Advertisement

Live-fly experimentation for pigeon-inspired obstacle avoidance of quadrotor unmanned aerial vehicles

  • Mengzhen Huo
  • Haibin DuanEmail author
  • Qing Yang
  • Daifeng Zhang
  • Huaxin Qiu
Research Paper
  • 8 Downloads

Abstract

In this paper, we applied a pigeon-inspired obstacle-avoidance model to the flight of quadrotor UAVs through environments with obstacles. Pigeons bias their flight direction by considering the largest gap and minimum required steering. Owing to the similarities between pigeon flocks and UAV swarms in terms of mission requirements, the pigeon-inspired obstacle-avoidance model is used to control a UAV swarm so that it can fly through a complex environment with multiple obstacles. The simulation and flight results illustrate the viability and superiority of pigeon-inspired obstacle avoidance for quadrotor UAVs.

Keywords

UAV swarm pigeon flock pigeon-inspired model obstacle-avoidance live-fly experimentation 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61425008, 61333004, 91648205).

References

  1. 1.
    Chen Z Y, Luo X Y, Dai B C. Design of obstacle avoidance system for micro-UAV based on binocular vision. In: Proceedings of International Conference on Industrial Informatics–Computing Technology, Intelligent Technology, Industrial Information Integration, Wuhan, 2017. 67–70Google Scholar
  2. 2.
    Meng G L, Pan H B. The application of ultrasonic sensor in the obstacle avoidance of quad-rotor UAV. In: Proceedings of Guidance, Navigation and Control Conference, Nanjing, 2016. 976–981Google Scholar
  3. 3.
    Yang Y, Wang T T, Chen L, et al. Stereo vision based obstacle avoidance strategy for quadcopter UAV. In: Proceedings of Chinese Control and Decision Conference, Shenyang, 2018Google Scholar
  4. 4.
    Peng X Z, Lin H Y, Dai J M. Path planning and obstacle avoidance for vision guided quadrotor UAV navigation. In: Proceedings of IEEE International Conference on Control and Automation, Kathmandu, 2016. 984–989Google Scholar
  5. 5.
    Zhao Y J, Zheng Z, Zhang X Y, et al. Q learning algorithm based UAV path learning and obstacle avoidance approach. In: Proceedings of Chinese Control Conference, Dalian, 2017. 3397–3402Google Scholar
  6. 6.
    Cekmez U, Ozsiginan M, Sahingoz O K. Multi colony ant optimization for UAV path planning with obstacle avoidance. In: Proceedings of International Conference on Unmanned Aircraft Systems, Arlington, 2016. 47–52Google Scholar
  7. 7.
    Norberg U M. Vertebrate flight: mechanics, physiology, morphology, ecology and evolution. Comp Biochem Phys Part A-Phy, 1990, 96: 529Google Scholar
  8. 8.
    Qiu H X, Wei C, Dou R, et al. Fully autonomous flying: from collective motion in bird flocks to unmanned aerial vehicle autonomous swarms. Sci China Inf Sci, 2015, 58: 128201CrossRefGoogle Scholar
  9. 9.
    Luo Q N, Duan H B. An improved artificial physics approach to multiple UAVs/UGVs heterogeneous coordination. Sci China Technol Sci, 2013, 56: 2473–2479CrossRefGoogle Scholar
  10. 10.
    Zhang T J. Unmanned aerial vehicle formation inspired by bird flocking and foraging behavior. Int J Autom Comput, 2018, 15: 402–416CrossRefGoogle Scholar
  11. 11.
    Baptista L, Trail P, Horblit H. Family columbidae. In: Handbook of the Birds of the World. Barcelona: Lynx Edicions, 1997Google Scholar
  12. 12.
    Lin H T, Ros I G, Biewener A A. Through the eyes of a bird: modelling visually guided obstacle flight. J R Soc Interface, 2014, 11: 20140239CrossRefGoogle Scholar
  13. 13.
    Moussaid M, Helbing D, Theraulaz G. How simple rules determine pedestrian behavior and crowd disasters. Proc Natl Acad Sci USA, 2011, 108: 6884–6888CrossRefGoogle Scholar
  14. 14.
    Qiu H X, Duan H B. Pigeon interaction mode switch-based UAV distributed flocking control under obstacle environments. ISA Trans, 2017, 71: 93–102CrossRefGoogle Scholar
  15. 15.
    Land M F, Collett T S. Chasing behaviour of houseflies (fannia canicularis). J Comp Physiol, 1974, 89: 331–357CrossRefGoogle Scholar
  16. 16.
    Warren W H, Fajen B R. Behavioral dynamics of visually guided locomotion. In: Coordination: Neural, Behavioral and Social Dynamics. Berlin: Springer, 2008. 45–75CrossRefGoogle Scholar
  17. 17.
    Foundation O S R. Robot operating system. https://doi.org/www.ros.org/about-ros/
  18. 18.
    Rokonuzzaman M, Amin M A A, Ahmed M H K M U, et al. Automatic vehicle identification system using machine learning and robot operating system (ROS). In: Proceedings of the 4th International Conference on Advances in Electrical Engineering (ICAEE 2017), Dhaka, 2017. 253–258CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mengzhen Huo
    • 1
  • Haibin Duan
    • 1
    Email author
  • Qing Yang
    • 1
  • Daifeng Zhang
    • 1
  • Huaxin Qiu
    • 1
  1. 1.Science and Technology on Aircraft Control Laboratory, School of Automation Science and Electrical EngineeringBeihang University (BUAA)BeijingChina

Personalised recommendations