Zhang Yinyan, Zheng Yuhua, Gao Feng, Li Shuai
IEEE Trans Neural Netw Learn Syst. 2024 Sep 12;PP. doi: 10.1109/TNNLS.2024.3454128.
The image-based visual servoing (IBVS) of manipulators is important for intelligent manipulation using visual feedbacks. While the traditional IBVS methods for manipulators require the knowledge of the depth information in the interaction matrix, in this article, we propose a novel IBVS method for manipulators without depth estimation by leveraging the property of the associated image Jacobian. Because of a novel transformation, the IBVS problem is converted into a convex optimization problem subject to the kinematic constraint, joint constraints, and other constraints that are not explicitly related to the depth information. The problem is then solved by developing a recurrent neural network of global asymptotic convergence, and a dynamic neural control law without depth estimation emerges for the IBVS of manipulators. The theoretical guarantee and simulation results are provided to show the efficacy of the proposed method.
基于图像的机器人视觉伺服（IBVS）对于利用视觉反馈进行智能操作非常重要。虽然传统的机器人IBVS方法需要交互矩阵中的深度信息，但在本文中，我们通过利用相关图像雅可比矩阵的性质，提出了一种无需深度估计的新型机器人IBVS方法。由于一种新颖的变换，IBVS问题被转化为一个受运动学约束、关节约束和其他与深度信息无明确关系的约束的凸优化问题。然后通过开发一个全局渐近收敛的递归神经网络来解决该问题，并且出现了一种无需深度估计的动态神经控制律用于机器人的IBVS。提供了理论保证和仿真结果以证明所提方法的有效性。
