发表机构
University of California, Davis（加州大学戴维斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合几何规划与数据驱动深度Koopman MPC的分层框架，实现轮式装载机V型循环作业的实时自主控制，并在高保真仿真中验证了准确性与高效性。
AI 中文摘要
轮式装载机在土方作业中反复进行的前进-后退 maneuvers 使其非常适合自动化。然而，铰接式车辆的非线性动力学以及复杂的车辆-地形相互作用限制了传统基于模型方法的有效性。本文提出了一种分层框架，将长时域几何规划与数据驱动的预测控制相结合，用于自主轮式装载机操作。采用一个降阶的铰接运动学模型来生成 maneuver 几何形状，其中前进和后退轨迹通过共享的中间状态进行联合优化。为了捕捉车辆动力学，利用在 Algoryx Dynamics 中高保真仿真生成的数据，分别学习了前进和后退运动的两个数据驱动深度双线性Koopman模型。学习得到的Koopman表示随后被纳入一个计算高效的模型预测控制（MPC）公式中，用于轨迹跟踪。所得到的控制器在50毫秒执行循环内实时运行。高保真仿真结果表明，所提出的端到端框架能够实现轮式装载机V型循环 maneuvers 的准确且计算高效的执行，为铰接式重型机械的自主操作提供了一种有前景的方法。
英文摘要
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.
Comments8 pages, 4 figure
