Generating informative paths for persistent sensing in unknown environments
We present an online algorithm for a robot to shape its path to a locally optimal configuration for collecting information in an unknown dynamic environment. As the robot travels along its path, it identifies both where the environment is changing, and how fast it is changing. The algorithm then mor...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2014
|
| Online Access: | http://hdl.handle.net/1721.1/90588 https://orcid.org/0000-0001-5473-3566 |
| _version_ | 1811087495090143232 |
|---|---|
| author | Soltero, Daniel E. Schwager, Mac Rus, Daniela L. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Soltero, Daniel E. Schwager, Mac Rus, Daniela L. |
| author_sort | Soltero, Daniel E. |
| collection | MIT |
| description | We present an online algorithm for a robot to shape its path to a locally optimal configuration for collecting information in an unknown dynamic environment. As the robot travels along its path, it identifies both where the environment is changing, and how fast it is changing. The algorithm then morphs the robot's path online to concentrate on the dynamic areas in the environment in proportion to their rate of change. A Lyapunov-like stability proof is used to show that, under our proposed path shaping algorithm, the path converges to a locally optimal configuration according to a Voronoi-based coverage criterion. The path shaping algorithm is then combined with a previously introduced speed controller to produce guaranteed persistent monitoring trajectories for a robot in an unknown dynamic environment. Simulation and experimental results with a quadrotor robot support the proposed approach. |
| first_indexed | 2024-09-23T13:47:00Z |
| format | Article |
| id | mit-1721.1/90588 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:47:00Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/905882022-10-01T17:05:35Z Generating informative paths for persistent sensing in unknown environments Soltero, Daniel E. Schwager, Mac Rus, Daniela L. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. School of Engineering Soltero, Daniel E. Rus, Daniela L. We present an online algorithm for a robot to shape its path to a locally optimal configuration for collecting information in an unknown dynamic environment. As the robot travels along its path, it identifies both where the environment is changing, and how fast it is changing. The algorithm then morphs the robot's path online to concentrate on the dynamic areas in the environment in proportion to their rate of change. A Lyapunov-like stability proof is used to show that, under our proposed path shaping algorithm, the path converges to a locally optimal configuration according to a Voronoi-based coverage criterion. The path shaping algorithm is then combined with a previously introduced speed controller to produce guaranteed persistent monitoring trajectories for a robot in an unknown dynamic environment. Simulation and experimental results with a quadrotor robot support the proposed approach. United States. Office of Naval Research. Multidisciplinary University Research Initiative (Award N00014-09-1-1051) National Science Foundation (U.S.). Graduate Research Fellowship (Award 0645960) Boeing Company 2014-10-07T17:34:36Z 2014-10-07T17:34:36Z 2012-10 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-1736-8 978-1-4673-1737-5 978-1-4673-1735-1 2153-0858 http://hdl.handle.net/1721.1/90588 Soltero, Daniel E., Mac Schwager, and Daniela Rus. “Generating Informative Paths for Persistent Sensing in Unknown Environments.” 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (October 2012). https://orcid.org/0000-0001-5473-3566 en_US http://dx.doi.org/10.1109/IROS.2012.6385730 Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Soltero, Daniel E. Schwager, Mac Rus, Daniela L. Generating informative paths for persistent sensing in unknown environments |
| title | Generating informative paths for persistent sensing in unknown environments |
| title_full | Generating informative paths for persistent sensing in unknown environments |
| title_fullStr | Generating informative paths for persistent sensing in unknown environments |
| title_full_unstemmed | Generating informative paths for persistent sensing in unknown environments |
| title_short | Generating informative paths for persistent sensing in unknown environments |
| title_sort | generating informative paths for persistent sensing in unknown environments |
| url | http://hdl.handle.net/1721.1/90588 https://orcid.org/0000-0001-5473-3566 |
| work_keys_str_mv | AT solterodaniele generatinginformativepathsforpersistentsensinginunknownenvironments AT schwagermac generatinginformativepathsforpersistentsensinginunknownenvironments AT rusdanielal generatinginformativepathsforpersistentsensinginunknownenvironments |