Research on UAV Path Planning Evolutionary Algorithm Based on Reinforcement Learning

Xu Chen

doi:10.47297/taposatWSP2633-456915.20250604

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Research on UAV Path Planning Evolutionary Algorithm Based on Reinforcement Learning

Volume 6, Issue 4

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Research on UAV Path Planning Evolutionary Algorithm Based on Reinforcement Learning
Research on UAV Path Planning Evolutionary Algorithm Based on Reinforcement Learning
Foshan University, Foshan, Guangdong China 528000
Vol. 6, Issue 4, Pages: 89-97(2025)
Published：22 April 2025
DOI：10.47297/taposatWSP2633-456915.20250604
Accepted：

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Xu Chen.Research on UAV Path Planning Evolutionary Algorithm Based on Reinforcement Learning[J].Theory and Practice of Science and Technology,2025,06(04):89-97.
DOI：

Xu Chen.Research on UAV Path Planning Evolutionary Algorithm Based on Reinforcement Learning[J].Theory and Practice of Science and Technology,2025,06(04):89-97. DOI： 10.47297/taposatWSP2633-456915.20250604.

摘要

Abstract

This paper presents an investigation into the integration of optimization algorithms with learning algorithms to enhance the performance of unmanned aerial vehicles (UAVs) in path planning. Specifically

an evolutionary algorithm is employed in conjunction with the reinforcement learning DQN model to improve its efficacy in UAV path planning. The evolutionary algorithm component focuses on population initialization methods and comparative analysis of several GWO algorithms. Subsequently

the DQN model training incorporates guidance from the evolutionary algorithm

demonstrating superior performance in simulated environments compared to the original training algorithm. Finally

a discussion and analysis of optimization algorithms and reinforcement learning algorithms in the path planning domain are provided

along with several research recommendations for future DQN model training.

关键词

Keywords

references

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