TY - GEN
T1 - Topological Structure Learning Based Enclosing Formation Behavior for Monitoring System
AU - Toda, Yuichiro
AU - Kubota, Naoyuki
N1 - Funding Information:
ACKNOWLEDGMENT This work was partially funded by ImPACT Program of the Council for Science, Technology and Innovation, Japan.
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - Recently, the expectation to teleoperated mobile robots has been increasing much in order to perform the monitoring in various scenes. However, there are many critical problems in the teleoperated mobile robots. In this paper, we discuss cooperative formation behavior of teleoperated multiple robots. Especially, we focus on an enclosing formation behavior of a target object. First, we define the problem setting of the enclosing formation behavior. In our method, the enclosing formation is divided by two strategies in order to reduce the search space of robot poses. Next, we introduce Batch Learning Growing Neural Gas (BL-GNG) in order to improve the learning convergence and reduce the user-designed parameters in GNG. BL-GNG uses an objective function based on Fuzzy C-means for improving the learning convergence. Furthermore, we apply two-layers BL-GNG to decide the positions of enclosing formation. Finally, we show several experimental results of the proposed method.
AB - Recently, the expectation to teleoperated mobile robots has been increasing much in order to perform the monitoring in various scenes. However, there are many critical problems in the teleoperated mobile robots. In this paper, we discuss cooperative formation behavior of teleoperated multiple robots. Especially, we focus on an enclosing formation behavior of a target object. First, we define the problem setting of the enclosing formation behavior. In our method, the enclosing formation is divided by two strategies in order to reduce the search space of robot poses. Next, we introduce Batch Learning Growing Neural Gas (BL-GNG) in order to improve the learning convergence and reduce the user-designed parameters in GNG. BL-GNG uses an objective function based on Fuzzy C-means for improving the learning convergence. Furthermore, we apply two-layers BL-GNG to decide the positions of enclosing formation. Finally, we show several experimental results of the proposed method.
KW - Growing Neural Gas
KW - Tele-monitoring system
UR - http://www.scopus.com/inward/record.url?scp=85062216687&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062216687&partnerID=8YFLogxK
U2 - 10.1109/SMC.2018.00149
DO - 10.1109/SMC.2018.00149
M3 - Conference contribution
AN - SCOPUS:85062216687
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 831
EP - 836
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Y2 - 7 October 2018 through 10 October 2018
ER -
