Path Planning for Delivery Drones Based on Deep Learning

DOI: https://doi.org/10.62517/jike.202504411

Author(s)

Yukai Zhang

Affiliation(s)

Vanke Meisha Academy, Shenzhen, China *Corresponding Author

Abstract

With the rapid development of e-commerce and the logistics industry, delivery drones, as a new type of logistics transportation tool, are gradually attracting widespread attention. Path planning is a key link for the efficient and safe execution of tasks by delivery drones. Deep learning, as an important branch in the field of artificial intelligence, provides new ideas and methods for the path planning of delivery drones. This paper explores the significance and challenges of path planning for delivery drones, analyzes the applicability of deep learning in path planning, studies the key technologies of path planning for delivery drones based on deep learning, including data collection and preprocessing, deep learning model construction, model training and optimization, etc., and looks forward to its application prospects. It aims to provide theoretical support and practical guidance for the development of path planning technology for delivery drones.

Keywords

Deep Learning; Delivery Drones; Path Planning; Logistics and Transportation

References

[1] Munoz, F., Holsapple, C. W., & Sasidharan, S. (2023). E-commerce. In Springer Handbook of Automation (pp. 1411-1430). Cham: Springer International Publishing. [2] Nurgaliev, I., Eskander, Y., & Lis, K. (2023). The use of drones and autonomous vehicles in logistics and delivery. Logistics and Transport, 57. [3] Li, Y., Liu, M., & Jiang, D. (2022). Application of unmanned aerial vehicles in logistics: a literature review. Sustainability, 14(21), 14473. [4] Luo, Y., Lu, J., Zhang, Y., Zheng, K., Qin, Q., He, L., & Liu, Y. (2022). Near-ground delivery drones path planning design based on BOA-TSAR algorithm. Drones, 6(12), 393. [5] Chronis, C., Anagnostopoulos, G., Politi, E., Garyfallou, A., Varlamis, I., & Dimitrakopoulos, G. (2023, June). Path planning of autonomous UAVs using reinforcement learning. In Journal of Physics: Conference Series (Vol. 2526, No. 1, p. 012088). IOP Publishing. [6] Shao, Q., Li, J., Li, R., Zhang, J., & Gao, X. (2022). Study of urban logistics drone path planning model incorporating service benefit and risk cost. Drones, 6(12), 418. [7] Modares, J., Ghanei, F., Mastronarde, N., & Dantu, K. (2017, May). Ub-anc planner: Energy efficient coverage path planning with multiple drones. In 2017 IEEE international conference on robotics and automation (ICRA) (pp. 6182-6189). IEEE. [8] Attenni, G., Arrigoni, V., Bartolini, N., & Maselli, G. (2023). Drone-based delivery systems: A survey on route planning. Ieee Access, 11, 123476-123504.