Trajectory Distribution Control for Model Predictive Path Integral Control using Covariance Steering

TL;DR

This paper introduces a Covariance-Controlled MPPI controller that enhances robustness in autonomous systems by managing trajectory dispersion, effectively handling uncertainties and environmental changes.

Contribution

It combines MPPI with Covariance Steering to improve robustness and adaptability in nonlinear systems under uncertainty, addressing divergence issues in traditional MPPI.

Findings

CC-MPPI prevents divergence in uncertain environments

Adjustable sampling improves efficiency

Numerical tests show superior navigation performance

Abstract

This paper presents a novel control approach for autonomous systems operating under uncertainty. We combine Model Predictive Path Integral (MPPI) control with Covariance Steering (CS) theory to obtain a robust controller for general nonlinear systems. The proposed Covariance-Controlled Model Predictive Path Integral (CC-MPPI) controller addresses the performance degradation observed in some MPPI implementations owing to unexpected disturbances and uncertainties. Namely, in cases where the environment changes too fast or the simulated dynamics during the MPPI rollouts do not capture the noise and uncertainty in the actual dynamics, the baseline MPPI implementation may lead to divergence. The proposed CC-MPPI controller avoids divergence by controlling the dispersion of the rollout trajectories at the end of the prediction horizon. Furthermore, the CC-MPPI has adjustable trajectory sampling distributions that can be changed according to the environment to achieve efficient sampling. Numerical examples using a ground vehicle navigating in challenging environments demonstrate the proposed approach.