TY - GEN
T1 - An Improved Q-Learning Algorithm for Human-robot Collaboration Two-sided Disassembly Line Balancing Problems
AU - Liu, Yi Zhi
AU - Zhou, Men Chu
AU - Guo, Xiwang
N1 - Funding Information:
This work is supported in part by Liaoning Province Education Department Scientific Research Foundation of China under Grant No. L2019027; LiaoNing Revitalization Talents Program under Grant No. XLYC1907166; The Natural Science Foundation of Shandong Province under Grant ZR2019BF004; National Natural Science Foundation of China (61573089); Archival Science and Technology Project of Liaoning Province, No.2021-B-004. Education Ministry Humanities and Social Science Research Youth Fund Project of China under grant 20YJCZH159.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - If people simply trash their used products, they would face many issues such as pollution to environment and resource waste. Recycling and remanufacturing used products are thus necessary, which makes the study of disassembly line balancing problems important. At present, manual disassembly is popular and it does not guarantee personal safety in the event of dangerous disassembly parts. Targeting at this problem, a mixed human-robot disassembly method is proposed. An improved Q-learning algorithm based on reinforcement learning is used to solve the two-sided disassembly line balancing problem with the objective of minimizing total disassembly time. The improved algorithm is compared with the SARSA algorithm. The results show that it can find better solutions than SARSA, and outperforms SARSA particularly in large-scale cases.
AB - If people simply trash their used products, they would face many issues such as pollution to environment and resource waste. Recycling and remanufacturing used products are thus necessary, which makes the study of disassembly line balancing problems important. At present, manual disassembly is popular and it does not guarantee personal safety in the event of dangerous disassembly parts. Targeting at this problem, a mixed human-robot disassembly method is proposed. An improved Q-learning algorithm based on reinforcement learning is used to solve the two-sided disassembly line balancing problem with the objective of minimizing total disassembly time. The improved algorithm is compared with the SARSA algorithm. The results show that it can find better solutions than SARSA, and outperforms SARSA particularly in large-scale cases.
KW - Q-learning algorithm
KW - Reinforcement learning
KW - human-robot combination
KW - two-sided disassembly line balancing
UR - http://www.scopus.com/inward/record.url?scp=85142713541&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142713541&partnerID=8YFLogxK
U2 - 10.1109/SMC53654.2022.9945263
DO - 10.1109/SMC53654.2022.9945263
M3 - Conference contribution
AN - SCOPUS:85142713541
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 568
EP - 573
BT - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
Y2 - 9 October 2022 through 12 October 2022
ER -