在计算设计环境中,通过分布式无模型深度强化学习进行自主机器人增材制造.
Benjamin Felbrich1,2, Tim Schork2, Achim Menges1,3
1University of Stuttgart, Institute for Computational Design and Construction, Stuttgart, Germany.
概括
这项研究将计算设计和机器人制造 (CDRF) 与深度强化学习 (DRL) 结合起来,以推进自主机器人制造. 它为训练机器人代理人设计和建造结构提供了一个框架,增强了AEC行业的自主性.
科学领域:
- 机器人和自动化机器人与自动化
- 计算设计的计算设计.
- 增材制造 增材制造 增材制造
背景情况:
- 自主机器人建设是一个新兴领域,研究分为计算设计和机器人制造 (CDRF) 和深度增强学习 (DRL).
- 结合这些领域可以显著增加建筑,工程和建筑 (AEC) 行业的自主性.
研究的目的:
- 整合CDRF和DRL,以实现增强的自主机器人构造.
- 提供分布式控制和通信基础设施,用于工业CDRF应用中的培训和任务执行.
- 通过机器人块堆叠和传感器适应3D打印的案例研究来证明该框架的有效性.
主要方法:
- 利用分布式控制和通信基础设施进行代理人培训和任务执行.
- 采用了两种无模型深度强化学习算法 (TD3,SAC) 来训练机器人代理.
- 实现了高效的几何压缩 (CAE,SDF),实时物理模拟和几何脚本.
主要成果:
- 展示了DRL培训的计算设计环境的适用性,并比较了TD3和SAC算法的学习成功率.
- 展示了工具路径规划,几何状态重建和通过参数建模结合制造约束的好处.
- 在案例研究中实现了结构的自主规划和建造.
结论:
- 集成的CDRF和DRL框架使机器人构建具有更高的自主性.
- 所介绍的基础设施和方法适用于工业CDRF应用.
- 提供开源代码以促进进一步的研究和开发.
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