Advances in construction automation research are currently validating a shift from industrial machines towards distributed and mobile material-robot construction systems. The potential of these systems lies in their adaptability and robustness, allowing them to operate in dynamic environments, to collaborate in large teams, and at potentially unlimited scales. This research synthesizes AEC and AI to address the problem of task and motion planning for such multi-machine systems. We will investigate how to combine reinforcement learning (RL) with Logic-Geometric Programming (LGP) to form a task and motion planning strategy that can solve collaborative construction problems.
Further information at IntCDC.