Informative path planning for anomaly detection in environment exploration and monitoring
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2005.10040.pdf
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4.36 MB
Format
Adobe PDF
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Author(s)
Blanchard, Antoine
Sapsis, Themistoklis
Date Issued
January 2022
Journal
Ocean Engineering
Publisher
Elsevier BV
Citation
Blanchard, Antoine and Sapsis, Themistoklis. 2022. "Informative path planning for anomaly detection in environment exploration and monitoring." Ocean Engineering, 243.
Version
Author's final manuscript
Abstract
An unmanned autonomous vehicle (UAV) is sent on a mission to explore and reconstruct an unknown environment from a series of measurements collected by Bayesian optimization. The success of the mission is judged by the UAV’s ability to faithfully reconstruct any anomalous features present in the environment, with emphasis on the extremes (e.g., extreme topographic depressions or abnormal chemical concentrations). We show that the criteria commonly used for determining which locations the UAV should visit are ill-suited for this task. We introduce a number of novel criteria that guide the UAV towards regions of strong anomalies by leveraging previously collected information in a mathematically elegant and computationally tractable manner. We demonstrate superiority of the proposed approach in several applications, including reconstruction of seafloor topography from real-world bathymetry data, as well as tracking of dynamic anomalies. A particularly attractive property of our approach is its ability to overcome adversarial conditions, that is, situations in which prior beliefs about the locations of the extremes are imprecise or erroneous.
Subjects
Ocean Engineering
Environmental Engineering
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.oceaneng.2021.110242
