Deep learning for unmanned aerial vehicle transportation cost and path planning

Shilin Hu; Zonglin Li; Fangyi Wu; Hanyu Tian

doi:10.1117/12.2647539

23 August 2022 Deep learning for unmanned aerial vehicle transportation cost and path planning

Shilin Hu, Zonglin Li, Fangyi Wu, Hanyu Tian

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 1233021 (2022) https://doi.org/10.1117/12.2647539
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

With the rapid development of the unmanned aerial vehicle (UAV) industry and artificial intelligence industry, UAV has been used in the civil field. For civil UAVs, the distribution of logistics UAV is a hot research topic. Based on the UAV data set, the value function model of logistics UAV task planning is established by deep learning. It is found that there are obvious differences in value function for different deep learning methods. Moreover, it is also found that using the Random Forest Model has a good effect on the modeling of the value function. On this basis, the task planning model of logistics UAVs based on K-Means is established. In order to find an optimization algorithm suitable for the logistics system, this research uses an improved Genetic Algorithm, improved TS Algorithm, and Particle Swarm Optimization Algorithm to solve the model. By comparing three intelligent optimization algorithms, it is found that the improved TS Algorithm not only converges faster for this model but also has fast convergence for a large number of logistics task points by comparing the flight route and convergence curve of the objective function, which can meet the needs of largescale tasks in the actual logistics system.

Citation Download Citation

Shilin Hu, Zonglin Li, Fangyi Wu, and Hanyu Tian "Deep learning for unmanned aerial vehicle transportation cost and path planning", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 1233021 (23 August 2022); https://doi.org/10.1117/12.2647539

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