A Human-Interactive Course of Action Planner for Aircraft Carrier Deck Operations
Aircraft carrier deck operations present a complex and uncertain environment in which time-critical scheduling and planning must be done, and to date all course of action planning is done solely by human operators who rely on experience and training to safely negotiate off -nominal situations. A com...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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American Institute of Aeronautics and Astronautics
2013
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| Online Access: | http://hdl.handle.net/1721.1/81478 https://orcid.org/0000-0001-8576-1930 |
| _version_ | 1826216334328856576 |
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| author | Michini, Bernard J. How, Jonathan P. |
| author2 | Massachusetts Institute of Technology. Aerospace Controls Laboratory |
| author_facet | Massachusetts Institute of Technology. Aerospace Controls Laboratory Michini, Bernard J. How, Jonathan P. |
| author_sort | Michini, Bernard J. |
| collection | MIT |
| description | Aircraft carrier deck operations present a complex and uncertain environment in which time-critical scheduling and planning must be done, and to date all course of action planning is done solely by human operators who rely on experience and training to safely negotiate off -nominal situations. A computer decision support system could provide the operator with both a vital resource in emergency scenarios as well as suggestions to improve e fficiency during normal operations. Such a decision support system would generate a schedule of coordinated deck operations for all active aircraft (taxi, refuel, take o ff, queue in Marshal stack, land, etc.) that is optimized for effi ciency, amenable to the operator, and robust to the many types of uncertainty inherent in the aircraft carrier deck environment. This paper describes the design, implementation, and testing of a human-interactive aircraft carrier deck course of action planner. The planning problem is cast in the MDP framework such that a wide range of current literature can be used to fi nd an optimal policy. It is designed such that human operators can specify priority aircraft and suggest scheduling orders. Inverse reinforcement learning techniques are applied that allow the planner to learn from recorded expert demonstrations. Results are presented that compare various types of human and learned policies, and show qualitative and quantitative matching between expert demonstrations and learned policies. |
| first_indexed | 2024-09-23T16:46:04Z |
| format | Article |
| id | mit-1721.1/81478 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:46:04Z |
| publishDate | 2013 |
| publisher | American Institute of Aeronautics and Astronautics |
| record_format | dspace |
| spelling | mit-1721.1/814782022-10-03T08:08:20Z A Human-Interactive Course of Action Planner for Aircraft Carrier Deck Operations Michini, Bernard J. How, Jonathan P. Massachusetts Institute of Technology. Aerospace Controls Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Michini, Bernard J. How, Jonathan P. Aircraft carrier deck operations present a complex and uncertain environment in which time-critical scheduling and planning must be done, and to date all course of action planning is done solely by human operators who rely on experience and training to safely negotiate off -nominal situations. A computer decision support system could provide the operator with both a vital resource in emergency scenarios as well as suggestions to improve e fficiency during normal operations. Such a decision support system would generate a schedule of coordinated deck operations for all active aircraft (taxi, refuel, take o ff, queue in Marshal stack, land, etc.) that is optimized for effi ciency, amenable to the operator, and robust to the many types of uncertainty inherent in the aircraft carrier deck environment. This paper describes the design, implementation, and testing of a human-interactive aircraft carrier deck course of action planner. The planning problem is cast in the MDP framework such that a wide range of current literature can be used to fi nd an optimal policy. It is designed such that human operators can specify priority aircraft and suggest scheduling orders. Inverse reinforcement learning techniques are applied that allow the planner to learn from recorded expert demonstrations. Results are presented that compare various types of human and learned policies, and show qualitative and quantitative matching between expert demonstrations and learned policies. United States. Office of Naval Research (Science of Autonomy Program) 2013-10-23T13:40:08Z 2013-10-23T13:40:08Z 2011-03 Article http://purl.org/eprint/type/ConferencePaper 978-1-60086-944-0 1946-9802 http://hdl.handle.net/1721.1/81478 Michini, Bernard, and Jonathan How. “A Human-Interactive Course of Action Planner for Aircraft Carrier Deck Operations.” In Infotech@Aerospace 2011. American Institute of Aeronautics and Astronautics, 2011. https://orcid.org/0000-0001-8576-1930 en_US http://dx.doi.org/10.2514/6.2011-1515 Proceedings of the Infotech@Aerospace 2011 Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf American Institute of Aeronautics and Astronautics MIT web domain |
| spellingShingle | Michini, Bernard J. How, Jonathan P. A Human-Interactive Course of Action Planner for Aircraft Carrier Deck Operations |
| title | A Human-Interactive Course of Action Planner for Aircraft Carrier Deck Operations |
| title_full | A Human-Interactive Course of Action Planner for Aircraft Carrier Deck Operations |
| title_fullStr | A Human-Interactive Course of Action Planner for Aircraft Carrier Deck Operations |
| title_full_unstemmed | A Human-Interactive Course of Action Planner for Aircraft Carrier Deck Operations |
| title_short | A Human-Interactive Course of Action Planner for Aircraft Carrier Deck Operations |
| title_sort | human interactive course of action planner for aircraft carrier deck operations |
| url | http://hdl.handle.net/1721.1/81478 https://orcid.org/0000-0001-8576-1930 |
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