Fast ADMM for Distributed Model Predictive Control of Cooperative Waterborne AGVs

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

doi:10.1109/tcst.2016.2599485

Cited by 80 publications

(31 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, when a controller performs computation at iteration s, it uses the information that the other controllers provide at the iteration s −1. An example of a parallel negotiation framework can be found in [33]. The scheme enjoys the advantage of parallel computation.…”

Section: Negotiation Frameworkmentioning

confidence: 99%

Cooperative Multi-Vessel Systems in Urban Waterway Networks

Chen

Huang

Zheng

et al. 2020

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Negotiation Frameworkmentioning

confidence: 99%

Cooperative Multi-Vessel Systems in Urban Waterway Networks

Chen

Huang

Zheng

et al. 2020

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In principle, RHP utilizes a dynamical model to obtain planned routes by minimizing an objective over a finite receding horizon 23 . A similar methodology, which is referred to as receding horizon control, has been successfully used in the industrial process and transportation 24,25 . In RHP, the dynamical model is used to predict the future routes of the vehicle based on the current state and the proposed future routes.…”

Section: Receding Horizon Planningmentioning

confidence: 99%

Receding horizon path planning of automated guided vehicles using a time‐space network model

Xin

Wei

Wang

et al. 2020

Optim Control Appl Methods

View full text Add to dashboard Cite

Summary Time‐space network (TSN) models have been widely used for collision‐free path planning of automated guided vehicles. However, existing TSN models are planned globally. The global method suffers from computational complexity and uncertainties cannot be dealt with in the dynamic environment. To address these limitations, this article proposes a new methodology to decompose the global planning problem into smaller local planning problems, which are planned in a receding horizon way. For the local problem, new decision variables and constraints are incorporated into the TSN framework. Extensive simulation experiments are carried out to show the potential of the proposed methodology. Simulation results show that the proposed method obtains competitive performances and computational times are considerably reduced, compared with the global method.

show abstract

“…Here, we introduce a procedure to linearize the non-convex constraints due to the collision avoidance (5). Based on the properties of receding horizon optimization, we generate a seed trajectory r 0 by moving the optimal control input calculated in previous cycle one step forward and repeating the last prediction step [24].…”

Section: E Convexification Of the Problemmentioning

confidence: 99%

Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM

Wang

Zheng

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

This paper proposes a parallel optimization algorithm for cooperative automation of large-scale connected vehicles. The task of cooperative automation is formulated as a centralized optimization problem taking the whole decision space of all vehicles into account. Considering the uncertainty of the environment, the problem is solved in a receding horizon fashion. Then, we employ the alternating direction method of multipliers (ADMM) to solve the centralized optimization in a parallel way, which scales more favorably to large-scale instances. Also, Taylor series is used to linearize nonconvex constraints caused by coupling collision avoidance constraints among interactive vehicles. Simulations with two typical traffic scenes for multiple vehicles demonstrate the effectiveness and efficiency of our method.

show abstract

Fast ADMM for Distributed Model Predictive Control of Cooperative Waterborne AGVs

Cited by 80 publications

References 21 publications

Cooperative Multi-Vessel Systems in Urban Waterway Networks

Cooperative Multi-Vessel Systems in Urban Waterway Networks

Receding horizon path planning of automated guided vehicles using a time‐space network model

Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM

Contact Info

Product

Resources

About