Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

Abstract

Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration.

Exploiting Wearable Goniometer Technology for Motion Sensing Gloves

“…This strategy consents to reconstruct the angle having vertex in the central marker of each finger. In [24], it is also proved that the accuracy of the motion capture data with the adopted model is in the order of 2-3 • . The adherence of the glove to the hand skin ensures that no geometric bias errors are introduced in the measurement.…”

“…Markers were placed according to the standard protocol described in [24], slightly modifying the marker position on the metacarpal bones in correspondence of the proximal acquisition point of the KPF goniometer which lies in the diaphysis of the bone. The proposed biomechanical model consists, for each finger, in a couple of segments, having the markers as extremities and placed in a 3-D space.…”

Tactile sensing in dexterous robot hands — Review

“…In unstructured environ- ments, object parameters are uncertain, which makes the grasping task even more difficult and presents a big challenge for grasp stability approaches. A detailed review of all grasping techniques is out the scope of this paper and can be found in previous papers [126], [127].…”

Performance Comparison Between FEDERICA Hand and LARM Hand

“…The high number of feasible DOFs makes the design and operation of a robotic hand very challenging. However, a careful analysis of the human fingers' motions shows that the feasible motions of phalanxes are not fully independent of one another, as outlined, for example, in [22][23][24][25]. In particular, by analysing several videos of human cylindrical grasping, one can notice that there are almost constant ratios between the motions of each phalanx during the motion of approaching an object, as also reported in [26][27][28].…”