Ruiz Garate Virginia, Pozzi Maria, Prattichizzo Domenico, Ajoudani Arash
Human-Robot Interfaces and Physical Interaction Department, Istituto Italiano di Tecnologia, Genova, Italy.
Advanced Robotics Department, Istituto Italiano di Tecnologia, Genova, Italy.
Front Robot AI. 2018 Jul 26;5:89. doi: 10.3389/frobt.2018.00089. eCollection 2018.
This work presents a bio-inspired grasp stiffness control for robotic hands based on the concepts of Common Mode Stiffness (CMS) and Configuration Dependent Stiffness (CDS). Using an ellipsoid representation of the desired grasp stiffness, the algorithm focuses on achieving its geometrical features. Based on preliminary knowledge of the fingers workspace, the method starts by exploring the possible hand poses that maintain the grasp contacts on the object. This outputs a first selection of feasible grasp configurations providing the base for the CDS control. Then, an optimization is performed to find the minimum joint stiffness (CMS control) that would stabilize these grasps. This joint stiffness can be increased afterwards depending on the task requirements. The algorithm finally chooses among all the found stable configurations the one that results in a better approximation of the desired grasp stiffness geometry (CDS). The proposed method results in a reduction of the control complexity, needing to independently regulate the joint positions, but requiring only one input to produce the desired joint stiffness. Moreover, the usage of the fingers pose to attain the desired grasp stiffness results in a more energy-efficient configuration than only relying on the joint stiffness (i.e., joint torques) modifications. The control strategy is evaluated using the fully actuated Allegro Hand while grasping a wide variety of objects. Different desired grasp stiffness profiles are selected to exemplify several stiffness geometries.
这项工作基于共模刚度（CMS）和构型相关刚度（CDS）的概念，提出了一种用于机器人手的仿生抓握刚度控制方法。该算法使用期望抓握刚度的椭球体表示，专注于实现其几何特征。基于手指工作空间的初步知识，该方法首先探索能够保持与物体抓握接触的可能手部姿势。这输出了一组可行抓握构型的初步选择，为CDS控制提供了基础。然后，进行优化以找到能够稳定这些抓握的最小关节刚度（CMS控制）。之后，可以根据任务要求增加该关节刚度。该算法最终在所有找到的稳定构型中选择一个，使其更接近期望抓握刚度的几何形状（CDS）。所提出的方法降低了控制复杂度，无需独立调节关节位置，而仅需一个输入来产生期望的关节刚度。此外，利用手指姿势来获得期望的抓握刚度，相比于仅依靠关节刚度（即关节扭矩）的改变，能得到更节能的构型。在抓取各种物体时，使用完全驱动的Allegro手对控制策略进行了评估。选择了不同的期望抓握刚度轮廓来举例说明几种刚度几何形状。
