High-performing adaptive grasp for a robotic gripper using super twisting sliding mode control

Mahboubi, S; Milella, F; Davis, ST; Nefti-Meziani, S

Abstract

Grasping an object in the presence of
unpredictable disturbances and with no previous knowledge of
the geometry and mass distribution of it represents a
significant challenge in Robotics. Also, grasping an object with
sufficient force to prevent slippage, whilst not damaging or
deforming the shape of the object proves to be still an intricate
challenge despite the existence of a huge body of literature on
robotic grasping. This paper addresses this challenge by
evaluating the performance of a Super Twisting Sliding Mode
Control (STSMC) designed for preventing slippage meanwhile
causing minimum deformation to the grasped object. The
performance of the STSMC is evaluated against the one of a
First Order Sliding Mode Controller (FOSMC). Both
controllers use grip force and slip feedback to counteract the
slippage. Force and slip detection are provided by low-cost,
off-the-shelf sensors. Experimental results presented in this
paper, show that, when grasping objects, both controllers can
robustly overcome external nonlinear disturbances, along with
inaccuracies in the system model, preventing slippage and
minimising deformation of objects. However, from these
results, we observe that the STSMC not only, as expected,
eliminates the major drawback of the FOSMC, which is
chattering, but also provides more consistent and robust
response in terms of slippage and gripping force.

Citation

Mahboubi, S., Milella, F., Davis, S., & Nefti-Meziani, S. (2017). High-performing adaptive grasp for a robotic gripper using super twisting sliding mode control. In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE. https://doi.org/10.1109/ICRA.2017.7989688

• Start Date: May 29, 2023
• End Date: Jun 3, 2017
• Acceptance Date: Jan 15, 2017
• Publication Date: Jan 1, 2017
• Deposit Date: Jul 10, 2017
• Book Title: 2017 IEEE International Conference on Robotics and Automation (ICRA)
• ISBN: 9781509046331
• DOI: https://doi.org/10.1109/ICRA.2017.7989688
• Publisher URL: http://dx.doi.org/10.1109/ICRA.2017.7989688
• Related Public URLs: http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000639; http://www.icra2017.org/
• Additional Information: Event Type : Conference; Funders : The People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme; Projects : SMART-E; Grant Number: 608022