/Learning Monocular Reactive UAV Control in Cluttered Natural Environments

Learning Monocular Reactive UAV Control in Cluttered Natural Environments

Stephane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J. Andrew (Drew) Bagnell and Martial Hebert
Conference Paper, No. 1211.169, November, 2012

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly straight-forward, as expensive sensors and monitoring devices can be employed. In contrast, obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike large vehicles, MAVs can only carry very light sensors, such as cameras, making autonomous navigation through obstacles much more challenging. In this paper, we describe a system that navigates a small quadrotor helicopter autonomously at low altitude through natural forest environments. Using only a single cheap camera to perceive the environment, we are able to maintain a constant velocity of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors.

BibTeX Reference
author = {Stephane Ross and Narek Melik-Barkhudarov and Kumar Shaurya Shankar and Andreas Wendel and Debadeepta Dey and J. Andrew (Drew) Bagnell and Martial Hebert},
title = {Learning Monocular Reactive UAV Control in Cluttered Natural Environments},
year = {2012},
month = {November},
publisher = {ArXiv},
keywords = {Imitation Learning, MAV, Control, Vision},