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Tianheng Feng is a leading researcher in autonomous mobile robotics, with a focus on real-time scheduling, trajectory planning, and task allocation for multi-robot systems in manufacturing automation. His work addresses critical challenges in factory automation, particularly how to coordinate multiple autonomous mobile robots (AMRs) operating under task space constraints and priorities. Feng’s most cited paper, “Fast Scheduling of Autonomous Mobile Robots Under Task Space Constraints With Priorities” (2019, 17 citations), introduces a novel scheduling framework that balances efficiency with real-time feasibility. He further advanced the field with “Energy-Conscientious Trajectory Planning for an Autonomous Mobile Robot in an Asymmetric Task Space” (2020, 14 citations), which integrates energy optimization into motion planning—a key concern for sustainable automation. His contributions include a regularized quadratic programming approach for real-time scheduling and a probabilistic technique for task allocation among robot teams. Collectively, his work has garnered over 37 citations, reflecting its growing influence in both academia and industry. Feng’s research is particularly notable for bridging theoretical control methods with practical deployment challenges, making him a significant voice in the next generation of smart manufacturing systems.