Enhancing AUV Autonomy with Model Predictive Path Integral Control

Pierre Nicolay, Yvan Petillot, Mykhaylo Marfeychuk, Sen Wang, Ignacio Carlucho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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 safe operation of the robotic platform 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.

Original languageEnglish
Title of host publicationOCEANS 2023 - MTS/IEEE U.S. Gulf Coast
ISBN (Electronic)9798218142186
Publication statusPublished - 11 Dec 2023
EventOCEANS 2023 - Biloxi, United States
Duration: 25 Sept 202328 Sept 2023


ConferenceOCEANS 2023
Country/TerritoryUnited States


  • AUV
  • Control systems
  • Model Predictive Path Integral

ASJC Scopus subject areas

  • Oceanography
  • Ocean Engineering


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