TY - GEN
T1 - A Collaborative Control Model for Shop-Floor Material Handling System Based on Artificial Intelligence
AU - Zhu, Zhenfei
AU - Pang, Yifan
AU - Zhang, Yingfeng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Industrial robots like automated guided vehicles (AGV) have drawn increased attention due to their flexibility and low cost. However, there are still several challenges, such as serious waste of vehicle loading capacity and the inability to change strategies when needed. To address these challenges, an Artificial Intelligence (AI) based collaborative control model is proposed. To achieve rapid response to changes from both high-level control systems and plant environment, the optimized decision rules are built to support the AGV self-decision-making to improve flexibility and interoperability. Then, by sharing the information on transport tasks, the vehicle can submit requests to take tasks proactively. The production logistic service system is built to assess the transport capabilities of vehicles and optimize the task plans to decrease the No-load rate of vehicles, improving overall efficiency. The presented method is demonstrated by a set of simulations, which proved that the overall task completion efficiency and the utilization of the vehicles are improved.
AB - Industrial robots like automated guided vehicles (AGV) have drawn increased attention due to their flexibility and low cost. However, there are still several challenges, such as serious waste of vehicle loading capacity and the inability to change strategies when needed. To address these challenges, an Artificial Intelligence (AI) based collaborative control model is proposed. To achieve rapid response to changes from both high-level control systems and plant environment, the optimized decision rules are built to support the AGV self-decision-making to improve flexibility and interoperability. Then, by sharing the information on transport tasks, the vehicle can submit requests to take tasks proactively. The production logistic service system is built to assess the transport capabilities of vehicles and optimize the task plans to decrease the No-load rate of vehicles, improving overall efficiency. The presented method is demonstrated by a set of simulations, which proved that the overall task completion efficiency and the utilization of the vehicles are improved.
KW - Artificial Intelligence
KW - Automated Guided Vehicle
KW - Collaborative control model
KW - rule-based decision making
KW - shop-floor material handling
UR - http://www.scopus.com/inward/record.url?scp=85189172562&partnerID=8YFLogxK
U2 - 10.1109/EIECC60864.2023.10456622
DO - 10.1109/EIECC60864.2023.10456622
M3 - 会议稿件
AN - SCOPUS:85189172562
T3 - 2023 3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023
SP - 364
EP - 370
BT - 2023 3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023
Y2 - 22 December 2023 through 24 December 2023
ER -