Achieving safe and stable navigation for autonomous ground vehicles (AGVs) in complex environments remains a key challenge in intelligent robotics. Conventional Model Predictive Path Integral (MPPI) control relies on pseudo-random Gaussian sampling, which often results in non-uniform sample distributions and jitter-prone control sequences, thereby limiting both convergence efficiency and control stability. This paper proposes a trajectory optimization method: Halton-MPPI, which improves MPPI by employing low-discrepancy sampling and modeling temporally correlated perturbations. Specifically, it utilizes the Halton sequence as the sampling basis for control disturbances to enhance spatial coverage, while the Ornstein–Uhlenbeck (OU) process is introduced to impose temporal correlation on control perturbations. This time-consistent noise propagation allows perturbation effects to accumulate over time, thereby expanding trajectory coverage. Large-scale simulations on the BARN dataset demonstrate that the method significantly enhances both trajectory smoothness (MSCX) and control smoothness (MSCU) while maintaining high success rates. Moreover, field tests in outdoor environments validate the effectiveness and robustness of Halton-MPPI, underscoring its practical value for autonomous navigation in complex environments.
Xu et al. (Thu,) studied this question.