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About

Jyh-Ming Lien is a computational researcher whose work spans motion planning, multi-agent systems, and geometric computing, with particularly influential contributions to robotics and autonomous navigation. His research has fundamentally advanced how robots and virtual agents navigate complex environments, with his obstacle-based Rapidly-Exploring Random Tree (RRT) variant — garnering over 200 citations — offering more effective exploration of narrow passages in high-dimensional spaces. Lien has also made significant strides in probabilistic roadmap planning for deformable objects and environments, addressing the underexplored challenge of non-rigid robot motion. His medial axis sampling framework provided a generalized template for configuration space exploration that continues to influence the field. Beyond individual robot navigation, Lien has tackled sophisticated multi-agent problems, developing shepherding and flocking behaviors for complex environments — work that bridges robotics and biological simulation with real-world applications. His contributions to group control planning and Minkowski sum boundary computation further demonstrate his geometric and algorithmic range. With research extending into semantic location recognition for outdoor scenes, Lien's portfolio reflects a productive career connecting theoretical geometry with practical autonomous systems, accumulating hundreds of citations across robotics, computer graphics, and computer vision communities.