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About

Yingying Kong is a leading researcher in robotic motion planning and autonomous navigation, with a particular focus on path planning algorithms and traversability analysis for legged robots. Her foundational work on the two-stage ant colony optimization (ACO) algorithm for robotic path planning, published in 2011, has garnered 132 citations, establishing her as a key contributor to efficient, nature-inspired pathfinding methods. This work introduced a fast, two-stage approach that significantly improves computational efficiency in complex environments. Kong further advanced the field by developing a two-stage Rapidly-exploring Random Tree (RRT) algorithm, addressing the challenge of motion planning with dynamics in cluttered settings. More recently, her research has shifted toward quadruped robots, pioneering methods for traversability analysis in outdoor and rough terrain using sparse point clouds. Her 2021 study on traversability analysis for quadruped navigation in outdoor environments, along with her 2022 work on real-time traversability mapping from sparse data, are critical for enabling legged robots to operate autonomously in unstructured, wild environments. Kong’s contributions bridge classical path planning with modern perception-driven navigation, making her work essential for students and researchers advancing field robotics.