Enhancing AUV Autonomy With Model Predictive Path Integral Control

TL;DR

This paper explores the application of Model Predictive Path Integral Control (MPPI) to enhance the autonomy and safety of autonomous underwater vehicles (AUVs) by enabling real-time control, constraint handling, and improved performance over traditional methods.

Contribution

The study demonstrates the feasibility and advantages of using MPPI for AUV control, including real-time operation, hyperparameter analysis, and constraint integration, outperforming classical PID controllers.

Findings

MPPI effectively controls AUVs in real-time.

Hyperparameter tuning impacts MPPI performance.

MPPI handles environmental constraints via cost function.

Abstract

Autonomous underwater vehicles (AUVs) play a crucial role in surveying marine environments, carrying out underwater inspection tasks, and ocean exploration. However, in order to ensure that the AUV is able to carry out its mission successfully, a control system capable of adapting to changing environmental conditions is required. Furthermore, to ensure the robotic platform's safe operation, the onboard controller should be able to operate under certain constraints. In this work, we investigate the feasibility of Model Predictive Path Integral Control (MPPI) for the control of an AUV. We utilise a non-linear model of the AUV to propagate the samples of the MPPI, which allow us to compute the control action in real time. We provide a detailed evaluation of the effect of the main hyperparameters on the performance of the MPPI controller. Furthermore, we compared the performance of the proposed method with a classical PID and Cascade PID approach, demonstrating the superiority of our proposed controller. Finally, we present results where environmental constraints are added and show how MPPI can handle them by simply incorporating those constraints in the cost function.