Constant Angular Velocity Regulation for Visually Guided Terrain Following

  • Huatian Wang
  • Qinbing Fu
  • Hongxin Wang
  • Jigen Peng
  • Shigang YueEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 559)


Insects use visual cues to control their flight behaviours. By estimating the angular velocity of the visual stimuli and regulating it to a constant value, honeybees can perform a terrain following task which keeps the certain height above the undulated ground. For mimicking this behaviour in a bio-plausible computation structure, this paper presents a new angular velocity decoding model based on the honeybee’s behavioural experiments. The model consists of three parts, the texture estimation layer for spatial information extraction, the motion detection layer for temporal information extraction and the decoding layer combining information from pervious layers to estimate the angular velocity. Compared to previous methods on this field, the proposed model produces responses largely independent of the spatial frequency and contrast in grating experiments. The angular velocity based control scheme is proposed to implement the model into a bee simulated by the game engine Unity. The perfect terrain following above patterned ground and successfully flying over irregular textured terrain show its potential for micro unmanned aerial vehicles’ terrain following.


Insect vision Flight control Angular velocity Terrain following 



This research is funded by the EU HORIZON 2020 project, STEP2DYNA (grant agreement No. 691154) and ULTRACEPT (grant agreement No. 778062); the National Natural Science Foundation of China (grant agreement No. 11771347).


  1. 1.
    Baird, E., Srinivasan, M.V., Zhang, S., Cowling, A.: Visual control of flight speed in honeybees. J. Exp. Biol. 208(20), 3895–3905 (2005)CrossRefGoogle Scholar
  2. 2.
    Cope, A.J., Sabo, C., Gurney, K., Vasilaki, E., Marshall, J.A.: A model for an angular velocity-tuned motion detector accounting for deviations in the corridor-centering response of the bee. PLoS Comput. Biol. 12(5), e1004887 (2016)CrossRefGoogle Scholar
  3. 3.
    Fleet, D.J.: Measurement of Image Velocity, vol. 169. Springer, Heidelberg (2012)zbMATHGoogle Scholar
  4. 4.
    Franceschini, N., Ruffier, F., Serres, J.: A bio-inspired flying robot sheds light on insect piloting abilities. Curr. Biol. 17(4), 329–335 (2007)CrossRefGoogle Scholar
  5. 5.
    Fu, Q., Hu, C., Peng, J., Yue, S.: Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation. Neural Netw. 106, 127–143 (2018)CrossRefGoogle Scholar
  6. 6.
    Fu, Q., Yue, S.: Modeling direction selective visual neural network with on and off pathways for extracting motion cues from cluttered background. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 831–838. Anchorage, AK, USA (2017)Google Scholar
  7. 7.
    Hassenstein, B., Reichardt, W.: Systemtheoretische analyse der zeit-, reihenfolgen-und vorzeichenauswertung bei der bewegungsperzeption des rüsselkäfers chlorophanus. Zeitschrift für Naturforschung B 11(9–10), 513–524 (1956)CrossRefGoogle Scholar
  8. 8.
    Heran, H., Lindauer, M.: Windkompensation und seitenwindkorrektur der bienen beim flug über wasser. Zeitschrift für vergleichende Physiologie 47(1), 39–55 (1963)CrossRefGoogle Scholar
  9. 9.
    Ibbotson, M.: Evidence for velocity-tuned motion-sensitive descending neurons in the honeybee. Proc. Roy. Soc. Lond. B Biol. Sci. 268(1482), 2195–2201 (2001)CrossRefGoogle Scholar
  10. 10.
    Ibbotson, M., Hung, Y.S., Meffin, H., Boeddeker, N., Srinivasan, M.: Neural basis of forward flight control and landing in honeybees. Sci. Rep. 7(1), 14591 (2017)CrossRefGoogle Scholar
  11. 11.
    Riabinina, O., Philippides, A.O.: A model of visual detection of angular speed for bees. J. Theor. Biol. 257(1), 61–72 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Ruffier, F., Franceschini, N.: Optic flow regulation: the key to aircraft automatic guidance. Robot. Auton. Syst. 50(4), 177–194 (2005)CrossRefGoogle Scholar
  13. 13.
    Ruffier, F., Franceschini, N.: Optic flow regulation in unsteady environments: a tethered MAV achieves terrain following and targeted landing over a moving platform. J. Intell. Robot. Syst. 79(2), 275–293 (2015)CrossRefGoogle Scholar
  14. 14.
    Seidl, R.A.: Die sehfelder und ommatidien divergenzwinkel von Arbeiterin, Königin und drohn der honigbiene (Apis mellifica). Ph.D. thesis (1982)Google Scholar
  15. 15.
    Serres, J.R., Masson, G.P., Ruffier, F., Franceschini, N.: A bee in the corridor: centering and wall-following. Naturwissenschaften 95(12), 1181 (2008)CrossRefGoogle Scholar
  16. 16.
    Serres, J.R., Ruffier, F.: Optic flow-based collision-free strategies: from insects to robots. Arthropod Struct. Dev. 46(5), 703–717 (2017)CrossRefGoogle Scholar
  17. 17.
    Srinivasan, M., Zhang, S., Lehrer, M., Collett, T.: Honeybee navigation en route to the goal: visual flight control and odometry. J. Exp. Biol. 199(1), 237–244 (1996)Google Scholar
  18. 18.
    Srinivasan, M., Zhang, S.: Visual control of honeybee flight. In: Lehrer, M. (ed.) Orientation and communication in arthropods, pp. 95–113. Springer, Heidelberg (1997). Scholar
  19. 19.
    Wang, H., Peng, J., Yue, S.: A directionally selective small target motion detecting visual neural network in cluttered backgrounds. IEEE Trans. Cybern. (to be published).
  20. 20.
    Wang, H., Peng, J., Baxter, P., Zhang, C., Wang, Z., Yue, S.: A model for detection of angular velocity of image motion based on the temporal tuning of the Drosophila. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11140, pp. 37–46. Springer, Cham (2018). Scholar
  21. 21.
    Yue, S., Rind, F.C.: Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement. IEEE Trans. Neural Netw. 17(3), 705–716 (2006)CrossRefGoogle Scholar
  22. 22.
    Zanker, J.M., Srinivasan, M.V., Egelhaaf, M.: Speed tuning in elementary motion detectors of the correlation type. Biol. Cybern. 80(2), 109–116 (1999)zbMATHCrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Huatian Wang
    • 1
  • Qinbing Fu
    • 1
    • 3
  • Hongxin Wang
    • 1
  • Jigen Peng
    • 2
    • 3
  • Shigang Yue
    • 1
    • 3
    Email author
  1. 1.The Computational Intelligence Lab (CIL), School of Computer ScienceUniversity of LincolnLincolnUK
  2. 2.School of Mathematics and Information ScienceGuangzhou UniversityGuangzhouChina
  3. 3.Machine Life and Intelligence Research CenterGuangzhou UniversityGuangzhouChina

Personalised recommendations