Uncalibrated and Unmodeled Image-Based Visual Servoing of Robot Manipulators Using Zeroing Neural Networks

Neural networks have been widely investigated for the control of robot manipulators and recurrent neural network (RNN) is accepted as a powerful tool for visual servoing. Different from existing control schemes for robot-camera systems, this article proposes a novel image-based visual servoing (IBVS) control scheme for both the regulation and tracking control of robot manipulators in the framework of a special class of RNN, termed zeroing neural network (ZNN), which does not require prior knowledge about camera configuration and kinematic model parameters. The proposed control scheme is composed of a data-driven mapping estimator and a controller, both of which are designed based on ZNN. To facilitate the deployment of the proposed IBVS control scheme, a discrete-time version of the proposed control scheme is developed. Theoretical analysis for the proposed method is presented in terms of convergence, stability, and robustness. In addition, simulations and experiments are carried out based on different types of robot-camera systems to verify the efficacy and portability of the proposed control scheme for solving regulation and trajectory IBVS problems. Moreover, comparative studies are performed to reveal the merits of the proposed control scheme.

Automated summary

This article proposes an image-based visual servoing control scheme using recurrent neural networks for robot manipulators. The scheme does not require prior knowledge and is proven to be effective, stable, and robust through theoretical analysis and simulations. Its applicability and advantages are also demonstrated on different robot-camera systems.