Literature DB >> 31034421

A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping.

Firas Abi-Farraj, Claudio Pacchierotti, Oleg Arenz, Gerhard Neumann, Paolo Robuffo Giordano.   

Abstract

Although robotic telemanipulation has always been a key technology for the nuclear industry, little advancement has been seen over the last decades. Despite complex remote handling requirements, simple mechanically linked master-slave manipulators still dominate the field. Nonetheless, there is a pressing need for more effective robotic solutions able to significantly speed up the decommissioning of legacy radioactive waste. This paper describes a novel haptic shared-control approach for assisting a human operator in the sort and segregation of different objects in a cluttered and unknown environment. A three-dimensional scan of the scene is used to generate a set of potential grasp candidates on the objects at hand. These grasp candidates are then used to generate guiding haptic cues, which assist the operator in approaching and grasping the objects. The haptic feedback is designed to be smooth and continuous as the user switches from a grasp candidate to the next one, or from one object to another one, avoiding any discontinuity or abrupt changes. To validate our approach, we carried out two human-subject studies, enrolling 15 participants. We registered an average improvement of 20.8%, 20.1%, and 32.5% in terms of completion time, linear trajectory, and perceived effectiveness, respectively, between the proposed approach and standard teleoperation.

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Year:  2019        PMID: 31034421     DOI: 10.1109/TOH.2019.2913643

Source DB:  PubMed          Journal:  IEEE Trans Haptics        ISSN: 1939-1412            Impact factor:   2.487


  3 in total

1.  Corrective Shared Autonomy for Addressing Task Variability.

Authors:  Michael Hagenow; Emmanuel Senft; Robert Radwin; Michael Gleicher; Bilge Mutlu; Michael Zinn
Journal:  IEEE Robot Autom Lett       Date:  2021-03-08

Review 2.  Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning.

Authors:  Haonan Duan; Peng Wang; Yayu Huang; Guangyun Xu; Wei Wei; Xiaofei Shen
Journal:  Front Neurorobot       Date:  2021-06-09       Impact factor: 2.650

3.  Design of a Hyper-Redundant Robot and Teleoperation Using Mixed Reality for Inspection Tasks.

Authors:  Andrés Martín-Barrio; Juan Jesús Roldán-Gómez; Iván Rodríguez; Jaime Del Cerro; Antonio Barrientos
Journal:  Sensors (Basel)       Date:  2020-04-12       Impact factor: 3.576

  3 in total

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