A nonparametric-learning visual servoing framework for robot manipulator in unstructured environments

Abstract

Current visual servoing methods used in robot manipulation require system modeling and parameters, only working in structured environments. This paper presents a nonparametric visual servoing for a robot manipulator operated in unstructured environments. A Gaussian-mapping likelihood process is used in Bayesian stochastic state estimation (SSE) for Robotic coordination control, in which the Monte Carlo sequential importance sampling (MCSIS) algorithm and a learning-remedied method are created for robotic visual-motor mapping estimation. The self-learning strategy described takes advantage of remedy the particles deterioration to maintain the robust performance at a low rate of particle sampling, rather than likes MCSIS rely on enlarge the sampling variance to cover the whole state distribution. Additionally, the servoing controller is deduced for robotic coordination directly by visual observation. The stability of the proposed framework is illustrated by Lyapunov theory and applied to a manipulator with eye-in-hand configuration no system parameters. Finally, the simulation and experimental results demonstrate consistently that the proposed algorithm involving learning-remedied outperforms traditional visual servoing approaches.