What question did this study set out to answer?

To develop a systematic approach for optimizing localization-aware path planning in autonomous underwater vehicle teams.

December 21, 2025Open Access

Integrated Localization and Path Planning for an Ocean Exploring Team of Autonomous Underwater Vehicles with Consensus Graph Model Predictive Control

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To develop a systematic approach for optimizing localization-aware path planning in autonomous underwater vehicle teams.
Proposes a model predictive control (MPC) optimization framework for path planning.
Integrates localization as consensus graph optimization among AUV nodes.
Analyzes the time complexity of the optimization as a nonconvex NP-hard problem.
Achieves energy-efficient and collision-free path planning for USV-AUV teams.
Utilizes numerical simulations to evaluate the proposed localization and path planning method.

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Implication

This approach demonstrates efficient path planning and accurate localization in autonomous underwater vehicles, indicating improved deep ocean exploration.

Cite This Study

Eskandari et al. (2025) studied this question.

synapsesocial.com/papers/69473b64db9c958d0dfca93c https://doi.org/10.48550/arxiv.2505.07484

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Implication

This approach demonstrates efficient path planning and accurate localization in autonomous underwater vehicles, indicating improved deep ocean exploration.

Integrated Localization and Path Planning for an Ocean Exploring Team of Autonomous Underwater Vehicles with Consensus Graph Model Predictive Control

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Implication

Cite This Study

Integrated Localization and Path Planning for an Ocean Exploring Team of Autonomous Underwater Vehicles with Consensus Graph Model Predictive Control

Key Points

Discussion

Implication

Cite This Study

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