Abstract
The execution of flight missions by unmanned aerial vehicles (UAV) primarily relies on navigation. In particular, the navigation pipeline has traditionally been divided into positioning and control, operating in a sequential loop. However, the existing navigation pipeline, where the positioning and control are decoupled, struggles to adapt to ubiquitous uncertainties arising from measurement noise, abrupt disturbances, and nonlinear dynamics. As a result, the navigation reliability of the UAV is significantly challenged in complex dynamic areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning can be degraded by the signal reflections from surrounding high-rising buildings in complex urban areas, leading to significantly increased positioning uncertainty. An additional challenge is introduced to the control algorithm due to the complex wind disturbances in urban canyons. Given the fact that the system positioning and control are highly correlated with each other, this research proposes a **tightly joined positioning and control model (JPCM) based on factor graph optimization (FGO)**. In particular, the proposed JPCM combines sensor measurements from positioning and control constraints into a unified probabilistic factor graph. Specifically, the positioning measurements are formulated as the factors in the factor graph. In addition, the model predictive control (MPC) is also formulated as the additional factors in the factor graph. By solving the factor graph contributed by both the positioning-related factors and the MPC-based factors, the complementariness of positioning and control can be deeply exploited. Finally, we validate the effectiveness and resilience of the proposed method using a simulated quadrotor system which shows significantly improved trajectory following performance.
Abstract (translated)
无人机（UAV）执行任务的主要依赖是导航。特别是，传统的导航管道被分为定位和控制，在顺序循环中运行。然而，由于测量噪声、突然干扰和非线性动力学等原因，现有的导航管道在复杂动态区域中面临着严重的导航可靠性挑战。例如，复杂城市区域周围高耸建筑的信号反射可能会降低全球导航卫星系统（GNSS）的定位精度，导致定位不确定性大幅增加。此外，城市峡谷中的复杂风干扰给控制算法带来了额外的挑战。鉴于系统定位和控制高度相关，这项研究基于因子图优化（FGO）提出了一个**紧密连接的定位和控制模型（JPCM）**。 具体来说，与定位和控制相关的传感器测量被统一到一个概率因子图上。具体而言，定位测量被表示为因子图中的因子。此外，模型预测控制（MPC）也被表示为因子图中的其他因子。通过解决定位相关因素和基于MPC的因子图中的因素，可以深入挖掘定位和控制的互补性。最后，我们通过模拟四旋翼系统来验证所提出方法的有效性和韧性，该系统显示出明显改善的轨迹跟随性能。
URL
https://arxiv.org/abs/2404.14724
