Optimizing heterogeneous multi-robot team composition for long-horizon construction tasks: Time- and utilization-guided simulation

To boost productivity and efficiency, construction robots are anticipated to become increasingly prevalent in future construction workplaces. In such multi-robot environments, forming a well-composed robotic team is crucial for efficient task allocation and execution. However, the complexity of multi-robot dynamics, arising from the heterogeneity and varying scales of robotic teams, coupled with long-horizon tasks, presents challenges in estimating team utility and optimizing team composition. To tackle these challenges, a time- and utilization-guided simulation approach is proposed. Specifically, heterogeneous multi-robot dynamics is modeled as a cooperative multi-robot board game, and team utility is estimated using Monte Carlo tree search-based task scheduling. The optimal team composition minimizes completion time and maximizes robot utilization. Simulation results demonstrate the effectiveness of identifying the optimal team composition in three robot collaboration scenarios. This paper contributes to efficient and robust team utility estimation for robotic team formation in long-horizon construction tasks, addressing gaps in existing research.