提出首个用于连续体机器人的在线连续时间滑窗滤波方法。
A Sliding-Window Filter for Online Continuous-Time Continuum Robot State Estimation
- 设计滑窗滤波框架，实现连续时间状态估计的在线化
- 运行速度超实时，保持高精度且计算高效
- 适合需要实时高精度状态估计的柔性机器人场景
连续体机器人（CRs）的随机状态估计算法常难以兼顾精度与计算效率。现有滑窗方法多基于简化离散时间近似，缺乏概率表征；而传统随机滤波器需与测量同步运行，无法充分发挥潜力。尽管近期连续时间估计算法在理论上表现优异，但仅限于离线使用。本文提出一种专为连续体机器人设计的滑窗滤波器（SWF），首次实现连续时间状态估计的在线化，兼具高精度与超实时运行能力，为该领域未来研究提供了新方向。
原文摘要 · Abstract (English)
Stochastic state estimation methods for continuum robots (CRs) often struggle to balance accuracy and computational efficiency. While several recent works have explored sliding-window formulations for CRs, these methods are limited to simplified, discrete-time approximations and do not provide stochastic representations. In contrast, current stochastic filter methods must run at the speed of measurements, limiting their full potential. Recent works in continuous-time estimation techniques for CRs show a principled approach to addressing this runtime constraint, but are currently restricted to offline operation. In this work, we present a sliding-window filter (SWF) for continuous-time state estimation of CRs that improves upon the accuracy of a filter approach while enabling continuous-time methods to operate online, all while running at faster-than-real-time speeds. This represents the first stochastic SWF specifically designed for CRs, providing a promising direction for future research in this area.
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