Motivated by the increasing use of quadrotors for payload delivery, we consider a joint trajectory generation and feedback control design problem for a quadrotor experiencing aerodynamic wrenches. Unmodeled aerodynamic drag forces from carried payloads can lead to catastrophic outcomes. Prior work model aerodynamic effects as residual dynamics or external disturbances in the control problem leading to a reactive policy that could be catastrophic. Moreover, redesigning controllers and tuning control gains on hardware platforms is a laborious effort. In this paper, we argue that adapting the trajectory generation component keeping the controller fixed can improve trajectory tracking for quadrotor systems experiencing drag forces. To achieve this, we formulate a drag-aware planning problem by applying a suitable relaxation to an optimal quadrotor control problem, introducing a tracking cost function which measures the ability of a controller to follow a reference trajectory. This tracking cost function acts as a regularizer in trajectory generation and is learned from data obtained from simulation. Our experiments in both simulation and on the Crazyflie hardware platform show that changing the planner reduces tracking error by as much as 83%. Evaluation on hardware demonstrates that our planned path, as opposed to a baseline, avoids controller saturation and catastrophic outcomes during aggressive maneuvers.

翻译：基于四旋翼无人机在载荷投递场景中的日益广泛应用，本文研究了受空气动力力矩影响的四旋翼系统的轨迹生成与反馈控制联合设计问题。未被建模的载荷气动阻力可能导致灾难性后果。现有研究将空气动力学效应建模为控制问题中的残余动力学或外部干扰，由此产生的反应式策略可能引发系统崩溃。此外，在硬件平台上重新设计控制器并整定控制增益是一项耗时的工作。本文论证了在保持控制器不变的前提下，通过改进轨迹生成组件能够提升受阻力影响的四旋翼系统的轨迹跟踪性能。为此，本文通过对最优四旋翼控制问题进行适当松弛，构建了阻力感知规划问题，并引入可衡量控制器对参考轨迹跟踪能力的代价函数。该跟踪代价函数作为轨迹生成的正则化项，通过仿真数据学习得到。在仿真环境与Crazyflie硬件平台上的实验表明，仅更换规划器即可将跟踪误差降低最多83%。硬件评估进一步证实，与基线方法相比，本文规划的轨迹在激进机动过程中能够避免控制器饱和与灾难性后果。