Abstract
In this paper, the Politecnico di Milano solutions proposed for the Leonardo Drone Contest (LDC) are presented. The Leonardo Drone Contest is an annual autonomous drone competition among universities, which has already seen the conclusion of its second edition. In each edition, the participating teams were asked to design and build an autonomous multicopter, capable of accomplishing complex tasks in an indoor urban-like environment. To reach this goal, the designed systems should be capable of navigating in a Global Navigation Satellite System (GNSS)-denied environment with autonomous decision making, online planning and collision avoidance capabilities. In this light, the authors describe the first two editions of the competition, i.e., their rules, objectives and overview of the proposed solutions. While the first edition is presented as relevant for the experience and takeaways acquired from it, the second edition solution is analyzed in detail, providing both the simulation and experimental results obtained.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Data Availability
The manuscript has no associated data.
References
Alphapilot AI Drone Innovation Challenge. https://www.lockheedmartin.com/en-us/news/events/ai-innovation-challenge.html. Accessed 28 June 2022
ANT-X website. https://antx.it/. Accessed 28 June 2022
Leonardo Drone Contest autonomous drone competition. https://www.leonardo.com/it/innovation-technology/open-innovation/drone-contest. Accessed 17 Jan 2022
Mohamed Bin Zayed International Robotics Challenge (MBZIRC). https://www.mbzirc.com/. Accessed 17 Jan 2022
PX4 documentation. https://docs.px4.io/v1.12/en/. Accessed 06 May 2022
Achtelik, M., Bachrach, A., He, R., Prentice, S., Roy, N.: Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments. Unmanned Systems Technology XI 7332, 733219 (2009)
Basilico, N.: Recent trends in robotic patrolling. Current Robotics Reports pp. 1–12 (2022)
Beul, M., Bultmann, S., Rochow, A., Rosu, R.A., Schleich, D., Splietker, M., Behnke, S.: Visually guided balloon popping with an autonomous MAV at MBZIRC 2020. IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) pp. 34–41 (2020)
Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R.: Receding horizon "next-best-view" planner for 3D exploration. IEEE International Conference on Robotics and Automation (ICRA) pp. 1462–1468 (2016)
Blösch, M., Weiss, S., Scaramuzza, D., Siegwart, R.: Vision based MAV navigation in unknown and unstructured environments. 2010 IEEE International Conference on Robotics and Automation pp. 21–28 (2010)
De Wagter, C., Paredes-Vallés, F., Sheth, N., de Croon, G.: The artificial intelligence behind the winning entry to the 2019 AI robotic racing competition. arXiv preprint arXiv:2109.14985 (2021)
De Wagter, C., Paredes-Vallés, F., Sheth, N., de Croon, G.: Learning fast in autonomous drone racing. Nature Machine Intelligence 3(10), 923–923 (2021)
Dias, J., Althoefer, K., Lima, P.U.: Robot competitions: What did we learn? IEEE Robotics & Automation Magazine 23(1), 16–18 (2016)
Doitsidis, L., Weiss, S., Renzaglia, A., Achtelik, M., Kosmatopoulos, E., Siegwart, R., Scaramuzza, D.: Optimal surveillance coverage for teams of micro aerial vehicles in GPS-denied environments using onboard vision. Autonomous Robots 33, 173–188 (2012)
Engel, J., Sturm, J., Cremers, D.: Accurate figure flying with a quadrocopter using onboard visual and inertial sensing. Proceedings of the Workshop on Visual Control of Mobile Robots (ViCoMoR) at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012)
Engel, J., Sturm, J., Cremers, D.: Scale-aware navigation of a low-cost quadrocopter with a monocular camera. Robotics and Autonomous Systems 62(11), 1646–1656 (2014)
Faessler, M., Fontana, F., Forster, C., Mueggler, E., Pizzoli, M., Scaramuzza, D.: Autonomous, vision-based flight and live dense 3D mapping with a quadrotor micro aerial vehicle. J. Field Rob. 33(4), 431–450 (2016)
Foehn, P., Brescianini, D., Kaufmann, E., Cieslewski, T., Gehrig, M., Muglikar, M., Scaramuzza, D.: Alphapilot: Autonomous drone racing. Autonomous Robots pp. 1–14 (2021)
Foehn, P., Romero, A., Scaramuzza, D.: Time-optimal planning for quadrotor waypoint flight. Science Robotics 6(56), eabh1221 (2021)
Forster, C., Zhang, Z., Gassner, M., Werlberger, M., Scaramuzza, D.: SVO: Semidirect visual odometry for monocular and multicamera systems. IEEE Transactions on Robotics 33(2), 249–265 (2016)
Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: Dense 3D reconstruction in real-time. IEEE Intelligent Vehicles Symposium (IV) pp. 963–968 (2011). https://doi.org/10.1109/IVS.2011.5940405
Giubilato, R., Chiodini, S., Pertile, M., Debei, S.: An evaluation of ROS-compatible stereo visual SLAM methods on a nVidia Jetson TX2. Measurement (2019). https://doi.org/10.1016/j.measurement.2019.03.038
González-Banos, H.: A randomized art-gallery algorithm for sensor placement. Proceedings of the seventeenth annual symposium on Computational geometry pp. 232–240 (2001)
Hong, W., Zhou, C., Tian, Y.: Robust Monte Carlo Localization for humanoid soccer robot. IEEE/ASME International Conference on Advanced Intelligent Mechatronics pp. 934–939 (2009). https://doi.org/10.1109/AIM.2009.5229889
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots (2013)
Hoshino, S., Ishiwata, T., Ueda, R.: Optimal patrolling methodology of mobile robot for unknown visitors. Advanced Robotics 30(16), 1072–1085 (2016)
Hoshino, S., Ugajin, S., Ishiwata, T.: Patrolling robot based on bayesian learning for multiple intruders. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 603–609 (2015)
Labbé, M., Michaud, F.: Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Transactions on Robotics 29(3), 734–745 (2013). https://doi.org/10.1109/TRO.2013.2242375
Labbé, M., Michaud, F.: RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. Journal of Field Robotics 36(2), 416–446 (2019)
LaValle, S.M.: Planning algorithms. Cambridge University Press, Cambridge, U.K. (2006)
Lenz, C., Schwarz, M., Rochow, A., Razlaw, J., Periyasamy, A.S., Schreiber, M., Behnke, S.: Autonomous wall building with a UGV-UAV team at MBZIRC 2020. IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) pp. 189–196 (2020)
Mohta, K., Watterson, M., Mulgaonkar, Y., Liu, S., Qu, C., Makineni, A., Saulnier, K., Sun, K., Zhu, A., Delmerico, J., et al.: Fast, autonomous flight in GPS-denied and cluttered environments. Journal of Field Robotics 35(1), 101–120 (2018)
Moon, H., Martínez-Carranza, J., Cieslewski, T., Faessler, M., Falanga, D., Simovic, A., Scaramuzza, D., Li, S., Ozo, M.M.O.I., de Wagter, C., de Croon, G.C., Hwang, S., Jung, S., Shim, H., Kim, H., Park, M., Au, T.C., Kim, S.J.: Challenges and implemented technologies used in autonomous drone racing. Intelligent Service Robotics 12, 137–148 (2019)
Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. IEEE Transactions on Robotics 31(5), 1147–1163 (2015)
Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., Siegwart, R.: A UAV system for inspection of industrial facilities. 2013 IEEE Aerospace Conference pp. 1–8 (2013)
Oleynikova, H., Burri, M., Taylor, Z., Nieto, J., Siegwart, R., Galceran, E.: Continuous-time trajectory optimization for online UAV replanning. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 5332–5339 (2016). https://doi.org/10.1109/IROS.2016.7759784
Oleynikova, H., Lanegger, C., Taylor, Z., Pantic, M., Millane, A., Siegwart, R., Nieto, J.: An open-source system for vision-based micro-aerial vehicle mapping, planning, and flight in cluttered environments. Journal of Field Robotics 37(4), 642–666 (2020)
Rezende, A., Miranda, V.R., Rezeck, P.A., Azpúrua, H., Santos, E.R., Gonçalves, V.M., Macharet, D.G., Freitas, G.M.: An integrated solution for an autonomous drone racing in indoor environments. Intel. Serv. 14(5), 641–661 (2021)
Roggi, G., Meraglia, S., Lovera, M.: Leonardo Drone Contest 2021: Politecnico di Milano team architecture. International Conference on Unmanned Aircraft Systems (ICUAS) pp. 191–196 (2022)
Sampedro, C., Bavle, H., Rodríguez-Ramos, A., Carrio, A., Fernández, R.A.S., Sanchez-Lopez, J.L., Campoy, P.: A fully-autonomous aerial robotic solution for the 2016 international micro air vehicle competition. 2017 International conference on unmanned aircraft systems (ICUAS) pp. 989–998 (2017)
Santamaria-Navarro, A., Loianno, G., Solà, J., Kumar, V., Andrade-Cetto, J.: Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors. Autonomous Robots 42(6), 1263–1280 (2018)
Scaramuzza, D., Achtelik, M., Doitsidis, L., Friedrich, F., Kosmatopoulos, E., Martinelli, A., Achtelik, M., Chli, M., Chatzichristofis, S., Kneip, L., et al.: Vision-controlled micro flying robots: from system design to autonomous navigation and mapping in GPS-denied environments. IEEE Robotics & Automation Magazine 21(3), 26–40 (2014)
Schedl, D.C., Kurmi, I., Bimber, O.: An autonomous drone for search and rescue in forests using airborne optical sectioning. Science Robotics 6(55) (2021)
Schmid, K., Lutz, P., Tomić, T., Mair, E., Hirschmüller, H.: Autonomous vision-based micro air vehicle for indoor and outdoor navigation. Journal of Field Robotics 31(4), 537–570 (2014)
Sciavicco, L., Siciliano, B.: Modelling and control of robot manipulators. Springer Science & Business Media, Heidelberg (2001)
Semsch, E., Jakob, M., Pavlicek, D., Pechoucek, M.: Autonomous UAV surveillance in complex urban environments. IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology 2, 82–85 (2009)
Sousa, A., Costa, P., Moreira, A., Carvalho, A.: Self localization of an autonomous robot: using an EKF to merge odometry and vision based landmarks. IEEE Conference on Emerging Technologies and Factory Automation 1, 7–233 (2005). https://doi.org/10.1109/ETFA.2005.1612524
Stronger, D., Stone, P.: A comparison of two approaches for vision and self-localization on a mobile robot. IEEE International Conference on Robotics and Automation pp. 3915–3920 (2007). https://doi.org/10.1109/ROBOT.2007.364079
Ulrich, I., Borenstein, J.: VFH*: Local obstacle avoidance with look-ahead verification. IEEE International Conference on Robotics and Automation (ICRA) 3, 2505–2511 (2000)
Vanneste, S., Bellekens, B., Weyn, M.: 3DVFH+: Real-time three-dimensional obstacle avoidance using an octomap. Proceedings of the 1st International Workshop on Model-Driven Robot Software Engineering (1319), 91–102 (2014)
Vrba, M., Stasinchuk, Y., Báča, T., Spurnỳ, V., Petrlík, M., Heřt, D., Žaitlík, D., Saska, M.: Autonomous capture of agile flying objects using UAVs: The MBZIRC 2020 challenge. Robotics and Autonomous Systems 149, 103970 (2022)
Walter, V., Spurný, V., Petrlík, M., Báča, T., Žaitlík, D., Saska, M.: Extinguishing of ground fires by fully autonomous UAVs motivated by the MBZIRC 2020 competition. International Conference on Unmanned Aircraft Systems (ICUAS) pp. 787–793 (2021). 10.1109/ICUAS51884.2021.9476723
Zhou, B., Gao, F., Wang, L., Liu, C., Shen, S.: Robust and efficient quadrotor trajectory generation for fast autonomous flight. IEEE Robotics and Automation Letters 4(4), 3529–3536 (2019). https://doi.org/10.1109/LRA.2019.2927938
Acknowledgements
The authors thank the spin-off company of Politecnico di Milano ANT-X for the collaboration in the development of the hardware platforms. The authors thank prof. Matteo Matteucci of Politecnico di Milano for the help in the conceptualization of the solution.
Funding
Open access funding provided by Politecnico di Milano within the CRUI-CARE Agreement. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All authors contributed to the research, development, and testing of the proposed systems. Gabriele Roggi is the author of the proposed software architectures and contributed to the development of the hardware platforms. Salvatore Meraglia contributed to the preliminary design of the solution and to the experimental activities. Marco Lovera is the head of the Aerospace Systems and Control Laboratory of Politecnico di Milano, and he provided us with the necessary guidance, funding and final proofreading. The first draft of the manuscript was written by Gabriele Roggi, and all authors commented on previous versions of the submitted manuscript.
Corresponding author
Ethics declarations
Ethics Approval and Consent to Participate
All applicable institutional and national guidelines were followed.
Consent for Publication
Informed consent was obtained from all the co-authors of this publication.
Competing Interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Roggi, G., Meraglia, S. & Lovera, M. Leonardo Drone Contest Autonomous Drone Competition: Overview, Results, and Lessons Learned from Politecnico di Milano Team. J Intell Robot Syst 108, 23 (2023). https://doi.org/10.1007/s10846-023-01855-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10846-023-01855-w