TY - GEN
T1 - A deep neural network based human following robot with fuzzy control
AU - Aye, Yin Yin
AU - Thiha, Kyaw
AU - Myint Pyu, Mi Mi
AU - Watanabe, Keigo
N1 - Funding Information:
ACKNOWLEDGMENT This research received funding from the Japan International Cooperation Agency (JICA) EEHT Project, which is sponsored by the Japanese government. I would like to thank JICA EEHE Project for financial support.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - This research aims to build a human following robot system based on a deep neural network algorithm in which a fuzzy controller controls the robot velocity and keeps the target person in the centre position of robot's view. Firstly, the system utilizes the deep neural network algorithm to detect a target person in the video sequence captured from a real sense D435 depth camera mounted on the mobile robot. Then, the system calculates the centre position of the target human and acquires the depth value of target human. Finally, these data are used as the inputs of a fuzzy controller to control the velocity and steering of the robot during tracking. Especially, the velocity of the robot which is normally limited as a constant in most existing human following robot systems is controlled by a fuzzy controller in this paper. The proposed system is verified through the experiments for a four-wheel steered mobile robot.
AB - This research aims to build a human following robot system based on a deep neural network algorithm in which a fuzzy controller controls the robot velocity and keeps the target person in the centre position of robot's view. Firstly, the system utilizes the deep neural network algorithm to detect a target person in the video sequence captured from a real sense D435 depth camera mounted on the mobile robot. Then, the system calculates the centre position of the target human and acquires the depth value of target human. Finally, these data are used as the inputs of a fuzzy controller to control the velocity and steering of the robot during tracking. Especially, the velocity of the robot which is normally limited as a constant in most existing human following robot systems is controlled by a fuzzy controller in this paper. The proposed system is verified through the experiments for a four-wheel steered mobile robot.
KW - A car-like mobile robot
KW - Deep neural network
KW - Fuzzy control
KW - Human tracking
UR - http://www.scopus.com/inward/record.url?scp=85079072976&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079072976&partnerID=8YFLogxK
U2 - 10.1109/ROBIO49542.2019.8961577
DO - 10.1109/ROBIO49542.2019.8961577
M3 - Conference contribution
AN - SCOPUS:85079072976
T3 - IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
SP - 720
EP - 725
BT - IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
Y2 - 6 December 2019 through 8 December 2019
ER -