# Airways: Optimization-Based Planning of Quadrotor Trajectories according to High-Level User Goals

arXiv: https://arxiv.org/abs/1906.11669 · HTML: https://mlsanity.com/paper/1906.11669/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning](https://mlsanity.com/paper/2109.09923/) (2021)
- [3D Human Reconstruction in the Wild with Collaborative Aerial Cameras](https://mlsanity.com/paper/2108.03936/) (2021)
- [Active Human Pose Estimation via an Autonomous UAV Agent](https://mlsanity.com/paper/2407.01811/) (2024)

## Abstract (arXiv)

In this paper we propose a computational design tool that al-lows end-users to create advanced quadrotor trajectories witha variety of application scenarios in mind. Our algorithm al-lows novice users to create quadrotor based use-cases withoutrequiring deep knowledge in either quadrotor control or theunderlying constraints of the target domain. To achieve thisgoal we propose an optimization-based method that gener-ates feasible trajectories which can be flown in the real world.Furthermore, the method incorporates high-level human ob-jectives into the planning of flight trajectories. An easy touse 3D design tool allows for quick specification and edit-ing of trajectories as well as for intuitive exploration of theresulting solution space. We demonstrate the utility of our ap-proach in several real-world application scenarios, includingaerial-videography, robotic light-painting and drone racing.