Progress of Deep Learning in Path Planning for Mobile Robots

DOI

10.2991/978-94-6463-821-9_54How to use a DOI?

Keywords

Mobile Robot; Path Planning; Dynamic Environment; Deep Learning

Abstract

Mobile robot path planning is a key technology in the field of intelligent robotics, and its performance directly affects the autonomy and reliability of robots in dynamic environments. Traditional algorithms rely on accurate environment modelling, which makes it difficult to cope with real-time changes in complex scenes. Deep learning techniques significantly improve the robustness and generalisation ability of path planning through end-to-end learning and autonomous feature extraction. This paper reviews the basic concepts of mobile robot path planning and analyses the recent advances of deep learning in path planning, including path optimization based on deep reinforcement learning, deep neural network-assisted path generation, and the fusion strategy of traditional methods and deep learning. Subsequently, this paper discusses the major challenges currently faced, such as computational complexity, model generalisation capability, and dynamic environment adaptation, and proposes future directions in the context of existing research, including lightweight models, cross-environment migratory learning, and multi-sensor fusion. This review aims to provide a comprehensive reference for researchers to promote the further application and development of deep learning in mobile robot path planning.

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.

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TY  - CONF
AU  - Yifan He
PY  - 2025
DA  - 2025/08/31
TI  - Progress of Deep Learning in Path Planning for Mobile Robots
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 537
EP  - 550
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_54
DO  - 10.2991/978-94-6463-821-9_54
ID  - He2025
ER  -