Hongliang Li

Zhejiang University, Xi'an Jiaotong University, University of Electronic Science and Technology of China

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About

Hongliang Li is a versatile researcher whose work spans robotics, autonomous navigation, and multimodal artificial intelligence. His most influential contribution, "CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints" (2021), addresses one of the core challenges in autonomous systems — coordinating multiple robots with realistic movement constraints in complex environments. With 83 citations, this work has established him as a notable voice in the multi-agent path planning community, where balancing computational efficiency with real-world applicability remains an open and critical challenge. His earlier work on trajectory tracking control for wheeled mobile robots (2009) demonstrated a foundational interest in adaptive kinematics modeling, introducing single-point preview strategies to overcome limitations of classical control methods. More recently, Li has expanded into cutting-edge multimodal AI, contributing to audio-visual segmentation through cross-modal cognitive consensus mechanisms — a reflection of growing interest in intelligent perception for augmented reality and robotic systems. Across his career, Li's research consistently bridges theoretical rigor with practical robotics applications, making his work valuable to students and researchers working at the intersection of autonomous systems, motion planning, and embodied AI.