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Autonomous robotic additive manufacturing through distributed model-free deep reinforcement learning in computational

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This study combines computational design and robotic fabrication (CDRF) with deep reinforcement learning (DRL) to advance autonomous robotic construction. It presents a framework for training robotic agents to plan and build structures, enhancing autonomy in the AEC industry.

Keywords:
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Area of Science:

  • Robotics and Automation
  • Computational Design
  • Additive Manufacturing

Background:

  • Autonomous robotic construction is an emerging field, with research split between computational design and robotic fabrication (CDRF) and deep reinforcement learning (DRL).
  • Combining these fields can significantly increase autonomy in the Architecture, Engineering, and Construction (AEC) industry.

Purpose of the Study:

  • To integrate CDRF and DRL for enhanced autonomous robotic construction.
  • To present a distributed control and communication infrastructure for training and task execution in industrial CDRF applications.
  • To demonstrate the effectiveness of this framework through case studies in robotic block stacking and sensor-adaptive 3D printing.

Main Methods:

  • Utilized a distributed control and communication infrastructure for agent training and task execution.
  • Employed two model-free deep reinforcement learning algorithms (TD3, SAC) for training robotic agents.
  • Implemented efficient geometry compression (CAE, SDF), real-time physics simulation, and geometric scripting.

Main Results:

  • Demonstrated the applicability of computational design environments for DRL training and compared the learning success of TD3 and SAC algorithms.
  • Showcased benefits in tool path planning, geometric state reconstruction, and fabrication constraint incorporation via parametric modeling.
  • Achieved autonomous planning and building of structures in case studies.

Conclusions:

  • The integrated CDRF and DRL framework enables higher autonomy in robotic construction.
  • The presented infrastructure and methods are suitable for industrial CDRF applications.
  • Open-source code is provided to facilitate further research and development.