Predictive path following with arrival time awareness for waterborne AGVs

Zheng, Huarong; Negenborn, Rudy R.; Lodewijks, Gabriël

doi:10.1016/j.trc.2015.11.004

Cited by 59 publications

(28 citation statements)

References 20 publications

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“…Moreover, MPC considers the latest available measurement of the state, which is particularly helpful to deal with uncertainties. Thus, an increasing number of researchers apply MPC for the control of vessels [18], [19].…”

Section: A Trajectory Trackingmentioning

confidence: 99%

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Cooperative Multi-Vessel Systems in Urban Waterway Networks

Chen

Huang

Zheng

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Urban waterways have great potential in cargo transport to relieve the congestion in the overloaded road networks. This paper explores the potential of applying cooperative multi-vessel systems (CMVSs) to improve the safety and efficiency of transport in urban waterway networks. A framework consisting of vessel train formation (VTF) and cooperative waterway intersection scheduling (CWIS) is proposed. Two types of controllers are introduced. Intersection controllers solve the CWIS problems and assign each vessel a desired time of arrival and vessel controllers are responsible for the VTF in waterway segments and the timely arrival at the intersections. An alternating direction method of multipliers (ADMM)-based negotiation framework is proposed for the cooperation among the controllers. The simulation experiments involving the scenarios in which up to 50 vessels sailing in the canal network in Amsterdam are carried out to illustrate the effectiveness of the proposed approach. In the simulation of an isolated intersection, rescheduling is triggered when some vessels cannot arrive on time. Although some ASVs arrive later, the time that is needed for all the ASVs to pass through is the same after rescheduling. Moreover, we compare the cooperative situation with the proposed CMVSs with a baseline situation. In the baseline situation, vessels avoid collisions using the generalized velocity obstacle (GVO) method and cross the intersection with a first in, first out rule. The CMVSs show better path following performance, while the GVO method needs fewer velocity changes. From the perspective of efficiency, the CMVSs help to reduce the total time to pass through the intersection. Index Terms-Cooperative multi-vessel system, cooperative waterway intersection scheduling, waterway network, autonomous surface vessel, cooperative intelligent traffic system. I. INTRODUCTION I N DENSELY populated regions, like cities, road networks are often confronted with congestion and capacity problems. Many cities have considerable waterway resources, such as Amsterdam, Rotterdam, and Utrecht in The Netherlands, and cities in Jiangsu and Zhejiang Province, East China (Fig. 1). Waterway transport could offer an Manuscript

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Section: A Trajectory Trackingmentioning

confidence: 99%

“…2) Linearized Prediction Model: MPC has attracted increasing interests [18], [34] in the field of waterborne transport. Besides, distributed MPC has been used for cooperative control of networked vehicles [35].…”

Section: A Dynamic Model Of An Individual Vessel 1) 3 Dof Dynamic Momentioning

confidence: 99%

Cooperative Multi-Vessel Systems in Urban Waterway Networks

Chen

Huang

Zheng

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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“…1) Successively linearized model: MPC has been popular in practical applications since its very early days [17]. For waterborne transport, MPC has been applied to control the motion of the vessels, such as, path following [7], heading control [18], and collision avoidance [19]. Besides, distributed MPC has been used for cooperative control of networked vehicles [20].…”

Section: Dynamic Model a 3 Dof Dynamic Model Of An Asvmentioning

confidence: 99%

“…If this nonlinear model is directly used to design the MPC controller, the MPC online predictions and optimizations would be too time-consuming for real-time control. Therefore, the successively linearized model presented in [7] is adopted in this paper. The dynamic model (3) is discretized with a sample time T s :…”

Section: Dynamic Model a 3 Dof Dynamic Model Of An Asvmentioning

confidence: 99%

“…Adaptive schemes are introduced in [4], [5], [6] where uncertainty within the model and environmental disturbances are handled using neural networks, fuzzy logic and model reconfiguration. The adoption of model-based approaches such as Model Predictive Control (MPC) algorithms are investigated in [7], [8] where the results suggest a remarkable decrease in the trajectory following error.…”

Section: Introductionmentioning

confidence: 99%

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Eco-VTF: Fuel-Efficient Vessel Train Formations for All-Electric Autonomous Ships

Chen

Haseltalab

Garofano

et al. 2019

2019 18th European Control Conference (ECC)

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In this paper, a distributed control approach is proposed to enable fuel-efficient Vessel Train Formations (VTF) in inland waterways and port areas for addressing the efficiency and environmental issues of transport over water. For path tracking, collision avoidance, and consensus over the VTF speed a distributed Model Predictive Control (MPC) algorithm is adopted which uses the Alternating Direction Method of Multipliers (ADMM) to guarantee path following and consensus between vessels. The all-electric Direct Current (DC) configuration is considered for the Power and Propulsion Systems (PPS) of the autonomous vessels under study. Considering their PPS specification, the vessels negotiate with each other to agree on the most efficient speed for all the vessels in the VTF. Simulation results suggest that a significant amount of fuel saving can be obtained by using the proposed approach.

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