# Enhancing AUV Autonomy With Model Predictive Path Integral Control

arXiv: https://arxiv.org/abs/2308.05547 · HTML: https://mlsanity.com/paper/2308.05547/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [An Online Learning Approach to Model Predictive Control](https://mlsanity.com/paper/1902.08967/) (2019)
- [Robust Model Predictive Path Integral Control: Analysis and Performance Guarantees](https://mlsanity.com/paper/2102.09027/) (2021)
- [The Value of Planning for Infinite-Horizon Model Predictive Control](https://mlsanity.com/paper/2104.02863/) (2021)
- [From market-ready ROVs to low-cost AUVs](https://mlsanity.com/paper/2108.05792/) (2021)
- [A Robust Model Predictive Control Approach for Autonomous Underwater Vehicles Operating in a Constrained workspace](https://mlsanity.com/paper/1709.04940/) (2017)

## Abstract (arXiv)

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.