotics and Automation
                Letters (R-AL)},
      month = {November},
      year = {2015},
      abstract = {Magnetic Anomaly Detection (MAD) is an important
                problem in applications ranging from geological
                surveillance to military reconnaissance. MAD
                sensors detect local disturbances in the magnetic
                field, which can be used to detect the existence
                of and to estimate the position of buried, hidden,
                or submerged objects, such as ore deposits or
                mines. These sensors may experience false positive
                and false negative detections and, without prior
                knowledge of the targets, can only determine
                proximity to a target. The uncertainty in the
                sensors, coupled with a lack of knowledge of even
                the existence of targets, makes the estimation and
                control problems challenging. We utilize a
                hierarchical decomposition of the environment,
                coupled with an estimation algorithm based on
                random finite sets, to determine the number of and
                the locations of targets in the environment. The
                small team of robots follow the gradient of mutual
                information between the estimated set of targets
                and the future measurements, locally maximizing
                the rate of information gain. We present
                experimental results of a team of quadrotor micro
                aerial vehicles discovering and localizing an
                unknown number of permanent magnets.},
      URL = {http://terraswarm.org/pubs/695.html}
  }
  

Posted by Elizabeth Coyne on 9 Nov 2015.
Groups: services tools