AI 中文总结
研究如何在资源限制下安排飞行架次机动动作以降不确定性，提出结合高斯过程与蒙特卡洛树搜索的实时规划框架，用加权综合方差减少评分，经模拟评估，该方法在降不确定性上优于基线，是提高飞行测试效率的有前途方法。
AI 中文摘要
飞行测试正朝着以数据为中心的方法转变，数据有助于模型优化，减少对预先设定测试点的依赖。一个开放问题是如何在资源限制下安排飞行架次中的机动动作，以最大程度降低不确定性。我们提出了一个实时规划框架，将高斯过程（GP）置信模型与蒙特卡洛树搜索（MCTS）相结合，在燃油限制下选择飞行员可执行的机动动作。使用加权综合方差减少（wIVR）对候选机动动作进行评分，并通过传播每个评估点的方差状态进行浅度前瞻，以考虑下游覆盖冗余和过渡成本。在封闭的、有人参与的X-Plane模拟中，针对贪婪的wIVR选择和固定测试卡基线对规划器进行评估。飞行架次汇总统计显示出显著的方向差异，MCTS-wIVR在每单位燃油上实现了比两个基线更高的不确定性降低。结果表明，后验感知自适应规划是提高飞行测试效率的一种有前途的方法。
英文摘要
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.
CommentsN. E. Bostock and H. Pruitt-Kennett contributed equally. 9 pages, 4 figures. Accepted to the 45th AIAA/IEEE Digital Avionics Systems Conference (DASC 2026)
