Air-Combat Strategy Using Approximate Dynamic Programming
Author(s)McGrew, James S.; How, Jonathan P.; Bush, Lawrence; Williams, Brian Charles; Roy, Nicholas
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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).
DepartmentMassachusetts 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
Journal of Guidance, Control, and Dynamics
American Institute of Aeronautics and Astronautics
McGrew, James S. et al. “Air-Combat Strategy Using Approximate Dynamic Programming.” Journal of Guidance, Control, and Dynamics 33 (2010): 1641-1654.
Author's final manuscript