About

Saixuan Chen is a robotics researcher whose work sits at the intersection of medical robotics, collaborative robot control, and human-robot safety. Chen’s primary contributions include developing a precise positioning method for puncture robots using a PSO-optimized BP neural network algorithm (87 citations), which addresses the fundamental challenge of inverse kinematics in robot control. This work has direct applications in computer-assisted surgery, particularly for needle guidance and positioning. Chen also proposed a universal algorithm for sensorless collision detection of robot actuator faults (30 citations), enhancing robot safety in human-occupied environments, and developed a zero-moment control algorithm for direct teaching of collaborative robots (13 citations), enabling intuitive human-robot interaction through gravity and friction compensation. Additional work includes analytical inverse kinematics solutions for 6-DOF collaborative robots and adaptive threshold collision detection using fuzzy systems. Chen’s research is notable for its practical focus on medical robotics—specifically puncture procedures—and for advancing the safety and teachability of collaborative robots. With over 160 total citations, Chen’s work continues to influence the design of safer, more intuitive robotic systems for both industrial and clinical settings.

Research Focus

Key Achievements

5
H-Index
12
Papers
167
Total Citations
14
Avg Citations/Paper
🏆 Most Cited Paper
A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm
87 citations · 2017
📈 Most Prolific Year: 2018 (3 Papers)
🤝 Key Collaborators: 18
🏛 Institutions: University of Science and Technology of China, Shanghai University of Engineering Science, Changzhou University

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago