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MPC-based Motion Planning and Control Enables Smarter and Safer Autonomous Marine Vehicles: Perspectives and a Tutorial Survey

  • Henglai Wei
  • , Yang Shi*
  • *Corresponding author for this work
  • University of Victoria BC

Research output: Contribution to journalArticlepeer-review

Abstract

Autonomous marine vehicles (AMVs) have received considerable attention in the past few decades, mainly because they play essential roles in broad marine applications such as environmental monitoring and resource exploration. Recent advances in the field of communication technologies, perception capability, computational power and advanced optimization algorithms have stimulated new interest in the development of AMVs. In order to deploy the constrained AMVs in the complex dynamic maritime environment, it is crucial to enhance the guidance and control capabilities through effective and practical planning, and control algorithms. Model predictive control (MPC) has been exceptionally successful in different fields due to its ability to sys-tematically handle constraints while optimizing control performance. This paper aims to provide a review of recent progress in the context of motion planning and control for AMVs from the perceptive of MPC. Finally, future research trends and directions in this substantial research area of AMVs are highlighted.

Original languageEnglish
Pages (from-to)8-24
Number of pages17
JournalIEEE/CAA Journal of Automatica Sinica
Volume10
Issue number1
DOIs
StatePublished - 1 Jan 2023
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 14 - Life Below Water
    SDG 14 Life Below Water

Keywords

  • Autonomous marine vehicles (AMVs)
  • model predictive control (MPC)
  • motion control
  • motion planning

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