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About

Jing Yang is a robotics and autonomous systems researcher whose work spans path planning, motion algorithms, and workspace estimation for mobile robots. Best known for pioneering contributions to surface path planning, Yang's 2020 paper "Surface Optimal Path Planning Using an Extended Dijkstra Algorithm" has garnered an impressive 224 citations, demonstrating the broad relevance of adapting classical graph-search methods to complex real-world terrains such as planetary surfaces and unstructured outdoor environments. This work has become a key reference for researchers tackling navigation challenges in robotics, gaming, and space exploration. Beyond surface planning, Yang has made meaningful contributions to multi-robot coordination, developing a bioinspired neural network approach for 3D path planning in pursuit scenarios, and advancing probabilistic methods for estimating robot reachable workspaces — a fundamental challenge in robotic motion planning for cluttered and dynamically changing environments. Earlier foundational work from 2009 and 2011 on hierarchical probabilistic workspace estimation reflects a sustained research trajectory in intelligent motion planning. Yang's portfolio also includes practical engineering contributions, such as a chassis speed algorithm for competitive wheeled robots. Together, these works position Jing Yang as a versatile contributor to both theoretical and applied robotics research.