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.

当前在机器人操纵中使用的视觉伺服方法要求系统建模和参数，仅在结构化环境中有效。本文提出了一种用于非结构化环境中的机械手的非参数视觉伺服。高斯映射似然过程用于机器人协调控制的贝叶斯随机状态估计（SSE）中，其中创建了蒙特卡洛顺序重要性采样（MCSIS）算法和学习校正的方法来进行机器人视觉运动映射估计。所描述的自学习策略利用纠正粒子劣化的优势，以在低采样率下保持鲁棒的性能，而不是像MCSIS那样依靠扩大采样方差来覆盖整个状态分布。另外，通过视觉观察直接推导出伺服控制器用于机器人协调。提出的框架的稳定性由李雅普诺夫（Lyapunov）理论加以说明，并应用于没有系统参数且手眼配置的操纵器。最后，仿真和实验结果一致地表明，所提出的涉及学习校正的算法优于传统的视觉伺服方法。