An Analysis of Robotic Arms Control Strategies

This paper provides an overview of recent research on robotic arm technology mainly focus on perception and control algorithms. Firstly, the paper introduces vision-based robotic robotic arm systems, describes the integration of visual perception and deep learning technologies that enables robotic arms to detect and interact with objects in complex environments. The paper also reviews control methods based on electromyography (EMG), which make human-robot interaction more intuitive by converting muscle signals into robotic motions. Furthermore, the paper discusses motion planning based on reinforcement learning that enhance autonomy and adaptability of robotic manipulators. Overall, this paper summarizes the current progress in robotic arm research and discusses the advantages and limitations of different technical methods. This study can provide a useful reference for future research on intelligent robotic arm systems.