Air-Combat Strategy Using Approximate Dynamic Programming

Unmanned Aircraft Systems (UAS) have the potential to perform many of the dangerous missions currently flown 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 the one-on-one air combat maneuvering problem and an approximate dynamic programming approach to computing an efficient approximation of the optimal policy. The method’s success is due to extensive feature development, reward shaping and trajectory sampling. An accompanying fast and effective rollout based policy extraction method is used to accomplish on-line implementation. Simulation results are provided which demonstrate the robustness of the method against an opponent beginning from both offensive and defensive situations. Flight results are also presented using micro-UAS flown at MITs Real-time indoor Autonomous Vehicle test ENvironment (RAVEN).