Skeleton-based Adaptive Visual Servoing for Control of Robotic Manipulators in Configuration Space
Skeleton-based Adaptive Visual Servoing for Control of Robotic Manipulators in Configuration Space
复制标题
DOI：
10.1109/iros47612.2022.9981159
复制
发表时间：
2022-10
期刊：
影响因子：
--
通讯作者：
Abhinav Gandhi;Sreejani Chatterjee;B. Çalli
中科院分区：
文献类型：
--
作者：
Abhinav Gandhi;Sreejani Chatterjee;B. Çalli
本文提出了一种新的视觉伺服方法，控制机器人操作器的配置空间，而不是经典的基于视觉的控制方法只专注于末端执行器的姿态。我们首先提取的机器人的形状从深度图像使用一个卷积算法，并表示它使用参数曲线。然后，我们采用了自适应视觉伺服计划，估计雅可比在线有关的曲线参数和关节速度的变化。所提出的方案不仅能够控制配置空间中的机械手，但也表现出更好的瞬态响应，同时收敛到目标配置相比，经典的自适应视觉伺服方法。我们提出了模拟和真实的机器人实验，证明所提出的方法的能力，并分析其性能，鲁棒性和可重复性相比，经典算法。
This paper presents a novel visual servoing method that controls a robotic manipulator in the configuration space as opposed to the classical vision-based control methods solely focusing on the end effector pose. We first extract the robot's shape from depth images using a skeletonization algorithm and represent it using parametric curves. We then adopt an adaptive visual servoing scheme that estimates the Jacobian online relating the changes of the curve parameters and the joint velocities. The proposed scheme does not only enable controlling a manipulator in the configuration space, but also demonstrates a better transient response while converging to the goal configuration compared to the classical adaptive visual servoing methods. We present simulations and real robot experiments that demonstrate the capabilities of the proposed method and analyze its performance, robustness, and repeatability compared to the classical algorithms.