A Universal Vehicle-Trailer Navigation System with Neural Kinematics and Online Residual Learning

TL;DR

This paper introduces a universal navigation system for vehicle-trailer setups that combines neural kinematic models with online learning and model predictive control, validated through real-world experiments across various trailer types and payloads.

Contribution

It presents a novel hybrid kinematic model with online residual learning and a model predictive control framework for robust, trailer-agnostic navigation without manual tuning.

Findings

Robust navigation across multiple trailer types and payloads.

Effective real-time correction of modeling discrepancies.

No manual calibration needed for different trailers.

Abstract

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.