Mobile Robotics Lab 2015:Autonamous Landing on a Moving Target
by Chen Minhua, Guo Jiangtao
1 Abstract
Autonomous landing for Unmanned Aerial Vehicle (UAV) is an important aspect in the field of UAV research. If you want to build a pilotless plane, you have to let it use automatic landing method to land itself on particular target. automatic landing can also be used in forced landing when a plane come up with some problem like running low on battery. Currently, there are some relative autonomous landing methods such as GPS (Global Position System) and INS (Inertial Navigation System). But if the landing environment is very bad or indoor environment, these methods won't be robust enough. As computer vision is developing in a high rate, it has been widely applied in UAV automatic landing research. We can also combined these methods together to get better performance.
2 Our Target
In our project, we want to consider the automatic landing problem and find an efficient and useful methods for aircraft indoors. We use AR.Drone 2.0 as the air platform and try to land it on a fixed or moving target.
In order to guarantee that the AR.Drone can recognize the landing target correctly. Here, QR code and AR tag are used as the target marker. Next, We let the AR.Drone try to get to the place just directly above the landing target. Then, according to the information we get from the marker(QR code or AR tag) image which is based on the fronte camera of AR.Drone 2.0 , we can calculate the distance from the aircraft to the landing platform. Finally, AR.Drone lands on the target smoothly.
3 Hardware and Software
- AR.Drone 2.0: it's a nice UAV
- Zbar library: for QR code recognition.
- The Robot Operating System (ROS): it's a flexible framework for writing robot software. Here, we use the laptop with ubuntu 14.04 and ROS indigo version installed.
- ardrone_autonomy
- ARDroneLib: The ardrone_autonomy drone driver
- tum_vision: This package builds on the well known monocular SLAM framework PTAM presented by Klein & Murray in their paper[1]
- ar_track_alvar: This package for AR tag recognition
4 Experiment
What we want to do is to land the UAV to the marker target. So we do some experiments to test our result.
This is a short video about our project.
5 Supplements
Here are some other details about our project.
- the report about our project: autonamous landing
- the slides about our project: autonamous landing slides
Any problem, welcome give us feedback.
- Guo Jiangtao: guojt@shanghaitech.edu.cn
- Chen Minhua: chenmh@shanghaitech.edu.cn
6 Reference
[1] Georg Klein and David Murray. Parallel tracking and mapping for small AR workspaces. In Proc. Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'07), Nara, Japan, November 2007.
[2] Miguel A Olivares-Méndez, Iván Fernando Mondragón, Pascual Campoy, and C Martinez. Fuzzy controller for uav-landing task using 3d-position visual estimation. In Fuzzy Systems (FUZZ), 2010 IEEE International Conference on, pages 1–8. Ieee, 2010.
[3] Daquan Tang, Fei Li, Ning Shen, and Shaojun Guo. Uav attitude and position estimation for vision-based landing. In Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on, volume 9, pages 4446–4450. IEEE, 2011.
[4] Allen C Tsai, Peter W Gibbens, and R Hugh Stone. Terminal phase visual position estimation for a tail-sitting vertical takeoff and landing uav via a kalman filter. In Optics East 2007, pages 67640P–67640P. International Society for Optics and Photonics, 2007.
[5] Guili Xu, Xin Chen, Biao Wang, Kaiyu Li, Jingdong Wang, and Xu Wei. A search strategy of uav's automatic landing on ship in all weathe. In Electrical and Control Engineering (ICECE), 2011 International Conference on, pages 2857–2860. IEEE, 2011.
[6] Patrick Benavidez, Josue Lambert, Aldo Jaimes, and Mo Jamshidi. Landing of an ardrone 2.0 quadcopter on a mobile base using fuzzy logic. In World Automation Congress (WAC), 2014, pages 803–812. IEEE, 2014.
[7]Patrick J Benavidez, Josue Lambert, Aldo Jaimes, and Mo Jamshidi. Land- ing of a quadcopter on a mobile base using fuzzy logic. In Advance Trends in Soft Computing, pages 429–437. Springer, 2014.
[8] Nick Dijkshoorn. Simultaneous localization and mapping with the ar. drone. PhD diss., Masters thesis, Universiteit van Amsterdam, 2012.
[9] Nick Dijkshoorn and Arnoud Visser. Integrating sensor and motion models to localize an autonomous ar. drone. International Journal of Micro Air Vehicles, 3(4):183–200, 2011.