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.
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.