A Terrain-Adaptive epsilon-Constraint MPC for Uneven Terrain Kinodynamic Planning

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%.

Robotics (cs.RO)

arXiv:2605.21188 [cs.RO]

(or arXiv:2605.21188v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.21188 Focus to learn more arXiv-issued DOI via DataCite

O. Jerome, G. Kulathunga and T. Nascimento,(2026), IEEE Robotics and Automation Letters

https://doi.org/10.1109/LRA.2026.3728313 Focus to learn more DOI(s) linking to related resources

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