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

TL;DR

This paper introduces a terrain-adaptive epsilon-constraint MPC that dynamically balances path efficiency and stability for vehicle navigation on uneven terrain, utilizing terrain-aware models and real-time Pareto front exploration.

Contribution

It develops a novel adaptive epsilon-constraint MPC framework with terrain-aware dynamics modeling for improved multi-objective kinodynamic planning.

Findings

Achieved 94% navigation success rate.

Reduced maximum orientation deviation by 24%.

Improved trade-off quality by 23%.

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%.