Lexicographic Optimization-Based Model Predictive Control Framework for Rigid-Formation-Based Collaborative Transportation*

As manufacturing continues to advance, autonomous mobile robots (AMRs) are becoming increasingly essential in logistics transportation. However, individual AMRs fall short in terms of size and payload capacity, restricting their ability to transport large or heavy objects. To address this limitation, collaborative systems composed of multiple AMRs have emerged as a promising solution for transporting oversized items. Despite their potential, existing collaborative transportation methods for controlling the system encounter several challenges, including formation maintenance, trajectory optimization, and obstacle avoidance, each of which holds varying levels of priority. To effectively manage these challenges and manage task prioritization, this study proposes a lexicographic optimization-based model predictive control (LO-MPC) framework. Through simulation experiments, the proposed framework demonstrates its ability to ensure a safe and reliable transportation process.