Quadrocopter control using an on-board video system with off-board processing

In recent years, Unmanned Aerial Vehicles (UAVs) have gained increasing popularity. These vehicles are employed in many applications, from military operations to civilian tasks. One of the main fields of UAV research is the vehicle positioning problem. Fully autonomous vehicles are required to be as self-sustained as possible in terms of external sensors. To achieve this in situations where the global positioning system (GPS) does not function, computer vision can be used. This paper presents an implementation of computer vision to hold a quadrotor aircraft in a stable hovering position using a low-cost, consumer-grade, video system. The successful implementation of this system required the development of a data-fusion algorithm that uses both inertial sensors and visual system measurements for the purpose of positioning. The system design is unique in its ability to successfully handle missing and considerably delayed video system data. Finally, a control algorithm was implemented and the whole system was tested experimentally. The results suggest the successful continuation of research in this field.

[1]  Rihard Karba,et al.  Wide-angle camera distortions and non-uniform illumination in mobile robot tracking , 2004, Robotics Auton. Syst..

[2]  P. Zhang,et al.  Navigation with IMU/GPS/digital compass with unscented Kalman filter , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[3]  Raffaello D'Andrea,et al.  A simple learning strategy for high-speed quadrocopter multi-flips , 2010, 2010 IEEE International Conference on Robotics and Automation.

[4]  Ivan Petrovic,et al.  Extending functionality of RF Ultrasound positioning system with dead-reckoning to accurately determine mobile robot's orientation , 2009, 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC).

[5]  Gerd Hirzinger,et al.  Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[6]  Sangdeok Park,et al.  Accurate Modeling and Robust Hovering Control for a Quad–rotor VTOL Aircraft , 2010, J. Intell. Robotic Syst..

[7]  T. Hamel,et al.  A practical Visual Servo Control for a Unmanned Aerial Vehicle , 2008, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[8]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[9]  Raffaello D'Andrea,et al.  Performing and extending aggressive maneuvers using iterative learning control , 2011, Robotics Auton. Syst..

[10]  Erdinç Altug,et al.  EKF Based Attitude Estimation and Stabilization of a Quadrotor UAV Using Vanishing Points in Catadioptric Images , 2011, J. Intell. Robotic Syst..

[11]  Radu-Emil Precup,et al.  A survey on industrial applications of fuzzy control , 2011, Comput. Ind..

[12]  Roland Siegwart,et al.  Vision based MAV navigation in unknown and unstructured environments , 2010, 2010 IEEE International Conference on Robotics and Automation.

[13]  B. Sridhar,et al.  GPS/machine vision navigation system for aircraft , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Andreas Zell,et al.  Low-Cost Visual Tracking of a Landing Place and Hovering Flight Control with a Microcontroller , 2010, J. Intell. Robotic Syst..

[15]  Niels Kjølstad Poulsen,et al.  Incorporation of time delayed measurements in a discrete-time Kalman filter , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[16]  Rogelio Lozano,et al.  Stabilization and Trajectory Tracking of a Quad-Rotor Using Vision , 2011, J. Intell. Robotic Syst..

[17]  Alonzo Kelly,et al.  A 3D State Space Formulation of a Navigation Kalman Filter for Autonomous Vehicles , 1994 .

[18]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[19]  I. Škrjanc,et al.  Using a LRF sensor in the Kalman-filtering-based localization of a mobile robot. , 2010, ISA transactions.

[20]  Jan Faigl,et al.  AR-Drone as a Platform for Robotic Research and Education , 2011, Eurobot Conference.

[21]  Sang Uk Lee,et al.  Integrated Position Estimation Using Aerial Image Sequences , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  David Hyunchul Shim,et al.  A vision-based landing system for small unmanned aerial vehicles using an airbag , 2010 .

[23]  Robert E. Mahony,et al.  Control of a quadrotor helicopter using visual feedback , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[24]  Pramod K. Varshney,et al.  Multisensor Data Fusion , 1997, IEA/AIE.

[25]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[26]  B. D. Lucas Generalized image matching by the method of differences , 1985 .

[27]  Sven Lange,et al.  A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments , 2009, 2009 International Conference on Advanced Robotics.

[28]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[29]  Kenzo Nonami,et al.  Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles , 2009, Robotics Auton. Syst..

[30]  Linear Optimal Filters and Predictors , 2008 .

[31]  Telework Supports General information , 2018, 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).

[32]  Andreas Zell,et al.  Flyphone: Visual Self-Localisation Using a Mobile Phone as Onboard Image Processor on a Quadrocopter , 2010, J. Intell. Robotic Syst..

[33]  N. Roy,et al.  Autonomous Navigation and Exploration of a Quadrotor Helicopter in GPS-denied Indoor Environments , 2009 .

[34]  David J. Fleet,et al.  Optical Flow Estimation , 2006, Handbook of Mathematical Models in Computer Vision.

[35]  Gaurav S. Sukhatme,et al.  Vision-based autonomous landing of an unmanned aerial vehicle , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[36]  Vijay Kumar,et al.  The GRASP Multiple Micro-UAV Testbed , 2010, IEEE Robotics & Automation Magazine.

[37]  Joseph K. Kearney,et al.  Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.