Informative path planning for anomaly detection in environment exploration and monitoring
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 envi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2024
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| Online Access: | https://hdl.handle.net/1721.1/154253 |
| _version_ | 1811071402050060288 |
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| author | Blanchard, Antoine Sapsis, Themistoklis |
| author_facet | Blanchard, Antoine Sapsis, Themistoklis |
| author_sort | Blanchard, Antoine |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:50:28Z |
| format | Article |
| id | mit-1721.1/154253 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:50:28Z |
| publishDate | 2024 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1542532024-04-20T04:00:06Z Informative path planning for anomaly detection in environment exploration and monitoring Blanchard, Antoine Sapsis, Themistoklis Ocean Engineering Environmental Engineering 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. 2024-04-19T17:38:57Z 2024-04-19T17:38:57Z 2022-01 2024-04-19T17:34:17Z Article http://purl.org/eprint/type/JournalArticle 0029-8018 https://hdl.handle.net/1721.1/154253 Blanchard, Antoine and Sapsis, Themistoklis. 2022. "Informative path planning for anomaly detection in environment exploration and monitoring." Ocean Engineering, 243. en 10.1016/j.oceaneng.2021.110242 Ocean Engineering Creative Commons Attribution-Noncommercial-ShareAlike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Elsevier BV arxiv |
| spellingShingle | Ocean Engineering Environmental Engineering Blanchard, Antoine Sapsis, Themistoklis Informative path planning for anomaly detection in environment exploration and monitoring |
| title | Informative path planning for anomaly detection in environment exploration and monitoring |
| title_full | Informative path planning for anomaly detection in environment exploration and monitoring |
| title_fullStr | Informative path planning for anomaly detection in environment exploration and monitoring |
| title_full_unstemmed | Informative path planning for anomaly detection in environment exploration and monitoring |
| title_short | Informative path planning for anomaly detection in environment exploration and monitoring |
| title_sort | informative path planning for anomaly detection in environment exploration and monitoring |
| topic | Ocean Engineering Environmental Engineering |
| url | https://hdl.handle.net/1721.1/154253 |
| work_keys_str_mv | AT blanchardantoine informativepathplanningforanomalydetectioninenvironmentexplorationandmonitoring AT sapsisthemistoklis informativepathplanningforanomalydetectioninenvironmentexplorationandmonitoring |