Air-Combat Strategy Using Approximate Dynamic Programming
Unmanned Aircraft Systems (UAS) have the potential to perform many of the dangerous missions currently own by manned aircraft. Yet, the complexity of some tasks, such as air combat, have precluded UAS from successfully carrying out these missions autonomously. This paper presents a formulation o...
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| Format: | Article |
| Language: | en_US |
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American Institute of Aeronautics and Astronautics
2011
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| Online Access: | http://hdl.handle.net/1721.1/67298 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0002-1057-3940 https://orcid.org/0000-0002-8293-0492 |
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| author | McGrew, James S. How, Jonathan P. Bush, Lawrence Williams, Brian Charles Roy, Nicholas |
| author2 | Massachusetts Institute of Technology. Aerospace Controls Laboratory |
| author_facet | Massachusetts Institute of Technology. Aerospace Controls Laboratory McGrew, James S. How, Jonathan P. Bush, Lawrence Williams, Brian Charles Roy, Nicholas |
| author_sort | McGrew, James S. |
| collection | MIT |
| description | Unmanned Aircraft Systems (UAS) have the potential to perform many
of the dangerous missions currently own by manned aircraft. Yet, the
complexity of some tasks, such as air combat, have precluded UAS from
successfully carrying out these missions autonomously. This paper presents
a formulation of a level flight, fixed velocity, one-on-one air combat maneuvering problem and an approximate dynamic programming (ADP) approach for computing an efficient approximation of the optimal policy. In the version of the problem formulation considered, the aircraft learning the
optimal policy is given a slight performance advantage. This ADP approach
provides a fast response to a rapidly changing tactical situation, long planning horizons, and good performance without explicit coding of air combat tactics. The method's success is due to extensive feature development, reward shaping and trajectory sampling. An accompanying fast and e ffective rollout-based policy extraction method is used to accomplish on-line implementation. Simulation results are provided that demonstrate the robustness of the method against an opponent beginning from both off ensive and defensive situations. Flight results are also presented using micro-UAS own at MIT's Real-time indoor Autonomous Vehicle test ENvironment
(RAVEN). |
| first_indexed | 2024-09-23T08:52:50Z |
| format | Article |
| id | mit-1721.1/67298 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:52:50Z |
| publishDate | 2011 |
| publisher | American Institute of Aeronautics and Astronautics |
| record_format | dspace |
| spelling | mit-1721.1/672982022-09-30T11:53:04Z Air-Combat Strategy Using Approximate Dynamic Programming McGrew, James S. How, Jonathan P. Bush, Lawrence Williams, Brian Charles Roy, Nicholas Massachusetts Institute of Technology. Aerospace Controls Laboratory Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Roy, Nicholas Roy, Nicholas How, Jonathan P. Bush, Lawrence Williams, Brian Charles McGrew, James S. Unmanned Aircraft Systems (UAS) have the potential to perform many of the dangerous missions currently own by manned aircraft. Yet, the complexity of some tasks, such as air combat, have precluded UAS from successfully carrying out these missions autonomously. This paper presents a formulation of a level flight, fixed velocity, one-on-one air combat maneuvering problem and an approximate dynamic programming (ADP) approach for computing an efficient approximation of the optimal policy. In the version of the problem formulation considered, the aircraft learning the optimal policy is given a slight performance advantage. This ADP approach provides a fast response to a rapidly changing tactical situation, long planning horizons, and good performance without explicit coding of air combat tactics. The method's success is due to extensive feature development, reward shaping and trajectory sampling. An accompanying fast and e ffective rollout-based policy extraction method is used to accomplish on-line implementation. Simulation results are provided that demonstrate the robustness of the method against an opponent beginning from both off ensive and defensive situations. Flight results are also presented using micro-UAS own at MIT's Real-time indoor Autonomous Vehicle test ENvironment (RAVEN). Defense University Research Instrumentation Program (U.S.) (grant number FA9550-07-1-0321) United States. Air Force Office of Scientific Research (AFOSR # FA9550-08-1-0086) American Society for Engineering Education (National Defense Science and Engineering Graduate Fellowship) 2011-11-28T21:16:54Z 2011-11-28T21:16:54Z 2010-09 Article http://purl.org/eprint/type/JournalArticle 0731-5090 1533-3884 http://hdl.handle.net/1721.1/67298 McGrew, James S. et al. “Air-Combat Strategy Using Approximate Dynamic Programming.” Journal of Guidance, Control, and Dynamics 33 (2010): 1641-1654. https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0002-1057-3940 https://orcid.org/0000-0002-8293-0492 en_US http://dx.doi.org/10.2514/1.46815 Journal of Guidance, Control, and Dynamics 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 | McGrew, James S. How, Jonathan P. Bush, Lawrence Williams, Brian Charles Roy, Nicholas Air-Combat Strategy Using Approximate Dynamic Programming |
| title | Air-Combat Strategy Using Approximate Dynamic Programming |
| title_full | Air-Combat Strategy Using Approximate Dynamic Programming |
| title_fullStr | Air-Combat Strategy Using Approximate Dynamic Programming |
| title_full_unstemmed | Air-Combat Strategy Using Approximate Dynamic Programming |
| title_short | Air-Combat Strategy Using Approximate Dynamic Programming |
| title_sort | air combat strategy using approximate dynamic programming |
| url | http://hdl.handle.net/1721.1/67298 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0002-1057-3940 https://orcid.org/0000-0002-8293-0492 |
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