TY - GEN
T1 - Deep Reinforcement Learning for Prefab Assembly Planning in Robot-based Prefabricated Construction
AU - Zhu, Aiyu
AU - Xu, Gangyan
AU - Pauwels, Pieter
AU - De Vries, Bauke
AU - Fang, Meng
N1 - Funding Information:
ACKNOWLEDGMENT The support provided by the China Scholarship Council (No.202007720036) for the PhD of Zhu Aiyu at Eindhoven University of Technology is acknowledged.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/23
Y1 - 2021/8/23
N2 - Smart construction has raised higher automation requirements of construction processes. The traditional construction planning does not match the demands of integrating smart construction with other technologies such as robotics, building information modelling (BIM), and internet of things (IoT). Therefore, more precise and meticulous construction planning is necessary. In this paper, leveraging recent advances in deep Reinforcement Learning (DRL), we design simulated construction environments for deep reinforcement learning and integrate these environments with deep Q-learning methods. We develop reliable controllers for assembly planning for prefabricated construction. For this, we first show that hand-designed rewards work well for these tasks; then we show deep neural policies can achieve good performance for some simple tasks.
AB - Smart construction has raised higher automation requirements of construction processes. The traditional construction planning does not match the demands of integrating smart construction with other technologies such as robotics, building information modelling (BIM), and internet of things (IoT). Therefore, more precise and meticulous construction planning is necessary. In this paper, leveraging recent advances in deep Reinforcement Learning (DRL), we design simulated construction environments for deep reinforcement learning and integrate these environments with deep Q-learning methods. We develop reliable controllers for assembly planning for prefabricated construction. For this, we first show that hand-designed rewards work well for these tasks; then we show deep neural policies can achieve good performance for some simple tasks.
UR - http://www.scopus.com/inward/record.url?scp=85116991065&partnerID=8YFLogxK
U2 - 10.1109/CASE49439.2021.9551402
DO - 10.1109/CASE49439.2021.9551402
M3 - Conference article published in proceeding or book
AN - SCOPUS:85116991065
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1282
EP - 1288
BT - 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021
PB - IEEE Computer Society
T2 - 17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Y2 - 23 August 2021 through 27 August 2021
ER -