ROSS-LAN: RObotic Sensing Simulation Scheme for Bioinspired Robotic Bird LANding

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1093)


Aerial robotics is evolving towards the design of bioinspired platforms capable of resembling the behavior of birds and insects during flight. The development of perception algorithms for navigation of ornithopters requires sensor data information to evaluate and solve the limitations presented during the flight of these platforms. However, the payload constraints and hardware complexity of ornithopters hamper the sensor data acquisition. This paper focuses on the development of a multi-sensor simulator to retrieve the sensor information captured during the landing maneuvers of ornithopters. The landing trajectory is computed by using a bioinspired trajectory generator relying on tau theory. Further, a dataset of the sensor information records obtained during the simulation of several landing trajectories is publicly available online.


Tau theory Ornithopter Event-based cameras LiDAR 



This work was supported by the European Research Council as part of GRIFFIN ERC Advanced Grant 2017 (Action 788247) and the ARM-EXTEND project funded by the Spanish National RD plan (DPI2017-89790-R).


  1. 1.
    Benosman, R., Clercq, C., Lagorce, X., Ieng, S., Bartolozzi, C.: Event-based visual flow. IEEE Trans. Neural Netw. Learn. Syst. 25(2), 407–417 (2014)CrossRefGoogle Scholar
  2. 2.
  3. 3.
  4. 4.
    Croon, G., Perçin, M., Remes, B., Ruijsink, R., Wagter, C.: The DelFly (2016)Google Scholar
  5. 5.
    Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1–16 (2017)Google Scholar
  6. 6.
    Fei, F., Tu, Z., Yang, Y., Zhang, J., Deng, X.: Flappy hummingbird: an open source dynamic simulation of flapping wing robots and animals. arXiv preprint arXiv:1902.09628 (2019)
  7. 7.
    Folkertsma, G., Straatman, W., Nijenhuis, N., Venner, C., Stramigioli, S.: Robird: a robotic bird of prey. IEEE Robot. Autom. Mag. 24(3), 22–29 (2017)CrossRefGoogle Scholar
  8. 8.
    Han, J., Lee, J., Kim, D.: Ornithopter modeling for flight simulation. In: 2008 International Conference on Control, Automation and Systems, pp. 1773–1777 (2008)Google Scholar
  9. 9.
    Kaiser, J., Tieck, J., Hubschneider, C., Wolf, P., Weber, M., Hoff, M., Friedrich, A., Wojtasik, K., Roennau, A., Kohlhaas, R., et al.: Towards a framework for end-to-end control of a simulated vehicle with spiking neural networks. In: 2016 IEEE International Conference on SIMPAR, pp. 127–134 (2016)Google Scholar
  10. 10.
    Kendoul, F.: Four-dimensional guidance and control of movement using time-to-contact: application to automated docking and landing of unmanned rotorcraft systems. Int. J. Robot. Res. 33(2), 237–267 (2014)CrossRefGoogle Scholar
  11. 11.
    Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: IEEE/RSJ IROS 2004, vol. 3, pp. 2149–2154 (2004)Google Scholar
  12. 12.
    Kueng, B., Mueggler, E., Gallego, G., Scaramuzza, D.: Low-latency visual odometry using event-based feature tracks. In: IEEE/RSJ IROS 2016, pp. 16–23 (2016)Google Scholar
  13. 13.
    Lee, D.: General tau theory: evolution to date. Perception 38(6), 837 (2009)CrossRefGoogle Scholar
  14. 14.
    Lee, D., Bootsma, R., Land, M., Regan, D., Gray, R.: Lee’s 1976 paper. Perception 38(6), 837–858 (2009)CrossRefGoogle Scholar
  15. 15.
    Mueggler, E., Rebecq, H., Gallego, G., Delbruck, T., Scaramuzza, D.: The event-camera dataset and simulator: event-based data for pose estimation, visual odometry, and SLAM. Int. J. Robot. Res. 36(2), 142–149 (2017)CrossRefGoogle Scholar
  16. 16.
    Pfeiffer, A., Lee, J., Han, J., Baier, H.: Ornithopter flight simulation based on flexible multi-body dynamics. J. Bionic Eng. 7(1), 102–111 (2010)CrossRefGoogle Scholar
  17. 17.
    Rebecq, H., Gehrig, D., Scaramuzza, D.: ESIM: an open event camera simulator. In: Conference on Robot Learning, pp. 969–982 (2018)Google Scholar
  18. 18.
    Rebecq, H., Horstschäfer, T., Gallego, G., Scaramuzza, D.: EVO: a geometric approach to event-based 6-dof parallel tracking and mapping in real time. IEEE Robot. Autom. Lett. 2(2), 593–600 (2017)CrossRefGoogle Scholar
  19. 19.
    Rohmer, E., Singh, S.P., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: IEEE/RSJ IROS 2013, pp. 1321–1326 (2013)Google Scholar
  20. 20.
    Shah, S., Dey, D., Lovett, C., Kapoor, A.: AirSim: high-fidelity visual and physical simulation for autonomous vehicles. In: Field and Service Robotics, pp. 621–635 (2018)Google Scholar
  21. 21.
    Vasco, V., Glover, A., Bartolozzi, C.: Fast event-based harris corner detection exploiting the advantages of event-driven cameras. In: IEEE/RSJ IROS 2016, pp. 4144–4149 (2016)Google Scholar
  22. 22.
    Vidal, A., Rebecq, H., Horstschaefer, T., Scaramuzza, D.: Ultimate SLAM? Combining events, images, and IMU for robust visual SLAM in HDR and high-speed scenarios. IEEE Robot. Autom. Lett. 3(2), 994–1001 (2018)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.GRVC-Robotics LabUniversity of SevilleSevilleSpain

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