Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles
Name
Rus_Active magnetic.pdf
Size
4.65 MB
Format
Adobe PDF
Checksum (MD5)
24fcf959508a7bec3b22e7272f33a4d2
Author(s)
Dames, Philip M.
Schwager, Mac
Kumar, Vijay
Rus, Daniela L
Date Issued
December 2015
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Dames, Philip M. et al. “Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles.” IEEE Robotics and Automation Letters 1, 1 (January 2016): 153–160 © 2016 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/LRA.2015.2511444
