基于几何感知的动态建模，让自动驾驶车在复杂地形上实时精准跟踪路径。
Nonplanar Model Predictive Control for Autonomous Vehicles with Recursive Sparse Gaussian Process Dynamics
- 用递归稀疏高斯过程学习车辆在非平面地形上的残差动力学
- 在3D复杂地表上实现高精度路径跟踪，误差显著低于传统方法
- 适合需要应对多变地形的自动驾驶系统研发人员
本文提出一种面向非平面地形下自动驾驶车辆的非平面模型预测控制（MPC）框架。为近似复杂环境中的车辆动力学，我们开发了一种几何感知建模方法，通过学习残差高斯过程（GP）进行建模。利用递归稀疏高斯过程，该框架实现了对变化地形几何形状的实时适应。通过在自研Isaac Sim环境中使用模型预测路径积分（MPPI）控制器进行参考轨迹跟踪任务验证，结果表明该方法在挑战性三维表面上仍能保持高跟踪精度。
原文摘要 · Abstract (English)
This paper proposes a nonplanar model predictive control (MPC) framework for autonomous vehicles operating on nonplanar terrain. To approximate complex vehicle dynamics in such environments, we develop a geometry-aware modeling approach that learns a residual Gaussian Process (GP). By utilizing a recursive sparse GP, the framework enables real-time adaptation to varying terrain geometry. The effectiveness of the learned model is demonstrated in a reference-tracking task using a Model Predictive Path Integral (MPPI) controller. Validation within a custom Isaac Sim environment confirms the framework's capability to maintain high tracking accuracy on challenging 3D surfaces.
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