Yi Guan

Harbin Institute of Technology, National Institute of Advanced Industrial Science and Technology

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About

Yi Guan’s research lies at the intersection of multi-robot coordination and humanoid robotics, with a particular focus on intelligent task allocation, autonomous navigation, and whole-body motion planning. His work on multi-robot task allocation, which integrates contract net protocols with neural networks (14 citations), provides a foundational framework for distributed robotic systems. In humanoid robotics, Guan has made notable contributions to localization and path planning. His hybrid map-based localization method, developed for the NAO humanoid robot, combines global topological maps with local metrical maps using Rao-Blackwellized particle filters (7 citations), enabling robust indoor navigation. He also advanced footstep planning by introducing a virtual force-directed Particle Swarm Optimization (PSO) approach, where obstacles exert repulsive forces and targets attract the robot, effectively merging PSO with footstep-level motion (5 citations). His earlier work on whole-body motion planning for humanoid robots (8 citations) reached an international audience, underscoring its relevance. Guan’s research is distinguished by its practical integration of learning, optimization, and perception, addressing core challenges in making humanoid robots autonomous and adaptable in real-world environments.