Real-Time Flight Test Maneuver Selection with Monte Carlo Tree Search

Abstract

Flight test is shifting toward a data-centric approach in which data contribute to model refinement, reducing reliance on pre-scripted test points. An open problem is how to sequence maneuvers within a sortie to maximize uncertainty reduction under resource limits. We present a real-time planning framework that combines a Gaussian Process (GP) belief model with Monte Carlo Tree Search (MCTS) to select pilot-actionable maneuvers under fuel constraints. Candidate maneuvers are scored using weighted integrated variance reduction (wIVR), and shallow lookahead is performed with a propagated per-evaluation-point variance state to account for downstream coverage redundancy and transition cost. The planner is evaluated in a closed, human-in-the-loop X-Plane simulation against greedy wIVR selection and a fixed test-card baseline. Sortie-summary statistics show significant directional differences, with MCTS-wIVR achieving higher uncertainty reduction per unit fuel over both baselines. The results indicate that posterior-aware adaptive planning is a promising approach to increase efficiency of flight tests.