摘要

近年来，由于工作环境恶劣、人口老龄化加剧等多重因素影响，建筑业劳动力缺口日益扩大，对建筑/施工机器人的关注与应用不断提升。同时，施工现场又具有高度的复杂性和动态性，研发完全自主的施工机器人仍然任重道远，工程施工将长期处于工人、机器人共存的状态。在这种情况下，工人和机器人应该如何协同配合？不同人机协同特征将如何影响施工效率呢？

围绕这个问题，课题组选择机器人砖墙施工场景，提出了基于智能体的人机协同建造过程多尺度建模仿真方法，支持单机-多人、多机-多人等不同场景的仿真，并通过仿真初步分析了不同的补砖策略、人机沟通模式、人因特征等对施工效率的影响。研究表明：1）人机协同建造过程具有高度的复杂性；2）单机-多人与多机-多人协同对施工效率具有不同的影响规律；3）人机协同对施工效率的影响呈现尺度效应，人机比例不变的情况下施工效率与机器人数量呈正相关性；4）主动式人机交互相比被动观察可大幅提升施工效率。本研究为认识复杂人机协同建造过程、优化人机协同方式以及新型施工机器人研发评估等提供了高效的仿真分析方法，对推动智能建造与建筑机器人发展具有明显价值。

Though construction robots have drawn attention in research and practice for decades, human-robot collaboration (HRC) remains important to conduct complex construction tasks. Considering its complexity and uniqueness, it is still unclear how HRC process will impact construction productivity, which is difficult to handle with conventional methods such as field tests, mathematical modeling and physical simulation approaches. To this end, an agent-based (AB) multi-fidelity modeling approach is introduced to simulate and evaluate how HRC influences construction productivity. A high-fidelity model is first proposed for a scenario with one robot. Then, a low-fidelity model is established to extract key parameters that capture the inner relationship among scenarios. The multi-fidelity models work together to simulate complex scenarios. Based on the simulation model, the twofold influence of HRC on productivity, namely the supplement strategy on the worker side, and the design for proactive interaction on the robot side, are fully investigated. Experimental results show that: 1) the proposed approach is feasible and flexible for simulation of complex HRC processes, and can cover multiple collaboration and interaction modes; 2) the influence of the supplement strategy is simple when there is only one robot, where lower Check Interval (CI) and higher Supplement Limit (SL) will improve productivity. But the influence becomes much more complicated when there are more robots due to the internal competition among robots for the limited time of workers; 3) HRC has a scale effect on productivity per robot, which means the productivity improves if there are more robots and workers, even if the human-robot ratio remains the same; 4) introducing proactive interaction between robots and workers could improve productivity significantly, up to 22% in our experiments, which further depends on the supplement strategy and the human-robot ratio. Overall, this research contributes an integrated approach to simulate and evaluate HRC’s impacts on productivity as well as valuable insights on how to optimize HRC for better performance and occupational health. The proposed approach is also useful for the evaluation and development of new robots.

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The research is supported by the National Key R&D Program of China (No. 2018YFD1100900), the National Natural Science Foundation of China (No. 51908323), the Tsinghua University Initiative Scientific Research Program (No. 2019Z02UOT) and Tsinghua University Students Research Training Program.

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