TY - GEN
T1 - An EMG-controlled mobile robot based on a multi-layered non-contact impedance model
AU - Shibanoki, Taro
AU - Sasaki, Masaru
AU - Tsuji, Toshio
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Numbers: JP26330226 and 20K20212.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/3/9
Y1 - 2021/3/9
N2 - This paper proposes an obstacle avoidance method for EMG-controlled mobile robots based on a noncontact impedance model. The proposed system can voluntarily control a mobile robot by classifying EMG signals using a recurrent probabilistic neural network and can avoid obstacles without user handling based on virtual repulsive force through a multi-layered non-contact impedance model. In the experiments, two obstacles were arranged in the path of the mobile robot, and the participant was asked to control the robot toward a target. The robot passed through the obstacles smoothly without any avoidance operations, indicating that the proposed system could be used for obstacle avoidance in mobile robots.
AB - This paper proposes an obstacle avoidance method for EMG-controlled mobile robots based on a noncontact impedance model. The proposed system can voluntarily control a mobile robot by classifying EMG signals using a recurrent probabilistic neural network and can avoid obstacles without user handling based on virtual repulsive force through a multi-layered non-contact impedance model. In the experiments, two obstacles were arranged in the path of the mobile robot, and the participant was asked to control the robot toward a target. The robot passed through the obstacles smoothly without any avoidance operations, indicating that the proposed system could be used for obstacle avoidance in mobile robots.
KW - Collision avoidance
KW - Electromyogram (EMG)
KW - Noncontact impedance control
KW - Recurrent probabilistic neural network
UR - http://www.scopus.com/inward/record.url?scp=85104581737&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104581737&partnerID=8YFLogxK
U2 - 10.1109/LifeTech52111.2021.9391859
DO - 10.1109/LifeTech52111.2021.9391859
M3 - Conference contribution
AN - SCOPUS:85104581737
T3 - LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
SP - 126
EP - 127
BT - LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021
Y2 - 9 March 2021 through 11 March 2021
ER -