Abstract
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.
Highlights
The human hand shows a high level of dexterity that allows to perform numerous complex tasks
Other approaches reproduced the human ability to generate coordinated movements in the joint space using the concept of postural synergies (Santello et al, 1998). These synergies have been tackled from the hardware perspective (Ajoudani et al, 2014; Catalano et al, 2014), and from the software viewpoint (Gioioso et al, 2013). We focus on another important feature of human grasp which so far has drawn less attention: a principled simplification of grasp stiffness control
More innovative applications could be for example, a robotic system that exploits the environmental constraints, contributing to the Configuration Dependent Stiffness (CDS) control (Eppner et al, 2015). This manuscript proposed a control method to achieve a desired grasp stiffness based on high-level features of the task to execute
Summary
The human hand shows a high level of dexterity that allows to perform numerous complex tasks. It is often used as inspiration for designing robots to operate in human environments. The five-fingered Shadow Dexterous HandTM1 and the Awiwi Hand (Grebenstein, 2014) are examples of such a bio-inspired conception. Their anthropomorphic design endows the robotic hands with a similar kinematic workspace to that of a human hand. These robots use actuation and perception systems that allow to imitate the dynamic movements of human hands and to grasp a wide variety of objects. The required number of sensing and actuation units in such poly-articulated hands has contributed to an increased cost and complexity in their manufacturing
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