Wheel-loader V-Cycle Automation with Deep Koopman MPC

2026-09-22

AI Summary

本文针对轮式装载机自动化操作中的非线性动力学和复杂车辆-地形交互问题，提出结合几何规划与基于数据驱动的预测控制的分层框架，使用深度Koopman模型实现高效轨迹跟踪。

Abstract

The repeated forward-reverse maneuvers performed by wheel loaders during earthmoving operations make them well suited for automation. However, the nonlinear dynamics of articulated vehicles and complex vehicle-terrain interactions limit the effectiveness of conventional model-based approaches. This paper presents a hierarchical framework that combines long-horizon geometric planning with data-driven predictive control for autonomous wheel-loader operation. A reduced-order articulated kinematic model is used to generate the maneuver geometry, where the forward and reverse trajectories are jointly optimized through a shared intermediate state. To capture the vehicle dynamics, two data-driven deep bilinear Koopman models are learned for the forward and reverse motions using data generated from high-fidelity simulations in Algoryx Dynamics. The learned Koopman representations are subsequently incorporated into a computationally efficient model predictive control (MPC) formulation for trajectory tracking. The resulting controller operates in real time within a 50-ms execution loop. High-fidelity simulation results demonstrate that the proposed end-to-end framework enables accurate and computationally efficient execution of wheel-loader V-cycle maneuvers, providing a promising approach toward autonomous operation of articulated heavy-duty machinery.

Problem

• wheel-loader
• automation
• nonlinear dynamics
• vehicle-terrain interaction
• V-cycle

Innovation

• Deep Koopman Models
• Model Predictive Control (MPC)
• Hierarchical Framework
• Data-Driven Predictive Control