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About

Yanjie Li is a researcher specializing in multi-agent systems, autonomous robot navigation, and intelligent task coordination. Their work sits at the intersection of robotics, artificial intelligence, and multi-agent planning, with a particular focus on solving complex real-world challenges in robot navigation and coordination. Li's most significant contribution is a comprehensive review of graph-based multi-agent pathfinding (MAPF) solvers, spanning classical and beyond-classical approaches, which has garnered 34 citations since its publication in 2023. This work has become a valuable reference for researchers navigating the rapidly expanding MAPF landscape, reflecting the field's growing importance in multi-robot system deployment. Complementing this, Li's 2020 research introduced a 3D simulation environment leveraging deep reinforcement learning to enable efficient, collision-free robot navigation in densely populated pedestrian settings — a practically significant challenge that has attracted 12 citations. Earlier work from 2014 explored auction-based task assignment strategies for smart logistics centers with autonomous mobile robots, demonstrating Li's long-standing interest in coordinating multi-robot systems in real-world industrial environments. Across their career, Yanjie Li has consistently advanced methods for making multi-robot systems smarter, safer, and more practically deployable — contributions of growing relevance as autonomous systems become increasingly embedded in everyday life.