Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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UAV Autonomous Flight Obstacle Avoidance Technology

Authors
Shengyao Duan1, *
1School of Artificial Intelligence, Shenyang Normal University, Shenyang, Liaoning, China
*Corresponding author. Email: Henry91200@outlook.com
Corresponding Author
Shengyao Duan
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_55How to use a DOI?
Keywords
UAV; obstacle avoidance; path planning; environmental perception
Abstract

With the maturity of drone technology and its increasing application in agriculture, logistics, and even medical fields, the issue of its flight safety in low-altitude complex environments has become increasingly critical. Autonomous flight obstacle avoidance is the core technology to ensure the reliability of drones and the completion of their missions, and it is also the key to breakthroughs in existing technologies. This paper aims to systematically review drone autonomous obstacle avoidance technology. First, from the perspective of “how to make decisions”, the drone flight path planning algorithm is deeply analyzed, covering classic global and local planning algorithms as well as cutting-edge machine learning-based methods. Second, from the perspective of “how to see”, various sensor technologies used for environmental perception and their applications in ranging and obstacle recognition are sorted out in detail. In addition, this paper also summarizes the software and hardware platforms that carry the algorithms and sensors. This paper systematically reviews drone autonomous obstacle avoidance technology. By deeply analyzing the two core links of path planning and environmental perception, it summarizes the limitations of current technology and looks forward to future directions, providing researchers in related fields with a comprehensive technical map and valuable reference.

Copyright
© 2026 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.

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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_55How to use a DOI?
Copyright
© 2026 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.

Cite this article

risenwbib
TY  - CONF
AU  - Shengyao Duan
PY  - 2026
DA  - 2026/04/24
TI  - UAV Autonomous Flight Obstacle Avoidance Technology
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 502
EP  - 508
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_55
DO  - 10.2991/978-94-6239-648-7_55
ID  - Duan2026
ER  -