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...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
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 |
| Summary: | 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). |
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