DOI QR코드

DOI QR Code

Optical Flow Based Collision Avoidance of Multi-Rotor UAVs in Urban Environments

  • Yoo, Dong-Wan (Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology) ;
  • Won, Dae-Yeon (Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology) ;
  • Tahk, Min-Jea (Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2011.04.29
  • Accepted : 2011.09.08
  • Published : 2011.09.30

Abstract

This paper is focused on dynamic modeling and control system design as well as vision based collision avoidance for multi-rotor unmanned aerial vehicles (UAVs). Multi-rotor UAVs are defined as rotary-winged UAVs with multiple rotors. These multi-rotor UAVs can be utilized in various military situations such as surveillance and reconnaissance. They can also be used for obtaining visual information from steep terrains or disaster sites. In this paper, a quad-rotor model is introduced as well as its control system, which is designed based on a proportional-integral-derivative controller and vision-based collision avoidance control system. Additionally, in order for a UAV to navigate safely in areas such as buildings and offices with a number of obstacles, there must be a collision avoidance algorithm installed in the UAV's hardware, which should include the detection of obstacles, avoidance maneuvering, etc. In this paper, the optical flow method, one of the vision-based collision avoidance techniques, is introduced, and multi-rotor UAV's collision avoidance simulations are described in various virtual environments in order to demonstrate its avoidance performance.

References

  1. Bradski, G. R. and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. Sebastopol: O'Reilly Media. pp. 435-444.
  2. Castillo, P., Lozano, R., and Dzul, A. (2005). Stabilization of a mini rotorcraft with four rotors. IEEE Control Systems, 25, 45-55.
  3. Muratet, L., Doncieux, S., Briere, Y., and Meyer, J. A. (2005). A contribution to vision-based autonomous helicopter flight in urban environments. Robotics and Autonomous Systems, 50, 195-209. https://doi.org/10.1016/j.robot.2004.09.017
  4. Muratet, L., Doncieux, S., and Meyer, J. A. (2004). A biomimetic reactive navigation system using the optical flow for a rotary-wing UAV in urban environment. Proceedings of the 35th International Symposium on Robotics, Paris, France.
  5. Park, B., Won, D., Huh, S., and Tahk, M. (2008). Obstacle avoidance for UAVs using optical flow in urban environment. CASS, Seoul, Korea.
  6. Salazar-Cruz, S., Kendoul, F., Lozano, R., and Fantoni, I. (2008). Real-time stabilization of a small three-rotor aircraft. IEEE Transactions on Aerospace and Electronic Systems, 44, 783-794. https://doi.org/10.1109/TAES.2008.4560220
  7. Sonka, M., Hlavac, V., and Boyle, R. (2008). Image Processing, Analysis, and Machine Vision. 3rd ed. Toronto: Thompson Learning. pp. 757-771.
  8. Souhila, K. and Karim, A. (2007). Optical flow based robot obstacle avoidance. International Journal of Advanced Robotic Systems, 4, 13-16. https://doi.org/10.5772/5704
  9. Stevens, B. L. and Lewis, F. L. (2003). Aircraft Control and Simulation. 2nd ed. Hoboken: John Wiley & Sons.
  10. Yoo, D. W., Oh, H. D., Won, D. Y., and Tahk, M. J. (2010). Dynamic modeling and control system design for Tri-Rotor UAV. Proceedings of the 3rd International Symposium on Systems and Control in Aeronautics and Astronautics, Harbin, China. pp. 762-767.

Cited by

  1. Attitude and Altitude Control of Trirotor UAV by Using Adaptive Hybrid Controller vol.2016, 2016, https://doi.org/10.1155/2016/6459891
  2. Image Segmentation-Based Unmanned Aerial Vehicle Safe Navigation vol.14, pp.7, 2017, https://doi.org/10.2514/1.I010457
  3. Flow Actuation by DC Surface Discharge Plasma Actuator in Different Discharge Modes vol.16, pp.3, 2015, https://doi.org/10.5139/IJASS.2015.16.3.339
  4. A Composite Guidance Strategy for Optical Flow based UAV Navigation vol.47, pp.1, 2014, https://doi.org/10.3182/20140313-3-IN-3024.00151
  5. Hardware Implementation of KLT Tracker for Real-Time Intruder Detection and Tracking Using On-board Camera pp.2093-2480, 2019, https://doi.org/10.1007/s42405-018-0131-2