Papers
1
Total Citations
3
H-Index
1
About
Shunfeng He is a researcher focused on intelligent control and optimization for robotic systems, with particular expertise in redundant manipulators and metaheuristic algorithms. His most notable work, "Trajectory tracking and obstacle avoidance of a redundant robotic manipulator based on the improved grey wolf optimizer" (2023), unifies trajectory tracking and obstacle avoidance into a single optimization problem. In this work, He models obstacle space using the bounding box method and employs the GJK algorithm to compute minimum distances between the manipulator and obstacles, then designs an improved grey wolf optimizer to solve the resulting optimization efficiently. This approach enables real-time, collision-free motion planning for redundant arms, a critical challenge in industrial and service robotics. While his citation count is still growing—reflecting the recent publication of his key paper—He’s contribution lies in elegantly merging path planning and obstacle avoidance into a streamlined optimization framework, offering a practical alternative to traditional hierarchical methods. His work is particularly valuable for researchers working on autonomous manipulation in cluttered environments, and it demonstrates a promising direction for applying swarm intelligence to robotic control problems.
Research Focus
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Top Papers
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