Robust proprioceptive grasping with a soft robot hand

This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements along with a combined system which autonomously performs grasps. A highly compliant soft hand allows for intrinsic robustness to grasping uncertainties; the addition of internal s...

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Bibliographic Details
Published in:Autonomous robots 2019-03, Vol.43 (3), p.681-696
Main Authors: Homberg, Bianca S., Katzschmann, Robert K., Dogar, Mehmet R., Rus, Daniela
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Closed loops
Clustering
Computer Imaging
Control
Curvature
Data points
End effectors
Engineering
Feedback control
Grasping (robotics)
Mechatronics
Pattern Recognition and Graphics
Robotics
Robotics and Automation
Sensors
Soft robotics
Uncertainty
Vision
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Description
Summary:This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements along with a combined system which autonomously performs grasps. A highly compliant soft hand allows for intrinsic robustness to grasping uncertainties; the addition of internal sensing allows the configuration of the hand and object to be detected. The finger module includes resistive force sensors on the fingertips for contact detection and resistive bend sensors for measuring the curvature profile of the finger. The curvature sensors can be used to estimate the contact geometry and thus to distinguish between a set of grasped objects. With one data point from each finger, the object grasped by the hand can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. A closed loop system uses a camera to detect approximate object locations. Compliance in the soft hand handles that uncertainty in addition to geometric uncertainty in the shape of the object.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-018-9754-1