Shun Jun Chen
Papers
1
Total Citations
32
H-Index
1
About
Shun Jun Chen is a robotics researcher whose work centers on intelligent path planning and collision avoidance for industrial manipulators. His most-cited paper, "6-DOF Robotic Obstacle Avoidance Path Planning Based on Artificial Potential Field Method" (2019, 32 citations), introduces a whole-arm path planning algorithm that leverages artificial potential fields to enable six-degree-of-freedom robots to navigate cluttered environments safely. A key innovation in this work is the integration of a multi-station conversion system for welding robots, where sensor data is used to simplify both obstacles and the robotic arm before computing collision-free trajectories. By transforming the complex geometry of the workspace into a tractable potential field model, Chen's approach allows for real-time, whole-arm obstacle avoidance—critical for industrial applications where precision and safety are paramount. This contribution has been cited by researchers developing autonomous robotic systems for manufacturing, demonstrating its practical relevance. Chen's research addresses fundamental challenges in robotic motion planning, bridging the gap between theoretical algorithms and real-world industrial deployment. His work continues to influence the design of safer, more efficient robotic systems in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1