Felbrich Benjamin, Schork Tim, Menges Achim
University of Stuttgart, Institute for Computational Design and Construction, Stuttgart, Germany.
School of Architecture, Faculty of Design, Architecture and Building, University of Technology Sydney, Sydney, Australia.
Constr Robot. 2022;6(1):15-37. doi: 10.1007/s41693-022-00069-0. Epub 2022 May 23.
The objective of autonomous robotic additive manufacturing for construction in the architectural scale is currently being investigated in parts both within the research communities of computational design and robotic fabrication (CDRF) and deep reinforcement learning (DRL) in robotics. The presented study summarizes the relevant state of the art in both research areas and lays out how their respective accomplishments can be combined to achieve higher degrees of autonomy in robotic construction within the Architecture, Engineering and Construction (AEC) industry. A distributed control and communication infrastructure for agent training and task execution is presented, that leverages the potentials of combining tools, standards and algorithms of both fields. It is geared towards industrial CDRF applications. Using this framework, a robotic agent is trained to autonomously plan and build structures using two model-free DRL algorithms (TD3, SAC) in two case studies: robotic block stacking and sensor-adaptive 3D printing. The first case study serves to demonstrate the general applicability of computational design environments for DRL training and the comparative learning success of the utilized algorithms. Case study two highlights the benefit of our setup in terms of tool path planning, geometric state reconstruction, the incorporation of fabrication constraints and action evaluation as part of the training and execution process through parametric modeling routines. The study benefits from highly efficient geometry compression based on convolutional autoencoders (CAE) and signed distance fields (SDF), real-time physics simulation in CAD, industry-grade hardware control and distinct action complementation through geometric scripting. Most of the developed code is provided open source.
The online version contains supplementary material available at 10.1007/s41693-022-00069-0.
目前，建筑规模的自主机器人增材制造目标正在计算设计与机器人制造（CDRF）研究社区以及机器人领域的深度强化学习（DRL）中分别进行研究。本研究总结了这两个研究领域的相关技术现状，并阐述了如何将它们各自的成果相结合，以在建筑、工程和施工（AEC）行业的机器人施工中实现更高程度的自主性。提出了一种用于智能体训练和任务执行的分布式控制与通信基础设施，该设施利用了两个领域的工具、标准和算法相结合的潜力。它适用于工业CDRF应用。使用此框架，在两个案例研究中训练了一个机器人智能体，以使用两种无模型DRL算法（TD3、SAC）自主规划和构建结构：机器人砌块堆叠和传感器自适应3D打印。第一个案例研究旨在证明计算设计环境对DRL训练的一般适用性以及所使用算法的比较学习成功率。案例研究二突出了我们的设置在刀具路径规划、几何状态重建、制造约束的纳入以及通过参数化建模例程作为训练和执行过程一部分的动作评估方面的优势。该研究受益于基于卷积自动编码器（CAE）和符号距离场（SDF）的高效几何压缩、CAD中的实时物理模拟、工业级硬件控制以及通过几何脚本进行的独特动作补充。大部分开发的代码都是开源提供的。
在线版本包含可在10.1007/s41693-022-00069-0获取的补充材料。
