Application of Artificial Intelligence on UAV Path Planning

DOI

10.2991/978-94-6463-821-9_104How to use a DOI?

Keywords

Drone Path Planning; Reinforcement Learning; Neural Network; Artificial Intelligence

Abstract

With the rapid development of artificial intelligence, its application effect in the field of UAV path planning is becoming more and more significant. It plays a great role in path search optimization, environment perception modelling, dynamic environment adaptation, and multi-UAV collaboration. Reinforcement learning algorithms train UAVs to fly stably in complex environments by minimizing the reward function as well as obstacle avoidance. LSTM (Long Short Term Memory Network) makes use of processing dynamic changes and being more adapt to complex environment. Machine-to-machine collisions are also avoided by realizing multi-machine collaboration through multi-intelligent body reinforcement learning. However, due to the limitations of the current technology, there are still many challenges, such as high algorithm complexity and limited computational resources. This paper introduces the development of AI and describes the application and effect of AI in UAV path planning. In the future, with the continuous improvement of arithmetic power and algorithmic innovation, UAV path planning based on AI will be more autonomous in complex environments, improve the endurance time, and realize the overall task in the state of multi-machine cooperation. Optimal completion of the overall mission under the state of multi-aircraft cooperation.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Shangjing Zhang
PY  - 2025
DA  - 2025/08/31
TI  - Application of Artificial Intelligence on UAV Path Planning
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 1078
EP  - 1089
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_104
DO  - 10.2991/978-94-6463-821-9_104
ID  - Zhang2025
ER  -