Deep Reinforcement Learning for Real-Time Assembly Planning in Robot-Based Prefabricated Construction

Aiyu Zhu, Tianhong Dai, Gangyan Xu, Pieter Pauwels, Bauke de Vries, Meng Fang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


The adoption of robotics is promising to improve the efficiency, quality, and safety of prefabricated construction. Besides technologies that improve the capability of a single robot, the automated assembly planning for robots at construction sites is vital for further improving the efficiency and promoting robots into practices. However, considering the highly dynamic and uncertain nature of a construction environment, and the varied scenarios in different construction sites, it is always challenging to make appropriate and up-to-date assembly plans. Therefore, this paper proposes a Deep Reinforcement Learning (DRL) based method for automated assembly planning in robot-based prefabricated construction. Specifically, a re-configurable simulator for assembly planning is developed based on a Building Information Model (BIM) and an open game engine, which could support the training and testing of various optimization methods. Furthermore, the assembly planning problem is modelled as a Markov Decision Process (MDP) and a set of DRL algorithms are developed and trained using the simulator. Finally, experimental case studies in four typical scenarios are conducted, and the performance of our proposed methods have been verified, which can also serve as benchmarks for future research works within the community of automated construction. Note to Practitioners - This paper is conducted based on the comprehensive analysis of real-life assembly planning processes in prefabricated construction, and the methods proposed could bring many benefits to practitioners. Firstly, the proposed simulator could be easily re-configured to simulate diverse scenarios, which can be used to evaluate and verify the operations' optimization methods and new construction technologies. Secondly, the proposed DRL-based optimization methods can be directly adopted in various robot-based construction scenarios, and can also be tailored to support the assembly planning in traditional human-based or human-robot construction environments. Thirdly, the proposed DRL methods and their performance in the four typical scenarios can serve as benchmarks for proposing new advanced construction technologies and optimization methods in assembly planning.

Original languageEnglish
Pages (from-to)1515-1526
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Issue number3
Publication statusPublished - 1 Jul 2023


  • assembly planning
  • automated construction
  • building information modelling (BIM)
  • deep reinforcement learning (DRL)
  • Prefabricated construction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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