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This research aims to enhance the stability and smoothness of trajectory planning for autonomous ground vehicles in complex environments.

February 2, 2026DronesOpen Access

Stable and Smooth Trajectory Optimization for Autonomous Ground Vehicles via Halton-Sampling-Based MPPI

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Halton-MPPI improves trajectory smoothness and control stability in autonomous ground vehicles, suggesting enhanced navigation efficiency.

This research aims to enhance the stability and smoothness of trajectory planning for autonomous ground vehicles in complex environments.
Developed Halton-MPPI using low-discrepancy Halton sequences for sampling control disturbances.
Implemented the Ornstein–Uhlenbeck process to create temporal correlation in control perturbations.
Conducted large-scale simulations on the BARN dataset to evaluate performance.
Performed field tests in outdoor environments to validate the proposed method.
Halton-MPPI significantly improved trajectory smoothness measured by MSCX.
Control smoothness measured by MSCU also showed substantial enhancement.
Achieved high success rates in both simulations and field tests, indicating reliability.

Xu et al. (2026) studied this question.

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Halton-MPPI improves trajectory smoothness and control stability in autonomous ground vehicles, suggesting enhanced navigation efficiency.