Work

Trajectory Optimization Strategy for Networked UAVs Based on Deep Learning

Year: 2025

Type: article

Abstract: The rapid advancement of networked UAV technology has broadened its applications in logistics, disaster relief, agriculture, and military. However, precise trajectory planning in complex dynamic environments remains a key challenge. Traditional methods suffer from low computational efficiency and poor real-time response, limiting practical use. This paper proposes a deep learning-based trajectory optimization strategy for networked UAVs, integrating reinforcement learning and deep neural networks to autonomously learn environmental information and collaborative patterns. Simulation experiments in realistic scenarios validate its effectiveness: the method enhances trajectory planning accuracy and robustness while reducing computational complexity and improving real-time response. This research provides new theoretical and technical foundations for promoting networked UAV applications.

Source: Journal of Cases on Information Technology

DOI: 10.4018/jcit.390783

Author: Shouting Xin

Institution: Henan Polytechnic University

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Education Department of Henan Province