A Bio-inspired Grasp Stiffness Control for Robotic Hands

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.

An approach to object-level stiffness regulation of hand-arm systems subject to under-actuation constraints

“…The controller has two main elements: the hand and the arm. The hand controller is based on the one presented in Ruiz Garate et al (2018a), where the parts that are adapted to fit the new controller are displayed in blue. (1) The arm Cartesian impedance controller is described in Balatti et al (2019) formation matrix T r is defined that connects the hand base (Hand) and the arm end-effector (Ar m) frames (Fig.…”

“…is the number of joints of the finger, α is the synergistic stiffness parameter (Nm/rad), and Γ f is a constant normalised vector implementing the coordinated stiffening of the hand fingers (CMS) (Rossi et al 2015). As mentioned before, the hand under-actuation principle here is based on our previous work in Ruiz Garate et al (2018a).…”

“…These are used as constraints in the exploration to find compatible grasp configurations (CDS). Then, for the initial and subsequent grasps a hand configuration is found that squeezes the object and stabilizes it as explained in Ruiz Garate et al (2018a). This is achieved by an optimization process where a cost function based on the index of Potential Contact Robustness (PCR) is maximized (Pozzi et al 2017).…”

“…Similar to Ruiz Garate et al (2018a, b), the joint stiffness synergy is defined by Γ f = [0.7, 0.95, 1, 0.8] for the index, middle, and little fingers, and Γ f = [1, 0.95, 1, 0.95] for the thumb, while the synergistic value α is constrained between 1 and 6 Nm/rad being initially of 4 Nm/rad. Also, taking into account the Allegro's hand fingertips dimensions and previous experiments in Ruiz Garate et al (2018a), the threshold value p t is set to 5 mm. For the stability optimization, minimum and maximum contact forces are set to 1 and 10 N as a compromise between the need to exert high contact forces and not setting a reference position too far inside the object.…”

“…Moreover, the impedance controller of the arm serves to regulate the obtained hand stiffness as needed for the task. Hence, the main contributions of this work can be summarized as: the formulation of a control strategy to regulate the object-level stiffness for hand-arm systems subject to under-actuation principles, and the adaptation of the grasp stiffness control in (Ruiz Garate et al 2018a) to a new scenario with a moving hand base attached to a robotic arm.…”