HapticV1, PascalFrancis, Corpus, bibRecord, 000B39

Investigation of motion guidance with scooter cobot and collaborative learning

Identifieur interne : 000B39 ( PascalFrancis/Corpus ); précédent : 000B38; suivant : 000B40

Investigation of motion guidance with scooter cobot and collaborative learning

Auteurs : ENG SENG BOY ; Etienne Burdet ; CHEE LEONG TEO ; James Edward Colgate

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.

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.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A11	`01`	`1`		`@1 ENG SENG BOY`
A11	`02`	`1`		`@1 BURDET (Etienne)`
A11	`03`	`1`		`@1 CHEE LEONG TEO`
A11	`04`	`1`		`@1 COLGATE (James Edward)`
A14	`01`			`@1 Victoria Junior College @2 Singapore 449035 @3 SGP @Z 1 aut.`
A14	`02`			`@1 Department of Bioengineering, Imperial College London @2 SW7 2AZ London @3 GBR @Z 2 aut.`
A14	`03`			`@1 Department of Mechanical Engineering, National University of Singapore @2 Singapore 119260 @3 SGP @Z 3 aut.`
A14	`04`			`@1 Department of Mechanical Engineering, Northwestern University @2 Evanston IL 60208-3111 @3 USA @Z 4 aut.`
A20				`@1 245-255`
A21				`@1 2007`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 21023A @5 354000149533850060`
A44				`@0 0000 @1 © 2007 INIST-CNRS. All rights reserved.`
A45				`@0 30 ref.`
A47	`01`	`1`		`@0 07-0308181`
A60				`@1 P`
A61				`@0 A`
A64	`01`	`1`		`@0 IEEE transactions on robotics`
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C01	`01`		`ENG`	@0 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.
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C03	`01`	`X`	`ENG`	`@0 Guidance @5 06`
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C03	`03`	`X`	`FRE`	`@0 Planification @5 08`
C03	`03`	`X`	`ENG`	`@0 Planning @5 08`
C03	`03`	`X`	`SPA`	`@0 Planificación @5 08`
C03	`04`	`X`	`FRE`	`@0 Homme @5 09`
C03	`04`	`X`	`ENG`	`@0 Human @5 09`
C03	`04`	`X`	`SPA`	`@0 Hombre @5 09`
C03	`05`	`X`	`FRE`	`@0 Mouvement corporel @5 18`
C03	`05`	`X`	`ENG`	`@0 Body movement @5 18`
C03	`05`	`X`	`SPA`	`@0 Movimiento corporal @5 18`
C03	`06`	`X`	`FRE`	`@0 Outil coupe @5 19`
C03	`06`	`X`	`ENG`	`@0 Cutting tool @5 19`
C03	`06`	`X`	`SPA`	`@0 Herramienta corte @5 19`
C03	`07`	`X`	`FRE`	`@0 Trajectoire @5 20`
C03	`07`	`X`	`ENG`	`@0 Trajectory @5 20`
C03	`07`	`X`	`SPA`	`@0 Trayectoria @5 20`
C03	`08`	`X`	`FRE`	`@0 Ergonomie @5 21`
C03	`08`	`X`	`ENG`	`@0 Ergonomics @5 21`
C03	`08`	`X`	`SPA`	`@0 Ergonomía @5 21`
C03	`09`	`X`	`FRE`	`@0 Sensibilité tactile @5 22`
C03	`09`	`X`	`ENG`	`@0 Tactile sensitivity @5 22`
C03	`09`	`X`	`SPA`	`@0 Sensibilidad tactil @5 22`
C03	`10`	`X`	`FRE`	`@0 Opérateur humain @5 23`
C03	`10`	`X`	`ENG`	`@0 Human operator @5 23`
C03	`10`	`X`	`SPA`	`@0 Operador humano @5 23`
C03	`11`	`X`	`FRE`	`@0 Capteur mesure @5 24`
C03	`11`	`X`	`ENG`	`@0 Measurement sensor @5 24`
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N21				`@1 197`
N44	`01`			`@1 OTO`
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Format Inist (serveur)

NO :	PASCAL 07-0308181 INIST
ET :	Investigation of motion guidance with scooter cobot and collaborative learning
AU :	ENG SENG BOY; BURDET (Etienne); CHEE LEONG TEO; COLGATE (James Edward)
AF :	Victoria Junior College/Singapore 449035/Singapour (1 aut.); Department of Bioengineering, Imperial College London/SW7 2AZ London/Royaume-Uni (2 aut.); Department of Mechanical Engineering, National University of Singapore/Singapore 119260/Singapour (3 aut.); Department of Mechanical Engineering, Northwestern University/Evanston IL 60208-3111/Etats-Unis (4 aut.)
DT :	Publication en série; Niveau analytique
SO :	IEEE transactions on robotics ; ISSN 1552-3098; Etats-Unis; Da. 2007; Vol. 23; No. 2; Pp. 245-255; Bibl. 30 ref.
LA :	Anglais
EA :	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.
CC :	001D02D11
FD :	Guidage; Robotique; Planification; Homme; Mouvement corporel; Outil coupe; Trajectoire; Ergonomie; Sensibilité tactile; Opérateur humain; Capteur mesure
ED :	Guidance; Robotics; Planning; Human; Body movement; Cutting tool; Trajectory; Ergonomics; Tactile sensitivity; Human operator; Measurement sensor
SD :	Guiado; Robótica; Planificación; Hombre; Movimiento corporal; Herramienta corte; Trayectoria; Ergonomía; Sensibilidad tactil; Operador humano; Captador medida
LO :	INIST-21023A.354000149533850060
ID :	07-0308181

Links to Exploration step

Pascal:07-0308181

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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<ET>Investigation of motion guidance with scooter cobot and collaborative learning</ET>
<AU>ENG SENG BOY; BURDET (Etienne); CHEE LEONG TEO; COLGATE (James Edward)</AU>
<AF>Victoria Junior College/Singapore 449035/Singapour (1 aut.); Department of Bioengineering, Imperial College London/SW7 2AZ London/Royaume-Uni (2 aut.); Department of Mechanical Engineering, National University of Singapore/Singapore 119260/Singapour (3 aut.); Department of Mechanical Engineering, Northwestern University/Evanston IL 60208-3111/Etats-Unis (4 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>IEEE transactions on robotics ; ISSN 1552-3098; Etats-Unis; Da. 2007; Vol. 23; No. 2; Pp. 245-255; Bibl. 30 ref.</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>001D02D11</CC>
<FD>Guidage; Robotique; Planification; Homme; Mouvement corporel; Outil coupe; Trajectoire; Ergonomie; Sensibilité tactile; Opérateur humain; Capteur mesure</FD>
<ED>Guidance; Robotics; Planning; Human; Body movement; Cutting tool; Trajectory; Ergonomics; Tactile sensitivity; Human operator; Measurement sensor</ED>
<SD>Guiado; Robótica; Planificación; Hombre; Movimiento corporal; Herramienta corte; Trayectoria; Ergonomía; Sensibilidad tactil; Operador humano; Captador medida</SD>
<LO>INIST-21023A.354000149533850060</LO>
<ID>07-0308181</ID>
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Investigation of motion guidance with scooter cobot and collaborative learning

Investigation of motion guidance with scooter cobot and collaborative learning

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