# Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems

arXiv: https://arxiv.org/abs/2401.04960 · HTML: https://mlsanity.com/paper/2401.04960/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [Data-Driven MPC for Quadrotors](https://mlsanity.com/paper/2102.05773/) (2021)
- [Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories](https://mlsanity.com/paper/1712.02402/) (2017)
- [FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning](https://mlsanity.com/paper/1703.07373/) (2017)
- [Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing](https://mlsanity.com/paper/1810.06224/) (2018)
- [Fast, Autonomous Flight in GPS-Denied and Cluttered Environments](https://mlsanity.com/paper/1712.02052/) (2017)
- [Search-based Motion Planning for Aggressive Flight in SE(3)](https://mlsanity.com/paper/1710.02748/) (2017)
- [Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion](https://mlsanity.com/paper/1804.00307/) (2018)
- [Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness](https://mlsanity.com/paper/1809.04048/) (2018)
- [Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments](https://mlsanity.com/paper/1907.00520/) (2019)
- [RotorPy: A Python-based Multirotor Simulator with Aerodynamics for Education and Research](https://mlsanity.com/paper/2306.04485/) (2023)
- [Fast UAV Trajectory Optimization using Bilevel Optimization with Analytical Gradients](https://mlsanity.com/paper/1811.10753/) (2018)
- [Multi-Rate Control Design Leveraging Control Barrier Functions and Model Predictive Control Policies](https://mlsanity.com/paper/2004.01761/) (2020)
- [The Urban Last Mile Problem: Autonomous Drone Delivery to Your Balcony](https://mlsanity.com/paper/1809.08022/) (2018)
- [Augmented Lagrangian Methods as Layered Control Architectures](https://mlsanity.com/paper/2311.06404/) (2023)
- [Model-free online motion adaptation for energy efficient flights of multicopters](https://mlsanity.com/paper/2108.03807/) (2021)
- [Probabilistic Trajectory Planning for Static and Interaction-aware Dynamic Obstacle Avoidance](https://mlsanity.com/paper/2302.12873/) (2023)
- [A Data-Driven Approach to Synthesizing Dynamics-Aware Trajectories for Underactuated Robotic Systems](https://mlsanity.com/paper/2307.13782/) (2023)

## Built on by (most cited first, 1 of 1)

- [Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic Learning](https://mlsanity.com/paper/2408.01639/) (2024)

## Abstract (arXiv)

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.