Performance Enhanced Risk-Aware MPPI Using Gaussian Process for Robust Mobile Robot Control

This paper presents a performance enhanced Risk-Aware Model Predictive Path Integral (RA-MPPI) control using Gaussian-process (GP). To this end, the real system is run with arbitrary control input and the resulting data is saved. Based on the nominal model of the system and the collected data, uncertainty affecting the state transition is estimated by a Gauss-Newton method offline. Then, the used control input and the estimated uncertainty are used to train the Gaussian process which generates the mean and variance of the uncertainty. Online, the gap between the nominal and real dynamics is compensated by the trained GP. In other words, the nominal model is refined using the estimated uncertainty mean, while the estimated uncertainty variance is directly incorporated into the RA-MPPI framework, eliminating the need to manually tune the disturbance covariance parameter. Validation is conducted through simulation experiments in Gazebo using a F1TENTH car with a bicycle model navigating a track with and without obstacles. Also, real-world performance is further demonstrated on the F1TENTH car platform driving the same track under similar conditions. Across both simulation and real-world scenarios, the proposed method demonstrates improved safety and trajectory tracking performance compared to baseline MPPI and RA-MPPI techniques in terms of lap times and the number of crash-free lap completions.