HapticV1, Main, Exploration, bibRecordById, Pascal:07-0308181

Investigation of motion guidance with scooter cobot and collaborative learning

Identifieur interne : 005993 ( Main/Exploration ); précédent : 005992; suivant : 005994

Investigation of motion guidance with scooter cobot and collaborative learning

Auteurs : ENG SENG BOY [Singapour] ; Etienne Burdet [Royaume-Uni] ; CHEE LEONG TEO [Singapour] ; James Edward Colgate [États-Unis]

Source :

IEEE transactions on robotics [ 1552-3098 ] ; 2007.

RBID : Pascal:07-0308181

Descripteurs français

Pascal (Inist)
- Guidage, Robotique, Planification, Homme, Mouvement corporel, Outil coupe, Trajectoire, Ergonomie, Sensibilité tactile, Opérateur humain, Capteur mesure.
Wicri :
- topic : Robotique, Homme, Ergonomie.

English descriptors

KwdEn :
- Body movement, Cutting tool, Ergonomics, Guidance, Human, Human operator, Measurement sensor, Planning, Robotics, Tactile sensitivity, Trajectory.

Abstract

This paper investigates how collaborative robots (cobots) can assist a human by mechanically constraining motion to software-defined guide paths, and introduces simple and efficient tools to design ergonomic paths. Analysis of the movements of seven subjects with the Scooter cobot reveals significant differences between guided movements (GM) and free movements (FM). While FM requires learning for each novel task, movements in GM are satisfying from the first trial, require little effort, are faster, smoother, and with fewer back and forth corrections than in FM. Operators rely on path guidance to rotate the Scooter and direct it along curved trajectories. While these advantages demonstrate the strength of the cobot concept, they do not show how guide paths should be defined. We introduce tools to enable the cobot and its operator to collaboratively learn ergonomic guide paths and adapt to changes in the environment. By relying on the haptic sensing, vision, and planning capabilities of the human operator, we can avoid equipping the cobot with complex sensor processing. Experiments with human subjects demonstrate the efficiency and complementarity of these guide paths design tools.

Affiliations:

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to stream Main, to step Merge: 005B88
to stream Main, to step Curation: 005993

Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper investigates how collaborative robots (cobots) can assist a human by mechanically constraining motion to software-defined guide paths, and introduces simple and efficient tools to design ergonomic paths. Analysis of the movements of seven subjects with the Scooter cobot reveals significant differences between guided movements (GM) and free movements (FM). While FM requires learning for each novel task, movements in GM are satisfying from the first trial, require little effort, are faster, smoother, and with fewer back and forth corrections than in FM. Operators rely on path guidance to rotate the Scooter and direct it along curved trajectories. While these advantages demonstrate the strength of the cobot concept, they do not show how guide paths should be defined. We introduce tools to enable the cobot and its operator to collaboratively learn ergonomic guide paths and adapt to changes in the environment. By relying on the haptic sensing, vision, and planning capabilities of the human operator, we can avoid equipping the cobot with complex sensor processing. Experiments with human subjects demonstrate the efficiency and complementarity of these guide paths design tools.</div>
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Investigation of motion guidance with scooter cobot and collaborative learning

Investigation of motion guidance with scooter cobot and collaborative learning

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri