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Distributed model predictive control for vessel train formations of cooperative multi-vessel systems. / Chen, Linying; Hopman, Hans; Negenborn, Rudy R.

In: Transportation Research Part C: Emerging Technologies, Vol. 92, 2018, p. 101-118.

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@article{b8d4ae9bc33948019da349c947a753c6,
title = "Distributed model predictive control for vessel train formations of cooperative multi-vessel systems",
abstract = "Recently, the cooperative control of multiple vessels has been gaining increasing attention because of the potential robustness, reliability and efficiency of multi-agent systems. In this paper, we propose the concept of Cooperative Multi-Vessel Systems (CMVSs) consisting of multiple coordinated autonomous vessels. We in particular focus on the so-called Vessel Train Formation (VTF) problem. The VTF problem considers not only cooperative collision avoidance, but also grouping of vessels. An MPC-based approach is proposed for addressing the VTF problem. A centralized and a distributed formulation based on the Alternating Direction of Multipliers Method (ADMM) are investigated. The distributed formulation adopts a single-layer serial iterative architecture, which gains the benefits of reduced communication requirements and robustness against failures. The impacts of information updating sequences and responsibility parameters are discussed. We furthermore analyze the scalability of the proposed method. Simulation experiments of a CMVS navigating from different terminals in the Port of Rotterdam to inland waterways are carried out to illustrate the effectiveness of our method. The proposed method successfully steers the vessels from different origins to form a vessel train. Due to the effective communication, vessels can timely respond to the velocity changes that others make. After the formation is formed, the distances between vessels become constant. The results show the potential to use CMVSs for inland shipping with enhanced safety.",
keywords = "ADMM, Autonomous vessels, Cooperative multi-vessel systems, Distributed model predictive control, Vessel train formation",
author = "Linying Chen and Hans Hopman and Negenborn, {Rudy R.}",
year = "2018",
doi = "10.1016/j.trc.2018.04.013",
language = "English",
volume = "92",
pages = "101--118",
journal = "Transportation Research. Part C: Emerging Technologies",
issn = "0968-090X",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Distributed model predictive control for vessel train formations of cooperative multi-vessel systems

AU - Chen, Linying

AU - Hopman, Hans

AU - Negenborn, Rudy R.

PY - 2018

Y1 - 2018

N2 - Recently, the cooperative control of multiple vessels has been gaining increasing attention because of the potential robustness, reliability and efficiency of multi-agent systems. In this paper, we propose the concept of Cooperative Multi-Vessel Systems (CMVSs) consisting of multiple coordinated autonomous vessels. We in particular focus on the so-called Vessel Train Formation (VTF) problem. The VTF problem considers not only cooperative collision avoidance, but also grouping of vessels. An MPC-based approach is proposed for addressing the VTF problem. A centralized and a distributed formulation based on the Alternating Direction of Multipliers Method (ADMM) are investigated. The distributed formulation adopts a single-layer serial iterative architecture, which gains the benefits of reduced communication requirements and robustness against failures. The impacts of information updating sequences and responsibility parameters are discussed. We furthermore analyze the scalability of the proposed method. Simulation experiments of a CMVS navigating from different terminals in the Port of Rotterdam to inland waterways are carried out to illustrate the effectiveness of our method. The proposed method successfully steers the vessels from different origins to form a vessel train. Due to the effective communication, vessels can timely respond to the velocity changes that others make. After the formation is formed, the distances between vessels become constant. The results show the potential to use CMVSs for inland shipping with enhanced safety.

AB - Recently, the cooperative control of multiple vessels has been gaining increasing attention because of the potential robustness, reliability and efficiency of multi-agent systems. In this paper, we propose the concept of Cooperative Multi-Vessel Systems (CMVSs) consisting of multiple coordinated autonomous vessels. We in particular focus on the so-called Vessel Train Formation (VTF) problem. The VTF problem considers not only cooperative collision avoidance, but also grouping of vessels. An MPC-based approach is proposed for addressing the VTF problem. A centralized and a distributed formulation based on the Alternating Direction of Multipliers Method (ADMM) are investigated. The distributed formulation adopts a single-layer serial iterative architecture, which gains the benefits of reduced communication requirements and robustness against failures. The impacts of information updating sequences and responsibility parameters are discussed. We furthermore analyze the scalability of the proposed method. Simulation experiments of a CMVS navigating from different terminals in the Port of Rotterdam to inland waterways are carried out to illustrate the effectiveness of our method. The proposed method successfully steers the vessels from different origins to form a vessel train. Due to the effective communication, vessels can timely respond to the velocity changes that others make. After the formation is formed, the distances between vessels become constant. The results show the potential to use CMVSs for inland shipping with enhanced safety.

KW - ADMM

KW - Autonomous vessels

KW - Cooperative multi-vessel systems

KW - Distributed model predictive control

KW - Vessel train formation

UR - http://www.scopus.com/inward/record.url?scp=85046634557&partnerID=8YFLogxK

U2 - 10.1016/j.trc.2018.04.013

DO - 10.1016/j.trc.2018.04.013

M3 - Article

VL - 92

SP - 101

EP - 118

JO - Transportation Research. Part C: Emerging Technologies

T2 - Transportation Research. Part C: Emerging Technologies

JF - Transportation Research. Part C: Emerging Technologies

SN - 0968-090X

ER -

ID: 45170382