Innovative Adaptive Imaged Based Visual Servoing Control of 6 DoFs Industrial Robot Manipulators

TL;DR

This paper introduces a robust adaptive control algorithm for image-based visual servoing of 6 DoF industrial robots, capable of handling features both inside and outside the camera's field of view, ensuring precise pose control.

Contribution

It presents an innovative feedforward-feedback adaptive control structure with Youla Parameterization, enhancing stability and accuracy in visual servoing for industrial robots.

Findings

Stable and fast motion control with features outside the view

High precision pose control when features enter the view

Robust performance demonstrated in various simulations

Abstract

Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla Parameterization method. A designed feature estimation loop ensures stable and fast motion control when point features are outside the field of view. As 3D point features move inside the field of view, the IBVS feedback loop preserves the precision of the pose at the end of the control period. Also, an adaptive controller is developed in the feedback loop to stabilize the system in the entire range of operations. The nonlinear camera and robot manipulator model is linearized and decoupled online by an adaptive algorithm. The adaptive controller is then computed based on the linearized model evaluated at current linearized point. The proposed solution is robust and easy to implement in different industrial robotic systems. Various scenarios are used in simulations to validate the effectiveness and robust performance of the proposed controller.