Publications       Rudy Negenborn
Full Professor, Head of Section
Team & Themes

Title:Distributed model predictive control for vessel train formations of cooperative multi-vessel systems
Authors:L. Chen, J.J. Hopman, R.R. Negenborn

Journal:Transportation Research Part C: Emerging Technologies

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. In particular, we focus on the cooperative behavior between vessels in a CMVS. We named it as Vessel Train Formation (VTF). VTF considers not only cooperative collision avoidance, but also grouping of vessels. An MPC-based approach is proposed for the VTF problem. A centralized and a distributed formulation based on ADMM are investigated. The distributed formulation adopts a single-layer serial iterative architecture, which gains the benefits of reduced communication requires 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 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 response 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.

Reference:Distributed model predictive control for vessel train formations of cooperative multi-vessel systems. L. Chen, J.J. Hopman, R.R. Negenborn. Transportation Research Part C: Emerging Technologies, vol. 92, pp. 101-118, July 2018.
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