Work

Research on Industrial Robot Control and Path Planning Based on Deep Learning

Year: 2025

Type: conference-paper

Abstract: This article proposes a deep learning based design and path planning scheme for industrial robot intelligent control system, focusing on in-depth research on deep learning, system design scheme, data flow and processing mechanism, and path planning. By integrating advanced sensing technology, deep learning algorithms, and efficient data processing modules, industrial robots have achieved high adaptability and precise control in complex environments. Meanwhile, the experimental results of path planning based on deep learning show that this planning method reduces the time and length of obstacle avoidance path planning, and improves the success rate of planning.

Source: International Conference on Communication Networks and Machine Learning (CNML)

DOI: 10.1145/3728199.3728284

Authors: Qiyan Yan, Zhibin Xiao, Yali Huang, Lei Li

Institution: Guangdong University of Science and Technology

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funder: Guangdong University of Science and Technology