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

Mahboubi, S, Milella, F, Davis, ST ORCID: https://orcid.org/0000-0002-4365-5619 and 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.

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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.

Item Type:	Book Section
Schools:	Schools > School of Computing, Science and Engineering
Publisher:	IEEE
ISBN:	9781509046331
Related URLs:	Publisher http://www.icra2017.org/
Funders:	The People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme
Depositing User:	S Mahboubi
Date Deposited:	10 Jul 2017 13:52
Last Modified:	15 Feb 2022 22:12
URI:	https://usir.salford.ac.uk/id/eprint/42902

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