| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 04028 | |
| Number of page(s) | 6 | |
| Section | Computer Vision, Robotic Systems, and Intelligent Control | |
| DOI | https://doi.org/10.1051/itmconf/20268404028 | |
| Published online | 06 April 2026 | |
Dynamic Path Planning and Obstacle Avoidance for Mobile Robots via Machine Vision
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
* Corresponding author’s email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
In recent years, the rapid development of automation and robotics technologies has led to a growing deployment of mobile robots in various dynamic environments. Mobile robots are increasingly utilized in dynamic environments, such as logistics warehouses and manufacturing plants, where the ability to avoid obstacles in real time directly affects efficiency and safety. This paper reviews key aspects of dynamic obstacle avoidance, focusing on three dimensions: commonly used technologies for environmental perception to detect obstacles, adaptive path planning algorithms for real-time route adjustment, and the structural design of integrated avoidance systems. Finally, this paper points out the challenges in existing research and looks forward to its future research directions. It also aims to address gaps in existing technologies, such as limited sensor accuracy and rigid path planning, providing a reference for enhancing mobile robots’ adaptability in complex settings. The research contributes to advancing practical applications, which supports the broader adoption of mobile robots in unstructured dynamic environments.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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