SCIENCE CHINA Information Sciences, Volume 62 , Issue 5 : 052201(2019) https://doi.org/10.1007/s11432-018-9576-x

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

More info
  • ReceivedJun 26, 2018
  • AcceptedJul 10, 2018
  • PublishedApr 3, 2019



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


[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--70. Google Scholar

[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, 2017. 976--981. Google Scholar

[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, 2018. Google Scholar

[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--989. Google Scholar

[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--3402. Google Scholar

[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--52. Google Scholar

[7] Vertebrate Flight: Mechanics, Physiology, Morphology, Ecology and Evolution. Comp Biochem Physiol Part A-Physiol, 1990, 96: 529 CrossRef Google Scholar

[8] Qiu H X, Wei C, Dou R. Fully autonomous flying: from collective motion in bird flocks to unmanned aerial vehicle autonomous swarms. Sci China Inf Sci, 2015, 58: 1-3 CrossRef Google Scholar

[9] Luo Q N, Duan H B. An improved artificial physics approach to multiple UAVs/UGVs heterogeneous coordination. Sci China Technol Sci, 2013, 56: 2473-2479 CrossRef Google Scholar

[10] Zhang T J. Unmanned Aerial Vehicle Formation Inspired by Bird Flocking and Foraging behavior. Int J Autom Comput, 2018, 15: 402-416 CrossRef Google Scholar

[11] Baptista L, Trail P, Horblit H. Family Columbidae. In: Handbook of the birds of the world. Barcelona: Lynx Edicions, 1997. Google Scholar

[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: 20140239-20140239 CrossRef PubMed Google Scholar

[13] Moussaid M, Helbing D, Theraulaz G. How simple rules determine pedestrian behavior and crowd disasters. Proc Natl Acad Sci USA, 2011, 108: 6884-6888 CrossRef PubMed ADS arXiv Google Scholar

[14] Qiu H, Duan H. Pigeon interaction mode switch-based UAV distributed flocking control under obstacle environments.. ISA Trans, 2017, 71: 93-102 CrossRef PubMed Google Scholar

[15] Land M F, Collett T S. Chasing behaviour of houseflies (Fannia canicularis). J Comp Physiol, 1974, 89: 331-357 CrossRef Google Scholar

[16] Warren W H, Fajen B R. Behavioral dynamics of visually guided locomotion. In: Coordination: Neural, Behavioral and Social Dynamics, 2008. 45--75. Google Scholar

[17] Foundation O S R. Robot operating system. http://www.ros.org/about-ros/. Google Scholar

[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--258. Google Scholar