Siyun Tan
Papers
2
Total Citations
7
H-Index
2
About
Siyun Tan is a rising researcher in robotics and intelligent control, with a focus on solving complex motion planning and kinematic challenges in unstructured environments. Their work addresses critical limitations in robotic arm path planning and inverse kinematics, where traditional algorithms often suffer from inefficiency, low precision, and excessive sampling randomness. Tan’s most-cited paper, “MMD-RRT: a path planning strategy for robotic arm with improved RRT algorithm in unstructured environments” (2025, 5 citations), introduces a multi-mode dynamic sampling approach that significantly enhances search efficiency and reduces redundant path nodes. Building on this, their second highly cited work, “Improved dung beetle optimization algorithm based inverse kinematics solution for robotic arm” (2025, 2 citations), proposes the ECDBO algorithm—a multi-strategy improvement that boosts population initialization, global search capability, and solution precision for six-axis robotic arms. Though early in their career, Tan’s publications already demonstrate a clear trajectory toward practical, high-performance solutions for real-world robotics. Their innovative fusion of bio-inspired optimization with classical path planning methods marks them as a promising contributor to the field of autonomous robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1
- 2