Deep Reinforcement Learning for Prefab Assembly Planning in Robot-based Prefabricated Construction

Aiyu Zhu, Gangyan Xu, Pieter Pauwels, Bauke De Vries, Meng Fang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021
PublisherIEEE Computer Society
Pages1282-1288
Number of pages7
ISBN (Electronic)9781665418737
DOIs
Publication statusPublished - 23 Aug 2021
Externally publishedYes
Event17th IEEE International Conference on Automation Science and Engineering, CASE 2021 - Lyon, France
Duration: 23 Aug 202127 Aug 2021

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2021-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Country/TerritoryFrance
CityLyon
Period23/08/2127/08/21

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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