Why Change Your Controller When You Can Change Your Planner: Drag-Aware
  Trajectory Generation for Quadrotor Systems
Hanli Zhang Anusha Srikanthan Spencer Folk Vijay Kumar Nikolai Matni
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.