Optimizing Energy Efficiency with Configuration Constraints for AMR Trajectory Planning

Abstract

Autonomous Mobile Robots (AMRs) play a crucial role in transporting materials across expansive manufacturing facilities and warehouses. Their successful deployment relies on three major factors: task allocation, task scheduling, and trajectory planning. These processes collectively shape the efficiency and effectiveness of AMRs in complex manufacturing and warehouse environments. This study focuses on AMR trajectory planning, emphasizing energy efficiency beyond traditional methods. We present a physics-oriented AMR model and an optimal control strategy to generate energy-optimized routes. Through simulation studies across different scenarios, we evaluate the efficacy of diverse numerical solutions and compare two different AMR designs, one with Ackermann steering and the other with Mecanum steering. Our results indicate that the proposed approach yields a 5-10% energy advantage over traditional shortest-path algorithms, without compromising computational integrity and efficiency. The saving is more pronounced for the AMR with Ackermann steering. These findings are also validated with an experimental study.