TY - GEN
T1 - Safe Persistent Coverage Control with Control Barrier Functions Based on Sparse Bayesian Learning
AU - Mizuta, Kazuki
AU - Hirohata, Yasuhide
AU - Yamauchi, Junya
AU - Fujita, Masayuki
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, we propose a control algorithm to explore an unknown environment while guaranteeing the safety of robots by learning safety constraints from sensor information. A sparse Bayesian classifier (SBC) is trained to estimate the probability that the robots will not collide with obstacles at each point based on the local distance data to obstacles obtained from onboard sensors. Then, we propose a control barrier function (CBF), named an SBCBF, which is used to avoid obstacles estimated by the SBC. We also develop a persistent coverage control based on the SBCBF for exploring the area keeping the robot at a given safety level. Furthermore, we build an online control algorithm that integrates the SBCBF synthesis and safe persistent coverage control. Finally, we demonstrate the effectiveness of the proposed algorithm by the simulation and experiment.
AB - In this paper, we propose a control algorithm to explore an unknown environment while guaranteeing the safety of robots by learning safety constraints from sensor information. A sparse Bayesian classifier (SBC) is trained to estimate the probability that the robots will not collide with obstacles at each point based on the local distance data to obstacles obtained from onboard sensors. Then, we propose a control barrier function (CBF), named an SBCBF, which is used to avoid obstacles estimated by the SBC. We also develop a persistent coverage control based on the SBCBF for exploring the area keeping the robot at a given safety level. Furthermore, we build an online control algorithm that integrates the SBCBF synthesis and safe persistent coverage control. Finally, we demonstrate the effectiveness of the proposed algorithm by the simulation and experiment.
UR - http://www.scopus.com/inward/record.url?scp=85144594218&partnerID=8YFLogxK
U2 - 10.1109/CCTA49430.2022.9966178
DO - 10.1109/CCTA49430.2022.9966178
M3 - 会議への寄与
AN - SCOPUS:85144594218
T3 - 2022 IEEE Conference on Control Technology and Applications, CCTA 2022
SP - 311
EP - 318
BT - 2022 IEEE Conference on Control Technology and Applications, CCTA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Conference on Control Technology and Applications, CCTA 2022
Y2 - 23 August 2022 through 25 August 2022
ER -