Abstract
A critical challenge in deploying unmanned aerial vehicles (UAVs) for autonomous tasks is their ability to navigate in an unknown environment. This paper introduces a novel vision-depth fusion approach for autonomous navigation on nano-UAVs. We combine the visual-based PULP-Dronet [1] convolutional neural network for semantic information extraction, i.e., serving as the global perception, with 8\(\times \)8 px depth maps for close-proximity maneuvers, i.e., the local perception. When tested in-field, our integration strategy highlights the complementary strengths of both visual and depth sensory information. We achieve a 100% success rate over 15 flights in a complex navigation scenario, encompassing straight pathways, static obstacle avoidance, and 90\(^\circ \) turns.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Niculescu, V., Lamberti, L., Conti, F., Benini, L., Palossi, D.: Improving autonomous nano-drones performance via automated end-to-end optimization and deployment of dnns. IEEE J. Emerg. Selected Topics Circ. Syst. 11(4), 548–562 (2021)
Anderson, M.J., et al.: A bio-hybrid odor-guided autonomous palm-sized air vehicle. Bioinsp. Biomimet. 16(2), 026002 (2020)
Awasthi, S., et al.: UAVs for industries and supply chain management. ArXiv arxiv:2212.03346 (2022)
Müller, H., et al.: Robust and efficient depth-based obstacle avoidance for autonomous miniaturized UAVs. IEEE Trans. Rob. 39(6), 4935–4951 (2023)
Crupi, L., et al.: Sim-to-real vision-depth fusion CNNs for robust pose estimation aboard autonomous nano-quadcopters. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7711–7717 (2023)
Palossi, D., et al.: An energy-efficient parallel algorithm for real-time near-optimal uav path planning. In: Proceedings of the ACM International Conference on Computing Frontiers, ser. CF 2016, pp. 392–397. Association for Computing Machinery, New York (2016)
McGuire, K., et al.: A comparative study of bug algorithms for robot navigation. Robot. Auton. Syst. 121, 103261 (2019)
Lamberti, L.: Bio-inspired autonomous exploration policies with CNN-based object detection on nano-drones. In: Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1–6 (2023)
Pourjabar, M., et al.: Multi-sensory anti-collision design for autonomous nano-swarm exploration. In: 2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS), pp. 1–5 (2023)
Acknowledgments
We thank D. Palossi and D. Christodoulou for their contribution to this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lamberti, L., Rutishauser, G., Conti, F., Benini, L. (2024). Combining Local and Global Perception for Autonomous Navigation on Nano-UAVs. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-76424-0_51
Download citation
DOI: https://doi.org/10.1007/978-3-031-76424-0_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-76423-3
Online ISBN: 978-3-031-76424-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)