Shijie Guo

Papers

Total Citations

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About

Shijie Guo is an emerging researcher in the field of robotics, with a focused interest in continuum robot systems and motion planning algorithms. Their most notable work introduces RRT-based CPC, a configuration planning method that applies the Rapidly-exploring Random Tree (RRT) algorithm to address the complex challenge of path and configuration planning for continuum robots — a class of flexible, biologically inspired robotic structures that present unique kinematic challenges compared to traditional rigid-link robots. This contribution represents a meaningful step forward in making continuum robots more practically deployable in constrained or unstructured environments, such as surgical or inspection applications, where precise yet adaptable motion planning is critical. Published in 2025, the work has already begun attracting attention within the robotics community, accumulating early citations that signal growing interest from peers. While Guo's publication record is still developing, their research sits at an exciting intersection of computational planning, robot kinematics, and applied robotics — areas of rapidly expanding importance as soft and continuum robotic systems move closer to real-world deployment. Researchers working in medical robotics, flexible manipulators, or sampling-based motion planning will find Guo's contributions particularly relevant.