An Improved Q-Learning Algorithm for Human-robot Collaboration Two-sided Disassembly Line Balancing Problems
Yizhi Liu, Menchu Zhou, Xiwang Guo
Abstract
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.