用神经动力学与在线学习，让车辆拖车系统自动适应不同负载和轮子类型。
A Universal Vehicle-Trailer Navigation System with Neural Kinematics and Online Residual Learning
- 融合经典运动约束与神经网络建模，统一处理多种拖车类型。
- 在线残差学习实时修正模型误差，提升路径规划精度。
- 无需调参或校准，适合机场、商超等复杂场景的通用导航。
自动驾驶车辆拖车系统在机场、超市、演唱会场馆等环境中至关重要，需应对不同类型的拖车及负载条件。然而，准确建模此类系统仍具挑战性，尤其对于带万向轮的拖车。本文提出一种新型通用车辆-拖车导航系统，结合混合名义运动学模型——融合车辆的经典非完整约束与基于神经网络的拖车运动学——并引入轻量级在线残差学习模块，以实时修正建模偏差与外部扰动。此外，我们设计了一种带有加权模型组合策略的模型预测控制框架，提升了长时程预测准确性，确保更安全的运动规划。通过大量真实世界实验，验证了该方法在多种拖车型号及不同负载条件下的鲁棒性能，且无需人工调参或拖车特异性校准。
原文摘要 · Abstract (English)
Autonomous navigation of vehicle-trailer systems is crucial in environments like airports, supermarkets, and concert venues, where various types of trailers are needed to navigate with different payloads and conditions. However, accurately modeling such systems remains challenging, especially for trailers with castor wheels. In this work, we propose a novel universal vehicle-trailer navigation system that integrates a hybrid nominal kinematic model--combining classical nonholonomic constraints for vehicles and neural network-based trailer kinematics--with a lightweight online residual learning module to correct real-time modeling discrepancies and disturbances. Additionally, we develop a model predictive control framework with a weighted model combination strategy that improves long-horizon prediction accuracy and ensures safer motion planning. Our approach is validated through extensive real-world experiments involving multiple trailer types and varying payload conditions, demonstrating robust performance without manual tuning or trailer-specific calibration.
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