Papers
3
Total Citations
30
H-Index
3
About
Junting Fei is a robotics researcher whose work focuses on intelligent motion planning, modular robotics, and human-robot collaboration. Fei’s most-cited paper, “Reinforcement Learning-Based Reactive Obstacle Avoidance Method for Redundant Manipulators” (2022, 22 citations), introduces a novel approach that enables robotic arms to dynamically avoid obstacles while tracking desired trajectories—a critical capability for safe, efficient human-robot interaction. This work addresses a key challenge in industrial and collaborative robotics by replacing conventional, often brittle, obstacle avoidance methods with a learning-based framework. Fei has also made significant contributions to modular robot design, as seen in “Genetic algorithm-based optimal design of modular robot topology” (2023, 5 citations), which uses evolutionary algorithms to optimize the structure of reconfigurable robots. Further advancing this area, Fei’s “S²MBot: A Spherical Self-Reconfigurable Modular Robot with High Torque Output Capability” (2023, 3 citations) presents a novel spherical module designed for in-orbit servicing, emphasizing high torque, precision, and robust communication. Through this work, Fei is pushing the boundaries of adaptable, high-performance robotic systems for space and terrestrial applications.
Research Focus
Key Achievements
Top Papers
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