In this paper, we solve a joint cooperative localization and path planning problem for a group of Autonomous Aerial Vehicles (AAVs) in GPS-denied areas using nonlinear model predictive control (NMPC). A moving horizon estimator (MHE) is used to estimate the vehicle states with the help of relative bearing information to known landmarks and other vehicles. The goal of the NMPC is to devise optimal paths for each vehicle between a given source and destination while maintaining desired localization accuracy. Estimating localization covariance in the NMPC is computationally intensive, hence we develop an approximate analytical closed form expression based on the relationship between covariance and path lengths to landmarks. Using this expression while computing NMPC commands reduces the computational complexity significantly. We present numerical simulations to validate the proposed approach for different numbers of vehicles and landmark configurations. We also compare the results with EKF-based estimation to show the superiority of the proposed closed form approach.

翻译：在本文中,我们用非线性模型预测控制(NMPC)解决了在全球定位系统封闭区一组自主飞行器(AAVs)的联合合作定位和路径规划问题。使用移动地平线估计器(MHE)来估计车辆状态,帮助将信息相对携带到已知的地标和其他车辆上。NMPC的目标是为每一车辆在特定来源和目的地之间设计最佳路径,同时保持理想的本地化准确度。估计NMPC中的本地化共变情况是计算密集的,因此我们根据相异性和路由长度与地标之间的关系开发了一种大致的分析封闭形式表达方式。在计算NMPC命令大幅降低计算复杂性时使用这一表达方式。我们提出数字模拟,以验证对不同数量车辆和地标配置的拟议方法。我们还将结果与基于EKF的估算结果进行比较,以显示拟议的封闭形式方法的优越性。