物理訊息神經網路輔助擴展卡爾曼濾波器之設計與實現

Abstract

本研究旨在設計並實現一套數據導向（Data-Driven）之系統狀態估測系統，應用於阿克曼（Ackermann）無人載具於戶外環境中的定位及定向。為解決阿克曼轉向機構的載具非線性模型不易掌握的問題，本研究進一步引入物理訊息神經網路（Physics-Informed Neural Networks0, PINN）輔助擴展卡爾曼濾波器（Extended Kalman Filter, EKF），透過學習車輛運動方程，融合GPS/GNSS 與IMU 資訊，有效估測車輛在戶外環境中的座標與朝向角，提升估測精度與穩定性。最終，引入動態節點擴張概念及線上即時學習修正類神經網路模型，整體系統融合數據導向方法與物理模型推導之優勢，實現具備自適應性、運動可行性與動態穩健性的戶外阿克曼載具定位與控制架構。

This study aims to design and implement a data-driven state estimation system for localization of Ackermann-type unmanned ground vehicles (UGVs) operating in outdoor environments. To address the challenges posed by the uncertain nonlinear dynamical model inherent to the Ackermann steering mechanism, a Physics-Informed Neural Network (PINN) is incorporated to enhance the performance of the Extended Kalman Filter (EKF). By learning the vehicle’s motion equations and fusing data from GPS/GNSS and inertial measurement units (IMUs), the proposed system effectively estimates the vehicle’s position and heading, thereby improving both the accuracy and stability of localization. To further adapt to real-world environmental dynamics and model uncertainties, this work introduces a dynamic node expansion strategy and an online learning mechanism for real-time model correction. The resulting framework leverages the complementary strengths of data-driven learning and physics-based modeling, enabling adaptive, physically feasible, and robust localization and control of outdoor Ackermann UGVs.