Reliable robotic handovers through tactile sensing

Gómez Eguíluz, A., Rañó, I., Coleman, S.A. and McGinnity, T.M. ORCID: 0000-0002-9897-4748, 2019. Reliable robotic handovers through tactile sensing. Autonomous Robots, 43 (7), pp. 1623-1637. ISSN 0929-5593

[img]
Preview
Text
13486_McGinnity.pdf - Post-print

Download (5MB) | Preview

Abstract

Joint manipulation and object exchange are common in many everyday scenarios. Although they are trivial tasks for humans, they are still very challenging for robots. Existing approaches for robot-to-human object handover assume that there is no fault during the transfer. However, unintentional perturbation forces can be occasionally applied to the object, resulting in the robot and the object being damaged, for example by being dropped. In this paper we present a novel approach to handover objects in a reliable manner while ensuring the safety of the robot and the object. Relying on tactile sensing, the system uses an effort controller to adapt the grasp forces in the presence of perturbations. Moreover, the proposed approach identifies a perturbation being applied on the object. When a perturbation event is detected, the algorithm classifies the direction of the pulling forces to decide whether to release it or not. The reliable handover system was implemented using a Shadow Robot hand equipped with BioTAC tactile sensors. Our results show that the system correctly adapts to the forces applied on the object to maintain the grasp and only releases the object if the human receiver pulls in the right direction.

Item Type: Journal article
Publication Title: Autonomous Robots
Creators: Gómez Eguíluz, A., Rañó, I., Coleman, S.A. and McGinnity, T.M.
Publisher: Springer New York
Date: 2019
Volume: 43
Number: 7
ISSN: 0929-5593
Identifiers:
NumberType
10.1007/s10514-018-09823-2DOI
694082Other
Record created by: Jonathan Gallacher
Date Added: 06 Mar 2019 15:03
Last Modified: 21 Jan 2021 09:53
URI: https://irep.ntu.ac.uk/id/eprint/35910

Actions (login required)

Edit View Edit View

Views

Views per month over past year

Downloads

Downloads per month over past year