Chengnan Shentu — accepted AI-conference papers

2026

Sampling-Based Follow-the-Leader Motion Planning for Manipulator-Mounted Continuum Robots

Chengnan Shentu, Nicholas Baldassini, Oluwagbotemi D. Iseoluwa, Radian Gondokaryono, Jessica Burgner-Kahrs

RSS 2026poster

Follow-the-leader (FTL) motion exploits the unique morphology of continuum robots (CRs) to navigate confined spaces by having the body retrace the path of the tip. While extensively studied, existing FTL methods typically assume a fixed base or a single degree-of-freedom insertion mechanism, limitin…

Cited by 0 Source ↗Scholar ↗

2025

A Non-Linear Model Predictive Task-Space Controller Satisfying Shape Constraints for Tendon-Driven Continuum Robots

Maximilian Hachen, Chengnan Shentu, Sven Lilge, Jessica Burgner-Kahrs

RA-L 2025

Tendon-driven continuum robots (TDCRs) have the potential to be used in minimally invasive surgery and industrial inspection, where the robot must navigate narrow and confined spaces. We propose a model predictive controller (MPC) to leverage the non-linear kinematics and redundancy of TDCRs for who

Cited by 9 Source ↗Scholar ↗

2024

MoSS: Monocular Shape Sensing for Continuum Robots

Chengnan Shentu, Enxu Li, Chaojun Chen, Puspita Triana Dewi, David B. Lindell, Jessica Burgner-Kahrs

RA-L 2024

Continuum robots are promising candidates for interactive tasks in medical and industrial applications due to their unique shape, compliance, and miniaturization capability. Accurate and real-time shape sensing is essential for such tasks yet remains a challenge. Embedded shape sensing has high hard

Cited by 12 Source ↗code ↗Scholar ↗