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

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Review on Autonomous Robot Mobility Based on Visual Deep Learning

Authors
Xun He1, Tianyi Xu2, Lo San Yuan3, *
1College of Engineering, HuaZhong Agricultural University, Wuhan, 430070, China
2Mechanical Engineering and Automation, Hubei Polytechnic University, Huangshi, 435003, China
3Faculty of Engineering, The University of Hong Kong, Hong Kong, China
*Corresponding author. Email: losan.yuan@outlook.com
Corresponding Author
Lo San Yuan
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_35How to use a DOI?
Keywords
Autonomous robot movement; deep visual learning; automatic obstacle avoidance; path optimization
Abstract

The topic related to dynamic autonomous navigation in complex environments has become more popular in the robotics research area. It is very crucial for mobile robotics to have automatic obstacle avoidance and path planning. Traditional methods, such as simultaneous localization and mapping (SLAM) technology, for instance, typically rely on sensors such as cameras, LiDAR, and ultrasonic sensors, in order to accomplish local obstacle avoidance. While these approaches demonstrate high reliability, they may be constrained by limitations in flexibility, detection efficiency, and real-time performance. In recent years, numerous emerging technologies about artificial intelligence (AI) and deep learning are introduced and applied in related aspects. By combining those state-of-the-art techniques and machine vision, the capabilities of perception and motion decision making for robotics are dramatically enhanced. In this article, the application of deep learning models, such as convolutional neural networks, to the problem of path planning, including obstacle avoidance algorithms of mobile robots will be mainly discussed. Key issues and challenges in current research and potential solutions along with future development trends will be determined at the end.

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_35How 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  - Xun He
AU  - Tianyi Xu
AU  - Lo San Yuan
PY  - 2026
DA  - 2026/04/24
TI  - Review on Autonomous Robot Mobility Based on Visual Deep Learning
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 321
EP  - 328
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_35
DO  - 10.2991/978-94-6239-648-7_35
ID  - He2026
ER  -