UAV-Aided FSO Communication Alignment with YOLOv5 and PID Control Optimized by Deep Reinforcement Learning

Authors

  • Yulin Ding College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
  • Mingyi Gu College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
  • Yixiang Li College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
  • Tong Wei College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
  • Feiyue Wang College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
  • Songshan Tian College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China

DOI:

https://doi.org/10.5912/bsrc4q68

Abstract

This paper presents a solution for aligning medium and short-distance FSO communication links using YOLOv5 for UAV detection and DDPG for PID parameter tuning. Implemented and verified through simulations and physical tests, the system achieves robust and stable alignment under various UAV trajectories. Results show enhanced communication quality.

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Published

2025-05-09

Issue

Vol. 30 No. 3 (2025): Volume 30, Issue 3 2025

Section

Article