动态调整约束的模型预测控制，让汽车在不平地形上更稳更快。
A Terrain-Adaptive epsilon-Constraint MPC for Uneven Terrain Kinodynamic Planning
- 根据地形自动调节约束参数，实时优化路径与稳定性平衡
- 导航成功率达94%，方向偏差最大降低24%
- 适合自动驾驶车辆在复杂地形中的实时规划
在不平地形上对类车车辆进行动力学规划，需同时优化路径效率与姿态稳定性。本文提出一种将自适应epsilon约束方法融入模型预测控制（MPC）框架的新方法，其中epsilon边界基于地形描述符动态调整，实现在运行时探索帕累托前沿。为捕捉车辆-地形相互作用，我们构建了一个半参数模型，结合解析车辆动力学与在相同地形描述符上训练的稀疏高斯过程（SGP）。所提出的epsilon-MPC在性能上优于MPPI和GAKD基线方法，实现94%的导航成功率，最大方向偏差降低24%，多目标权衡质量提升23%。
原文摘要 · Abstract (English)
Kinodynamic planning for car-like vehicles on uneven terrain requires simultaneously optimizing competing objectives such as path efficiency and pose stability. This work presents an adaptive epsilon-constraint method integrated into a Model Predictive Control (MPC) framework, where the epsilon bounds are dynamically adjusted based on terrain descriptors to explore the Pareto front in real time. To capture vehicle-terrain dynamics, we develop a semi-parametric model combining analytical vehicle dynamics with a Sparse Gaussian Process (SGP) trained on the same terrain descriptors. The proposed epsilon-MPC is evaluated against MPPI and GAKD baselines, achieving a 94% navigation success rate while reducing maximum orientation deviation by 24% and improving multi-objective trade-off quality by 23%.
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