基于随机森林算法的气动软体机械臂视觉伺服

软体机械臂具有灵活性和柔顺性的特点,可在实现对位姿跟踪的同时确保与环境交互的安全性,近年成为研究的热点。但由于软体机械臂材料变形是非线性的,其运动学建模的参数众多且难以获得准确值,使软体机械臂实现运动学控制较为困难。为了补偿软体机械臂的不确定性,在现在视觉伺服的基础上,提出一种基于历史数据驱动的手眼视觉伺服新方法。该方法结合基于随机森林算法的控制器来完成机械臂控制任务,通过对历史数据聚类,基于随机森林回归模型建立软体机械臂驱动状态和末端图像特征的逆映射,无须求解机械臂和摄像机的任何参数,即可快速获取系统输入变量。实验结果表明,所提出的方法可以较好地实现预期控制目标。

Visual servo control of pneumatic soft manipulator based on random forest algorithm

The soft manipulator possesses dexterity and flexibility, ensuring safe interaction with the environment while accurately tracking position and posture. It has emerged as a prominent area of research in recent years. However, because the material deformation of the soft manipulator is nonlinear, its kinematic modeling parameters are numerous and it is difficult to obtain accurate values, these difficulties hinder the realization of kinematic control for the soft manipulator. To address the uncertainty of the soft manipulator, this paper proposes a new hand-eye visual servoing method driven by historical data, building upon the current visual servoing techniques. This method integrates a controller based on the random forest algorithm to accomplish the control tasks of the manipulator. By clustering historical data, an inverse mapping of the driving state of the soft manipulator and image characteristics is established using the random forest regression model. The system input variables are predicted quickly without the need to solve any parameters of the manipulator and camera. The experimental results show that the proposed method can better achieve the expected control objectives.