Timed-Elastic-Band-Based Variable Splitting for Autonomous Trajectory Planning
Hao Zhu, Kefan Jin, Rui Gao, Jialin Wang, Richard Shi · Symmetry · 2025
Existing trajectory planning methods often face challenges in ensuring stable robot motion control, leading to significant positional errors during navigation. This study proposes Timed-Elastic-Band-Based Variable Splitting (TEB-VS), a novel framework that integrates variable splitting (VS)—a constrained optimization technique—with the classical Timed-Elastic-Band (TEB) algorithm. Unlike incremental modifications to TEB, TEB-VS introduces a systematic combination of VS and TEB to decompose non-convex global constraints into tractable subproblems while leveraging symmetry principles for balanced multi-objective control (e.g., velocity, acceleration, and obstacle avoidance). Experimental results demonstrate that TEB-VS achieves a 46.5% improvement in motion stability over traditional TEB in obstacle-free simulations and a 37% enhancement in dynamic obstacle scenarios. Real-world tests show a 26.7% reduction in angular velocity oscillations, with computational efficiency comparable to TEB. The framework’s effectiveness in harmonizing trajectory smoothness and dynamic adaptability is validated through extensive simulations and TurtleBot2 experiments.