A Terrain-Adaptive epsilon-Constraint MPC for Uneven Terrain Kinodynamic Planning
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866916031737364480 |
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| author | Jerome, Otobong Kalathunga, Geesara Nascimento, Tiago |
| author_facet | Jerome, Otobong Kalathunga, Geesara Nascimento, Tiago |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21188 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Terrain-Adaptive epsilon-Constraint MPC for Uneven Terrain Kinodynamic Planning Jerome, Otobong Kalathunga, Geesara Nascimento, Tiago Robotics 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%. |
| title | A Terrain-Adaptive epsilon-Constraint MPC for Uneven Terrain Kinodynamic Planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.21188 |