Parameter-free Regression-based Autonomous Control of Off-the-shelf Quadrotor UAVs
Parameter-free Regression-based Autonomous Control of Off-the-shelf Quadrotor UAVs
复制标题
DOI：
10.1109/icuas.2019.8798034
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Rahul Peddi;N. Bezzo
中科院分区：
文献类型：
--
作者：
Rahul Peddi;N. Bezzo
无人机的自主飞行通常需要特定于平台的动力学参数和控制体系结构的知识。最近，无人机变得更容易使用，具有现成的选项，这些选项针对用户的遥操作进行了良好的调整和稳定，但由于未知的型号参数，它们通常还没有准备好进行自动操作。在这篇文章中，我们开发了一种方法，在最少的动力学和控制器参数知识的情况下，能够在为遥操作而设计的飞行器上实现自主飞行。该方法使用控制和动力学结构的基本知识以及人类遥控轨迹作为示范，训练薄板样条(TPS)回归模型，然后使用该回归模型操纵预先训练的命令来生成新的自主输入命令，用于在新的轨迹上进行自主导航。还提出了一种统计方法和可满足性模理论(SMT)求解器，以评估学习的预测误差和校正，以最大限度地减少输入生成的误差。提出了一种基于控制的鲁棒控制策略，用于在运行时调整自主输入命令，以实现闭环系统轨迹跟踪。最后，在四旋翼无人机上进行了轨迹跟踪实验，验证了所提方法的有效性。
Autonomous flight in unmanned aerial vehicles (UAVs) generally requires platform-specific knowledge of the dynamical parameters and control architecture. Recently, UAVs have become more accessible with off-the-shelf options that are well-tuned and stable for user teleoperation but due to unknown model parameters, they are typically not ready for autonomous operations. In this paper, we develop a method to enable autonomous flight on vehicles that are designed for teleoperation with minimal knowledge of the dynamical and controller parameters. The proposed method uses a basic knowledge of the control and dynamic architecture along with human teleoperated trajectories as demonstrations to train a thin-plate spline (TPS) regression model, which is then used to manipulate the pre-trained commands to generate new autonomous input commands for autonomous navigation over new trajectories. A statistical approach is also presented together with a satisfiability modulo theories (SMT) solver to assess the learned prediction error and correct to minimize errors in the input generation. A robust control-based strategy is also proposed to adjust autonomous input commands during run-time for closed loop trajectory tracking. Finally, we validate the proposed approach with trajectory-following experiments on a quadrotor UAV.