Soft robotic hands are reliable for grasping objects of various shapes. However, they perform poorly in the high-precision manipulation of grasped objects because the modeling and sensing of soft actuator deformation are complex. To overcome this problem, in a previous study, we proposed a robust visual feedback control method for precise in-hand manipulation using parallel soft actuators. This method enables precise in-hand manipulation without measuring the soft actuator deformations. Generally, in the feedback control of a parallel drive system, the actuator force is converted into the force/torque applied to the grasped object. The conversion matrix constantly changes depending on the position/orientation of the object and the contact points of the soft actuators with the object. Consequently, accurately measuring the conversion matrix is difficult. Therefore, we estimated it as a constant matrix in our previous studies, and its robustness was confirmed experimentally. However, its theoretical robustness was not analyzed sufficiently. Therefore, in this article, we discuss the robustness of the estimated constant matrix through a mathematical stability proof. Furthermore, we investigate the characteristics of the estimated drive matrix. Then, we perform a numerical robustness analysis. Finally, the robustness of the proposed method is studied via the above investigation and verification experiments.

中文翻译：

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使用并行软执行器进行精确的手动操作的鲁棒视觉反馈控制


软机器人手可以可靠地抓取各种形状的物体。然而，由于软执行器变形的建模和传感复杂，它们在抓取物体的高精度操纵方面表现不佳。为了克服这个问题，在之前的研究中，我们提出了一种鲁棒的视觉反馈控制方法，用于使用并行软执行器进行精确的手动操作。这种方法可以实现精确的手动操作，而无需测量软执行器的变形。通常，在并联驱动系统的反馈控制中，致动器力被转换为施加到抓取物体的力/扭矩。转换矩阵根据物体的位置/方向以及软致动器与物体的接触点不断变化。因此，精确测量转换矩阵是困难的。因此，我们在之前的研究中将其估计为常数矩阵，并通过实验证实了其稳健性。然而，其理论稳健性尚未得到充分分析。因此，在本文中，我们通过数学稳定性证明来讨论估计常数矩阵的鲁棒性。此外，我们研究了估计驱动矩阵的特征。然后，我们进行数值稳健性分析。最后，通过上述调查和验证实验，研究了所提方法的鲁棒性。