用概率马尔可夫模型实现飞行器近距离操作的鲁棒控制
Robust Proximity Operations using Probabilistic Markov Models
- 基于马尔可夫决策过程切换多种导航模式
- 融合陀螺仪、单目视觉和超宽带雷达数据
- 适合小卫星对接与无人机精准着陆场景
本文设计、实现并分析了一种基于马尔可夫决策过程的状态切换框架，用于各类自主飞行器的近距离操作。该框架包含一个统一的姿态估计算法，采用扩展卡尔曼滤波融合速率陀螺仪、单目视觉和超宽带雷达传感器的数据，并通过马氏距离进行异常值剔除及测量权重调整以提升鲁棒性。提出使用概率马尔可夫模型在不同引导模式间切换，以实现鲁棒且高效的近距离操作。最后，通过两颗小型卫星的对接实验以及空中飞行器的精密着陆测试验证了该框架的有效性。
原文摘要 · Abstract (English)
A Markov decision process-based state switching is devised, implemented, and analyzed for proximity operations of various autonomous vehicles. The framework contains a pose estimator along with a multi-state guidance algorithm. The unified pose estimator leverages the extended Kalman filter for the fusion of measurements from rate gyroscopes, monocular vision, and ultra-wideband radar sensors. It is also equipped with Mahalonobis distance-based outlier rejection and under-weighting of measurements for robust performance. The use of probabilistic Markov models to transition between various guidance modes is proposed to enable robust and efficient proximity operations. Finally, the framework is validated through an experimental analysis of the docking of two small satellites and the precision landing of an aerial vehicle.
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