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About

Yixiong Liang is a robotics researcher whose work centers on autonomous navigation, exploration, and perception for Unmanned Aerial Vehicles (UAVs) and intelligent robotic systems. His research spans autonomous exploration strategies, deep reinforcement learning, and semantic navigation, with a particular focus on enabling robots to efficiently map and navigate unknown environments without human supervision. Liang's most influential contribution, "Information-Driven Fast Marching Autonomous Exploration with Aerial Robots" (2021, 46 citations), introduced a frontier-based exploration framework leveraging the fast marching method to guide UAVs more intelligently through uncharted spaces. Building on this foundation, he developed STExplorer (2023), a hierarchical strategy incorporating spatio-temporal awareness to address limitations in information gain estimation and cost budgeting. His work on EMExplorer further advances the field by integrating episodic memory and deep reinforcement learning with innovative Voronoi domain conversion techniques, while SemNav-HRO extends his expertise into semantic target-driven navigation through a novel human–robot–object ternary fusion framework. With a growing body of work accumulating over 75 citations, Liang represents an emerging voice in autonomous robotics research, consistently pushing toward more intelligent, efficient, and context-aware robotic systems.