Human motion intention based scaled teleoperation for orientation assistance in preshaping for grasping

Author

Khokar, Karan H. ; Alqasemi, Redwan ; Sarkar, Santonu ; Dubey, Rajiv V.

Author_Institution

Dept. of Mech. Eng., Univ. of South Florida, Tampa, FL, USA

fYear

2013

fDate

24-26 June 2013

Firstpage

1

Lastpage

6

Abstract

In this paper, we present an algorithm that provides human motion intention based assistance to users teleoperating a remote gripper for preshaping over an object in order to grasp it. Human motion data from the remote arm is used to train a Hidden Markov Model (HMM) offline. During the execution of a grasping task, the motion data is processed in real time through the HMM to determine the intended preshape configuration of the user. Based on the intention, the motion of the remote arm is scaled up in those orientation directions that lead to the desired configuration, thus providing the necessary assistance to the user to preshape for grasping. Tests on healthy human subjects validated the hypothesis that the users are able to preshape quicker and with much ease. Average time savings of 36% were obtained.

Keywords

grippers; hidden Markov models; human-robot interaction; motion control; path planning; telerobotics; ADL; HMM; activities-of-daily living; autonomous grasping; grasp planning; grasp synthesis; grasping task; hidden Markov model; human motion data; human motion intention based scaled teleoperation; orientation assistance; preshaping; remote gripper; service robots; Fixtures; Grasping; Grippers; Hidden Markov models; Probability distribution; Vectors; Viterbi algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1945-7898

Print_ISBN

978-1-4673-6022-7

Type

conf

DOI

10.1109/ICORR.2013.6650443

Filename

6650443