Intuitive Adaptive Orientation Control for Enhanced Human-Robot Interaction

被引:25
作者
Campeau-Lecours, Alexandre [1 ,2 ]
Cote-Allard, Ulysse [3 ]
Dinh-Son Vu [1 ]
Routhier, Francois [4 ,5 ]
Gosselin, Benoit [3 ,4 ]
Gosselin, Clement [1 ,2 ]
机构
[1] Univ Laval, Dept Mech Engn, Qubec, PQ G1V 0A6, Canada
[2] Inst Radaptat Dficience Phys Qubec, Ctr Interdisciplinary Res Rehabil & Social Integr, Ctr Integr St & Serv Soc Capitale Natl, Qubec, PQ G1M 2S8, Canada
[3] Univ Laval, Dept Comp & Elect Engn, Qubec, PQ G1V 0A6, Canada
[4] Univ Laval, Dept Rehabil, Quebec City, PQ G1V 0A6, Canada
[5] Inst Readaptat Deficience Phys Quebec, Ctr Interdisciplinary Res Rehabil & Social Integr, Ctr Integre Sante & Serv Soc Capitale Natl, Quebec City, PQ G1M 2S8, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Assistive robotics; human-robot interaction (HRI); orientation control; rehabilitation robotics; INTERFACE;
D O I
10.1109/TRO.2018.2885464
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Robotic devices can be leveraged to raise the abilities of humans to perform demanding and complex tasks with less effort. Although the first priority of such human-robot interaction (HRI) is safety, robotic devices must also be intuitive and efficient in order to be adopted by a broad range of users. One challenge in the control of such assistive robots is the management of the end-effector orientation, that is not always intuitive for the human operator, especially for neophytes. This paper presents a novel orientation control algorithm designed for robotic arms in the context of HRI. This paper aims at making the control of the robot's orientation easier and more intuitive for the user, both in the fields of rehabilitation (in particular individuals living with upper limb disabilities) and industrial robotics. The performance and intuitiveness of the proposed orientation control algorithm is assessed and improved through two experiments with a JACO assistive robot with 25 able-bodied subjects, an online survey with 117 respondents via the Amazon Mechanical Turk and through two experiments with a UR5 industrial robot with 12 able-bodied subjects.
引用
收藏
页码:509 / 520
页数:12
相关论文
共 43 条
[1]  
[Anonymous], INT S ROB
[2]  
Baldi TL, 2017, INT C REHAB ROBOT, P1567, DOI 10.1109/ICORR.2017.8009471
[3]   Fast scene analysis using vision and artificial intelligence for object prehension by an assistive robot [J].
Bousquet-Jette, C. ;
Achiche, S. ;
Beaini, D. ;
Cio, Y. S. Law-Kam ;
Leblond-Menard, C. ;
Raison, M. .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2017, 63 :33-44
[4]  
Campeau-Lecours Alexandre, 2017, International Journal of Robotics Applications and Technologies, V5, P49, DOI 10.4018/IJRAT.2017070104
[5]  
Campeau-Lecours A., 2016, PROC ANN C REHABIL E
[6]  
Campeau-Lecours A, 2016, 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016), P4232, DOI 10.1109/IROS.2016.7759623
[7]   Human performance issues and user interface design for teleoperated robots [J].
Chen, Jessie Y. C. ;
Haas, Ellen C. ;
Barnes, Michael J. .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 2007, 37 (06) :1231-1245
[8]   Collaborative manufacturing with physical human-robot interaction [J].
Cherubini, Andrea ;
Passama, Robin ;
Crosnier, Andre ;
Lasnier, Antoine ;
Fraisse, Philippe .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2016, 40 :1-13
[9]  
Chiu CY, 2017, IEEE IJCNN, P3003, DOI 10.1109/IJCNN.2017.7966228
[10]  
Chung CS, 2017, TOP SPINAL CORD INJ, V23, P131, DOI 10.1310/sci2302-131