Optimized Design and Calibration of a Human-Eye-Sized Active Binocular Vision System Based on Spherical Parallel Mechanism

Abstract

The Active Binocular Vision System (ABVS), resembling the human eye, demonstrates potential for improving visual perception in robotic systems, especially in dynamic and complex environments. In this letter, we present an optimized design of a three degree-of-freedom (DoF) Active Monocular Vision System (AMVS) based on a Spherical Parallel Manipulator (SPM). By combining two identical AMVS units, we form an ABVS, which has been successfully integrated into a humanoid robotic head. Due to the highly nonlinear kinematics of SPM and complex error coupling in its multi-link structure, traditional end-to-end neural network training methods are insufficient in accuracy and require large datasets. To address these challenges, we propose a two-branch optimization network that significantly improves calibration accuracy. Furthermore, we introduce a four-branch fine-tuning strategy that enables accurate kinematic models to be obtained with only a small amount of data from new AMVS devices. Experimental results demonstrate that the two-branch optimization network reduces rotational prediction error by 16% and translational error by 5% compared to a single-branch network. Furthermore, the four-branch fine-tuning network achieves comparable accuracy to a fully trained single-branch network using only 343 data points. Finally, our ABVS shows the capability to perform 3D visual tasks, such as stereo reconstruction during movement.