The construction industry is increasingly challenged by an aging workforce and persistent labor shortages, underscoring the need for automation and the integration of construction robotics. However, the high uncertainty and variability of real construction environments impose significant constraints on robot design and deployment. In particular, accurately estimating the required operational force—without unnecessary overdesign—is essential for ensuring operational safety, energy efficiency, and battery endurance. Conducting on-site experiments that reflect diverse field conditions is often impractical, making simulation-based approaches a viable alternative. This study proposes a simulation-driven method for deriving energy-efficient, task-appropriate operational forces for construction robots. As a case study, an aluminum formwork dismantling operation was modeled in NVIDIA Isaac Sim, and a dataset of environmental variables was generated through random sampling. Sensitivity analysis revealed that the dynamic friction coefficient at the aluminum–aluminum interface had the greatest impact on the required dismantling force. To mitigate this influence, a lubrication strategy was introduced to reduce surface friction. With a 10% safety margin applied, the dismantling operation achieved a 99.5% success probability at an operational force of 50 N-representing an 11.71 N reduction and an 18.97% decrease compared to the non-lubricated scenario. These results demonstrate a practical and evidence-based approach for optimizing operational forces in construction robotics, contributing to reduced energy consumption, improved operational efficiency, and mitigation of construction schedule delays.
Kim et al. (Thu,) studied this question.