Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
https://grvc.us.es/bioinspired-landing-trajectory-sensor-dataset/.
References
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)
Corporate, F.: Smartbird (2011). https://www.festo.com/net/SupportPortal/Files/46270/Brosch_SmartBird_en_8s_RZ_110311_lo.pdf
Corporate, F.: Bionicflyingfox (2018). https://www.festo.com/net/SupportPortal/Files/492827/Festo_BionicFlyingFox_en.pdf
Croon, G., Perçin, M., Remes, B., Ruijsink, R., Wagter, C.: The DelFly (2016)
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)
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)
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)
Han, J., Lee, J., Kim, D.: Ornithopter modeling for flight simulation. In: 2008 International Conference on Control, Automation and Systems, pp. 1773–1777 (2008)
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)
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)
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)
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)
Lee, D.: General tau theory: evolution to date. Perception 38(6), 837 (2009)
Lee, D., Bootsma, R., Land, M., Regan, D., Gray, R.: Lee’s 1976 paper. Perception 38(6), 837–858 (2009)
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)
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)
Rebecq, H., Gehrig, D., Scaramuzza, D.: ESIM: an open event camera simulator. In: Conference on Robot Learning, pp. 969–982 (2018)
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)
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)
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)
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)
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)
Acknowledgements
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Rodríguez-Gómez, J.P., Gómez Eguíluz, A., Martínez-de Dios, J.R., Ollero, A. (2020). ROSS-LAN: RObotic Sensing Simulation Scheme for Bioinspired Robotic Bird LANding. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-36150-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-36150-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36149-5
Online ISBN: 978-3-030-36150-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)