TY - GEN
T1 - Human Robot Collaboration in Industrial Applications
AU - Yang, Yuyi
AU - Wang, Yanzhang
AU - Cao, Yilin
AU - Zhao, Zhiyang
AU - Liu, Xinpeng
AU - Wang, Yihong
AU - Zhang, Haiyang
AU - Pan, Yushan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Nowadays, in many modern factories, the transport of products and the assembly of parts are carried out by robots. These robots increase production efficiency and save labor and money. However, getting the robotic arms to correctly identify the required parts and transport the products to the right place is still a challenge. To solve this problem, we conducted a user-centered design iteratively. Ideally, the user can specify specific inputs and outputs (i.e., different colors correspond to different exits). And people are also part of the system supervision system so as to give full play to the advantages of humans compared to robots in terms of the subjective initiative. Our system solves real-world classification problems by combining the strengths of machines in repetitive tasks with humans. And use this as an experimental basis to create a questionnaire. We analyzed the questionnaire data and found that our system can better achieve classification through human-robot interaction.
AB - Nowadays, in many modern factories, the transport of products and the assembly of parts are carried out by robots. These robots increase production efficiency and save labor and money. However, getting the robotic arms to correctly identify the required parts and transport the products to the right place is still a challenge. To solve this problem, we conducted a user-centered design iteratively. Ideally, the user can specify specific inputs and outputs (i.e., different colors correspond to different exits). And people are also part of the system supervision system so as to give full play to the advantages of humans compared to robots in terms of the subjective initiative. Our system solves real-world classification problems by combining the strengths of machines in repetitive tasks with humans. And use this as an experimental basis to create a questionnaire. We analyzed the questionnaire data and found that our system can better achieve classification through human-robot interaction.
KW - Collaborative tasks
KW - Color Recognition
KW - Human-Robot Collaboration
KW - Industrial applications
UR - http://www.scopus.com/inward/record.url?scp=85166381571&partnerID=8YFLogxK
U2 - 10.1109/ICVR57957.2023.10169650
DO - 10.1109/ICVR57957.2023.10169650
M3 - Conference Proceeding
AN - SCOPUS:85166381571
T3 - 2023 9th International Conference on Virtual Reality, ICVR 2023
SP - 247
EP - 255
BT - 2023 9th International Conference on Virtual Reality, ICVR 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Virtual Reality, ICVR 2023
Y2 - 12 May 2023 through 14 May 2023
ER -