Real-time Kinematic Calibration of Parallel Kinematics Mechanisms using Machine Learning Algorithms

Abstract

Parallel kinematics mechanisms (PKMs) offer advantages in terms of precision, rigidity, and dynamics over their serial counterparts. However, achieving high accuracy in PKMs requires precise calibration due to the complexity of their kinematic structure. Real-time kinematic calibration plays a crucial role in enhancing the accuracy and performance of PKMs. This paper explores the application of machine learning algorithms for real-time kinematic calibration of PKMs. We present a comprehensive review of the state-of-the-art methodologies, challenges, and opportunities in this domain. Additionally, we propose a novel framework that integrates machine learning techniques with kinematic modeling for enhanced calibration accuracy and efficiency. Experimental results demonstrate the effectiveness and feasibility of the proposed approach in achieving real-time calibration of PKMs.