论文标题
使用非线性模型预测控制和本地化约束的多AAV合作路径计划
Multi-AAV Cooperative Path Planning using Nonlinear Model Predictive Control with Localization Constraints
论文作者
论文摘要
在本文中，我们使用非线性模型预测控制（NMPC）解决了一组自动驾驶汽车（AAV）的联合合作定位和路径计划问题。移动地平线估计器（MHE）用于借助于已知地标和其他车辆的相对轴承信息来估算车辆状态。 NMPC的目的是在给定来源和目的地之间为每辆车辆设计最佳路径，同时保持所需的定位精度。估计NMPC中的定位协方差是计算密集的，因此我们根据协方差与路径长度与地标之间的关系开发出近似的分析闭合表达式。计算NMPC命令时使用此表达式可显着降低计算复杂性。我们提出数值模拟，以验证不同数量的车辆和具有里程碑意义的配置的建议方法。我们还将结果与基于EKF的估计进行了比较，以显示拟议的封闭形式方法的优越性。
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.
