Research on Industrial Robot Grasping Based on Visual Technology

DOI

10.2991/978-94-6239-648-7_42How to use a DOI?

Keywords

Visual technology; industrial robot grasping; target recognition; grasping path planning

Abstract

Under the backdrop of the rapid development of industrial automation, industrial robots have become the core force for the transformation and upgrading of manufacturing. Integrating visual technology into the grasping of industrial robots changes the traditional operation mode, endowing robots with the “perception - decision-making – execution” closed-loop intelligent operation capability, enhancing their intelligence and flexibility, and enabling them to adapt to complex and changeable industrial environments. This article first provides an overview of the industrial robot vision grasping system, highlighting the key role of the visual perception system. Then, it elaborates on the core technology chain of vision grasping, including image processing which acquires and preprocesses images through cameras and performs recognition and segmentation; three-dimensional positioning which acquires the three-dimensional information of objects through various technologies; grasping pose estimation which infers the pose by combining visual and other sensing technologies or deep learning; and grasping planning and path guidance which plan collision-free trajectories and optimize them, while correcting deviations through visual servoing. Currently, this technology is evolving towards greater intelligence and other directions, which can empower manufacturing and support emerging scenarios. However, it faces challenges such as extreme environment recognition and multi-modal data fusion. In the future, with the in-depth integration of technologies such as deep learning, it is expected to improve the accuracy and robustness of industrial robot grasping and promote the intelligent upgrade of manufacturing.

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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TY  - CONF
AU  - Changye Du
PY  - 2026
DA  - 2026/04/24
TI  - Research on Industrial Robot Grasping Based on Visual Technology
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 377
EP  - 387
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_42
DO  - 10.2991/978-94-6239-648-7_42
ID  - Du2026
ER  -