Songlin Chen
Papers
9
Total Citations
65
H-Index
5
About
Songlin Chen is a robotics researcher whose work spans bio-inspired underwater vehicles, autonomous control systems, and human-robot interaction. His primary research areas include robotic fish dynamics and control, visual SLAM in dynamic environments, and disturbance rejection control for self-balancing systems. Chen’s most impactful contribution is his work on tail-actuated robotic fish, where he developed control-oriented averaging methods based on Lighthill’s large-amplitude elongated-body theory, enabling efficient target tracking and energy-optimal path planning for carangiform robots. His 2013 paper on averaging robotic fish dynamics has garnered 13 citations, while his 2022 study on visual SLAM in dynamic scenes—integrating object tracking and static points detection—has reached 17 citations, reflecting growing interest in robust perception for mobile robots. Chen also advanced control design for two-wheeled self-balancing robots using Active Disturbance Rejection Control (ADRC), achieving stable upright control with 12 citations. His recent work (2025) on force sensorless lead-through programming for direct drive manipulators enhances physical human-robot interaction compliance, demonstrating his versatility. With a career spanning foundational bio-robotics theory to practical control solutions, Chen’s research offers valuable insights for students exploring autonomous systems, underwater robotics, and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1
- 2Control-oriented averaging of tail-actuated robotic fish dynamics13 citations · 2013
- 3Study on control design of a two-wheeled self-balancing robot based on ADRC12 citations · 2016
- 4
- 5Target-tracking control design for a robotic fish with caudal fin6 citations · 2013
- 6
- 7
- 8
- 9On the shortest path planning for the carangiform robotic fish2 citations · 2014