实时构建动态障碍物运动预测模型，提升机器人规划安全性
Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning
- 用改进的滑动窗口汉克尔DMD实现在线去噪与预测
- 在模拟和起重机实验中实现稳定方差感知的短期预测
- 适合需要风险感知的实时机器人控制场景
自主系统常需从部分且噪声干扰的数据中预测周围智能体的运动。本文回答了关键问题：能否实时学习另一个智能体运动的非线性预测模型？提出一种在线框架，通过改进的滑动窗口汉克尔动态模态分解（Hankel-DMD）对动态进行去噪与预测。将不完整噪声观测嵌入汉克尔矩阵，结合佩奇矩阵实现奇异值硬阈值化（SVHT），估计有效秩；通过卡德佐投影强制结构化低秩一致性，获得去噪轨迹及局部噪声方差估计。由此构建时变汉克尔-DMD升维线性预测器，实现多步预测。残差分析提供方差追踪信号，可支持下游估计器与风险感知规划。在高斯与重尾噪声下的仿真及动态起重机实验中验证，方法实现了稳定的方差感知去噪与短时预测，适用于实时控制框架集成。
原文摘要 · Abstract (English)
Autonomous systems often must predict the motions of nearby agents from partial and noisy data. This paper asks and answers the question: "can we learn, in real-time, a nonlinear predictive model of another agent's motions?" Our online framework denoises and forecasts such dynamics using a modified sliding-window Hankel Dynamic Mode Decomposition (Hankel-DMD). Partial noisy measurements are embedded into a Hankel matrix, while an associated Page matrix enables singular-value hard thresholding (SVHT) to estimate the effective rank. A Cadzow projection enforces structured low-rank consistency, yielding a denoised trajectory and local noise variance estimates. From this representation, a time-varying Hankel-DMD lifted linear predictor is constructed for multi-step forecasts. The residual analysis provides variance-tracking signals that can support downstream estimators and risk-aware planning. We validate the approach in simulation under Gaussian and heavy-tailed noise, and experimentally on a dynamic crane testbed. Results show that the method achieves stable variance-aware denoising and short-horizon prediction suitable for integration into real-time control frameworks.
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