# 用于不平坦地形运动动力学规划的地形自适应 ε-约束 MPC

> 原文链接：https://robotics.hellobike.com/papers/2026-08-27-2605-21188
> 频道：papers | 更新日期：2026-08-27 | 标签：arxiv, cs.RO
> 核心摘要：该工作针对不平坦地形类车车辆规划，提出地形自适应 ε-约束 MPC，并在与 MPPI、GAKD 对比中验证。

# 用于不平坦地形运动动力学规划的地形自适应 ε-约束 MPC

::: v-pre
> 原文链接：[A Terrain-Adaptive $\epsilon$-Constraint MPC for Uneven Terrain Kinodynamic Planning](https://arxiv.org/abs/2605.21188)（arXiv 2605.21188 · 发布日期：2026-08-27）

**A Terrain-Adaptive $\epsilon$-Constraint MPC for Uneven Terrain Kinodynamic Planning**

该工作针对不平坦地形类车车辆规划，提出地形自适应 ε-约束 MPC，并在与 MPPI、GAKD 对比中验证。

## 核心要点

- 不平坦地形下类车车辆运动动力学规划需同时权衡路径效率与姿态稳定性等竞争目标。
- 方法将自适应 ε-约束融入 MPC，并依据地形描述符动态调整 ε 边界以实时探索 Pareto 前沿。
- 车辆-地形动力学采用半参数模型，结合解析车辆动力学与基于相同地形描述符训练的 SGP。
- 相较 MPPI 和 GAKD，该方法达到 94% 导航成功率，最大姿态偏差降低 24%，多目标权衡质量提升 23%。

**验证范围与局限**：验证范围为不平坦地形上的类车车辆运动动力学规划，摘要未说明仿真或实车设置。

## 英文摘要 (Abstract)

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%.

作者：Otobong Jerome, Geesara Kulathunga, Tiago do Nascimento
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## 原始参考来源 (Sources)

- [arXiv 2605.21188：A Terrain-Adaptive $\epsilon$-Constraint MPC for Uneven Terrain Kinodynamic Planning](https://arxiv.org/abs/2605.21188)