Position-Based Visual Servo Control of Dual Robotic Arms With Unknown Kinematic Models: A Cerebellum- Inspired Approach

Abstract

The inverse kinematics problem of robotic arms can be solved effectively based on the kinematic model. However, the control of robotic arms with unknown kinematic models remains challenging. This article proposes a position-based visual servo (PBVS) control system for dual robotic arms with unknown kinematic models. First, a unified PBVS control method based on dual gradient neural dynamic (DGND) models is developed to achieve the position and orientation control of a single robotic arm with unknown kinematic model. Next, a cerebellum-inspired DGND (CIDGND) control system is devised to achieve higher accuracy. More importantly, a control system for dual robotic arms is proposed based on the CIDGND method to expand the working space of the primary robotic arm, by autonomously adjusting the camera pose with another robotic arm. Finally, simulations and experiments are performed to verify the efficacy of the proposed control systems. The results validate that the DGND method can achieve the PBVS control of robotic arms in the absence of kinematic models and the CIDGND method is able to reduce the tracking error significantly. Comparative results reveal that the CIDGND method is effectively and robustly better than existing methods.