Mengshi Guo

About

Mengshi Guo is a robotics researcher specializing in path planning and obstacle avoidance for serial manipulators. Their work focuses on developing computationally efficient methods to enable collision-free movement in complex environments. Guo’s most cited paper, "Obstacle-free workspace based path planning for serial manipulator" (2017, 5 citations), introduces a novel approach that combines Monte Carlo methods with computer graphics to create an Obstacle-free workspace (OFW) for multi-joint robots. This work provides a foundation for real-time path planning in industrial and service robotics. A follow-up study, "PATH PLANNING-ORIENTED OBSTACLE AVOIDING WORKSPACE MODELLING FOR ROBOT MANIPULATOR" (2018, 2 citations), further refines the modeling of obstacle-avoiding workspaces tailored to path planning tasks. Though early in their career, Guo’s contributions address a critical challenge in robotics: ensuring safe and efficient motion in cluttered settings. Their research bridges theoretical workspace analysis with practical implementation, offering tools for engineers designing autonomous robotic systems. As the demand for intelligent manipulation grows, Guo’s work remains a valuable reference for advancing robot autonomy.

Research Focus

Key Achievements

Top Papers

1. 1
   Obstacle-free workspace based path planning for serial manipulator

   5 citations · 2017
2. 2
   PATH PLANNING-ORIENTED OBSTACLE AVOIDING WORKSPACE MODELLING FOR ROBOT MANIPULATOR

   2 citations · 2018

Key Collaborators

Contact & Links