A Terrain-Adaptive epsilon-Constraint MPC for Uneven Terrain Kinodynamic Planning
For autonomous vehicles operating on uneven terrain, this work provides a real-time Pareto-optimal planning method that balances path efficiency and stability.
The paper tackles kinodynamic planning for car-like vehicles on uneven terrain, achieving a 94% navigation success rate, 24% reduction in orientation deviation, and 23% improvement in multi-objective trade-off quality using an adaptive epsilon-constraint MPC.
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%.