Real-Time Trajectory Optimization for Continuum Robots in Human–Robot Interaction Using Vision-Based Target Pose Estimation

Abstract

Continuum robots possess intrinsic compliance, high flexibility, and continuously deformable structures, making them well-suited for safe human–robot interaction (HRI). However, their continuous backbone and high degrees of freedom pose significant challenges for real-time trajectory generation: motions must satisfy curvature constraints while adapting to uncertain and rapidly changing human inputs. Existing methods can generate smooth and feasible paths, but many are computationally intensive, neglect curvature continuity or mechanical constraints, or lack adaptability to dynamic environments. As a result, producing smooth, feasible, and responsive trajectories for continuum robots in interactive scenarios remains challenging. To address this, we propose a real-time trajectory optimization framework that integrates temporally filtered, vision-based human intention signals with curvature-constrained planning. Human hand motions are converted into stable reference signals, which guide a sliding-window sequential quadratic programming (SQP) optimizer. The planner continuously generates smooth and feasible trajectories that adapt in real time to evolving inputs. Simulations and hardware experiments demonstrate accurate tracking, robustness to noise, and timely adaptation, highlighting the framework’s potential to enable safe and natural human–continuum robot collaboration in real-world applications.