用距离测量实现无人机编队飞行，提升定位精度与稳定性。
Observability-Aware Control for Quadrotor Formation Flight with Range-only Measurement
- 基于短期可观性格拉姆矩阵设计控制策略，优化弱可观方向信息增益。
- 在无GPS环境下，显著提升定位置信度与估计算法鲁棒性。
- 适合需要高精度编队飞行的无人机任务，如物流运输、搜救等。
协同定位是通过低成本机间传感器实现精确位置感知，从而保障四旋翼无人机编队飞行安全的有效方法。本文针对仅能获取距离测量的四旋翼编队飞行，提出一种可观性感知控制原则。该原则基于一种新的局部可观性格拉姆（LOG）近似——短时局部可观性格拉姆（STLOG），并证明其与非线性系统中方向估计精度的关联。我们设计了可观性预测控制器（OPC），一种滚动时域控制器，通过最大化STLOG的最小特征值来生成最优输入，增强弱可观状态方向的信息获取，降低因不确定性无限增长导致估计算法发散的风险。蒙特卡洛仿真与实际飞行实验在无GNSS环境下的运输任务中验证了该方法，结果表明OPC显著提升了定位置信度和估计算法的鲁棒性。
原文摘要 · Abstract (English)
Cooperative Localization is a promising approach to achieving safe quadrotor formation flight through precise positioning via low-cost inter-drone sensors. This paper develops an observability-aware control principle tailored to quadrotor formation flight with range-only inter-drone measurements. The control principle is based on a novel approximation of the local observability Gramian (LOG), which we name the Short-Term Local Observability Gramian (STLOG). The validity of STLOG is established by proving its link to directional estimation precision in nonlinear systems. We propose the Observability Predictive Controller (OPC), a receding-horizon controller that generates optimal inputs to enhance information gain in weakly observable state directions by maximizing the minimum eigenvalue of the STLOG. This reduces the risk of estimator divergence due to the unbounded growth of uncertainty in weakly observed state components. Monte Carlo simulations and flight experiments are conducted with quadrotors in a GNSS-denied ferrying mission, showing that the OPC improves positioning confidence and estimator robustness.
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