Deep Learning-Driven Robot Arm Control Fusing Convolutional Visual Perception and Predictive Modeling for Motion Planning
Year: 2024
Type: article
Abstract: The wide application of robotic technology in various industries from industrial automation to medical assistance is gradually changing our production and lifestyle, and has attracted widespread attention in many fields. However, existing robotic control systems often grapple with limited flexibility and poor adaptability to complex environments, particularly in highly dynamic operational contexts. Against this backdrop, the integration of deep learning technologies offers new possibilities in enhancing robotic perception and decision-making, especially in visual perception and motion planning. To address these challenges, we have introduced a novel robotic arm control network model, MPC-WGAN-Faster R-CNN, which combines Model Predictive Control concepts with Wasserstein Generative Adversarial Networks and Faster R-CNN visual recognition technology. This integration aims to improve the precision and adaptability of robotic arm operations in complex environments.
DOI: 10.4018/joeuc.355191
Institutions: Hubei University of Technology, Hubei University Of Economics
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402667965