Detection and Implementation Autonomous Target Tracking with a Quadrotor AR.Drone

K. Boudjit, C. Larbes

Abstract

Nowadays, There Are Many Robotic Applications Being Developed to Do Tasks Autonomously without Any Interactions or Commands from Human, Therefore, Developing a System Which Enables a Robot to Do Surveillance Such as Detection and Tracking of a Moving Object Will Lead Us to More Advanced Tasks Carried out by Robots in the Future, AR.Drone Is a Flying Robot Platform That Is Able to Take Role as UAV (Unmanned Aerial Vehicle), Usage of Computer Vision Algorithm Such as Hough Transform Makes It Possible for Such System to Be Implemented on AR.Drone, in This Research, the Developed Algorithm Is Able to Detect and Track an Object with Certain Shape, then the Algorithm Is Successfully Implemented on AR.Drone Quadcopter for Detection and Tracking.

References

  1. Engel, J. J. Sturn, and D. Gremers. (2012). Camera-Based Navigation of a Low-Cost Quadrocopter In Proc. Of the International Conference on Intelligent Robot Systems (IROS).
  2. Dijkshoorn, N. and A. Visser J. (2011). Integrating Sensor and Motion to Localize an Autonomous AR.Drone Universiteit Van Amsterdam, International Journal of Micro Air Vehicles.
  3. Linz, A and A. Ruckelshausen. (2012). Educational Robotic Platform “Zero2Nine” for Autonomous Navigation and Tracking Based on Imaging Sensor Systems. in 3rd International Conference on Machine Control & Guidance, March 27-29, 2012.
  4. Grade.V. H. H. Bulthoff, and P. Robuffo Giordano. (2012). On-Board Velocity Estimation and ClosedLoop Control of a Quadrotor UAV Based on Optical Flow, In 2012 IEEE Int, Conf, On Robotics and Automation, pages 491-497, St, Paul, MN, May 2012.
  5. Gilluda, J. H., and C. J. Tomlin. (2012). Guaranteed Safe Online Learning via reachability: tracking a Ground Target Using a Quadrotor. In Robotics and Automations (ICRA), 2012 IEEE International Conference on, pages 2723-2730.
  6. Grale. V, M. Riedel. H. H. Bulthoff, P. Robuffo Giodano, and A. franchi. (2013). The Telekyb framework for a Modular and Extensible ROS-Based Quadrotor Control. In 6th European Conference on Mobile Robots, Barcelona, Spain, Sep 2013.
  7. Lim. H. J. Park, D. Lee, and H. J. Kim. (2012). Build Your Own Quadrotor: Open-Source Projects on Unmanned Aerial Vehicles. IEEE Robotics & Automation Magazine, 19(3): 33-45, 2012.
  8. Mahong. R. V. Kumar, and P. Corke. (2012). Multirotor Aerial Vehicles Modeling Estimation and Control of Quadrotor. IEEE Robotics & Automation Magazine 19(3): 20-32, 2012.
  9. AutonomyLab (2014). https://github.com/AutonomyLab/ ardrone_autonomy/, last accessed: 2015-01-26.
  10. Parrot (2014). http://ardrone2.parrot.com/, last accessed : 2015-02-15.
  11. Ros (2015). www.ros.org/, Last accessed: 2015-03-13.
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Paper Citation


in Harvard Style

Boudjit K. and Larbes C. (2015). Detection and Implementation Autonomous Target Tracking with a Quadrotor AR.Drone . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-123-6, pages 223-230. DOI: 10.5220/0005523102230230


in Bibtex Style

@conference{icinco15,
author={K. Boudjit and C. Larbes},
title={Detection and Implementation Autonomous Target Tracking with a Quadrotor AR.Drone},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2015},
pages={223-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005523102230230},
isbn={978-989-758-123-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Detection and Implementation Autonomous Target Tracking with a Quadrotor AR.Drone
SN - 978-989-758-123-6
AU - Boudjit K.
AU - Larbes C.
PY - 2015
SP - 223
EP - 230
DO - 10.5220/0005523102230230