Papers
13
Total Citations
525
H-Index
6
About
Minglong Li is a versatile researcher whose work bridges two distinct domains: intelligent robotics and hospitality service innovation. In robotics, Li has made substantial contributions to multi-robot coordination, autonomous path planning, and behavior-based control architectures. His development of novel algorithms — including a communication-maintaining auction method for swarm task planning and the BT Expansion algorithm for automated Behavior Tree synthesis — addresses fundamental challenges in deploying intelligent, fault-tolerant robot teams in complex real-world environments such as disaster search and rescue scenarios. His parallel deep reinforcement learning platform further demonstrates a commitment to scalable, practical robotics infrastructure. Equally notable is Li's influence in hospitality and service research. His 2019 study on service robots and rapport-building has accumulated an impressive 386 citations, establishing him as a leading voice on how robotic attributes shape customer experience and relationship dynamics. His more recent work examining employee-robot hybrid teams and value co-creation continues this trajectory, reflecting timely engagement with the evolving human-robot service landscape. With a publication record spanning swarm robotics, reinforcement learning, robot software frameworks, and consumer behavior, Li exemplifies interdisciplinary breadth, offering insights that are simultaneously technically rigorous and practically relevant across industries.
Research Focus
Key Achievements
Top Papers
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- 3Rapid path planning algorithm for mobile robot in dynamic environment32 citations · 2017
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- 9Optimizing High-dimensional Learner with Low-Dimension Action Features4 citations · 2019
- 10MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration3 citations · 2025