Coordination of Multiple Vessels Via Distributed Nonlinear Model Predictive Control

Ferranti, Laura; Negenborn, Rudy R.; Keviczky, Tamás; Alonso–Mora, Javier

doi:10.23919/ecc.2018.8550178

Cited by 28 publications

(24 citation statements)

References 18 publications

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“…Other works employ the consensus ADMM variant without a central node [111] with other notable applications in target tracking [3], signal estimation [16], task assignment [112], motion planning [5], online learning [113], and parameter estimation in global navigation satellite systems [114]. Further applications of C-ADMM arise in trajectory tracking problems involving teams of robots using non-linear model predictive control [115] and in cooperative localization [116]. Applications of SOVA include collaborative manipulation [117].…”

Section: Applications Of C-admmmentioning

confidence: 99%

A Survey of Distributed Optimization Methods for Multi-Robot Systems

Halsted¹,

Shorinwa²,

Yu³

et al. 2021

Preprint

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Distributed optimization consists of multiple computation nodes working together to minimize a common objective function through local computation iterations and networkconstrained communication steps. In the context of robotics, distributed optimization algorithms can enable multi-robot systems to accomplish tasks in the absence of centralized coordination. We present a general framework for applying distributed optimization as a module in a robotics pipeline. We survey several classes of distributed optimization algorithms and assess their practical suitability for multi-robot applications. We further compare the performance of different classes of algorithms in simulations for three prototypical multi-robot problem scenarios. The Consensus Alternating Direction Method of Multipliers (C-ADMM) emerges as a particularly attractive and versatile distributed optimization method for multi-robot systems.

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Section: Applications Of C-admmmentioning

confidence: 99%

A Survey of Distributed Optimization Methods for Multi-Robot Systems

Halsted¹,

Shorinwa²,

Yu³

et al. 2021

Preprint

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show abstract

“…Any violation of the ship domain is considered to be a threat to navigational safety and a potential source of collision accidents. The ship domain can be defined using three shapes, namely, circles [14], ellipses [15] and polygons [16], and is primarily determined by expert experience. In most cases, the shape and size remain unchanged during a voyage regardless of the actual traffic and encounter situation.…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

Jia

Yang

et al. 2020

IEEE Access

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Collision risk early warning is critical to sailing safety in vessel encounter situations because it provides ship officers with sufficient time to react to emergencies and take evasive actions in advance. In this study, we take spatiotemporal motion behaviors of encountering vessels into account since vessel motion behaviors have great influences on the occurrence of a dangerous situation. For this purpose, a datadriven approach is proposed to associate the motion behaviors with the future risk and early prediction of risk is achieved through classifying the behaviors into corresponding risk level. Specifically, we first derive a sequence of relative motion features between encountering vessels to characterize the spatial interactions that vary over time. Then a novel deep learning architecture, which combines bidirectional long short-term memory (BiLSTM) and attention mechanism, is developed to capture the spatial-temporal dependences of behaviors as well as their impacts on future risk. In particular, the BiLSTM is able to discover correlations among behaviors and the attention mechanism can emphasize the key information relevant to the risk prediction task. Exploiting the advantages of these two mechanisms makes the risk prediction more reasonable and reliable. Extensive experiments using ship trace data from the Yangtze River Estuary demonstrate that the proposed Attention-BiLSTM approach outperforms conventional LSTM in terms of accuracy and stability. Moreover, the real-time capability of the approach gives it a significant potential for use in predicting collisions at the early stages.

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“…[11] uses invariant-set theory and mix-integer linear programming (MILP). [12] and [13] rely on the alternating direction method of multipliers (ADMM). However, they are not computationally efficient with a large number of vehicles for real time applications, since the optimization problem is usually nonlinear and non-convex.…”

Section: Introductionmentioning

confidence: 99%

Distributed Motion Coordination Using Convex Feasible Set Based Model Predictive Control

Zhou¹,

Liu²

2021

Preprint

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The implementation of optimization-based motion coordination approaches in real world multi-agent systems remains challenging due to their high computational complexity and potential deadlocks. This paper presents a distributed model predictive control (MPC) approach based on convex feasible set (CFS) algorithm for multi-vehicle motion coordination in autonomous driving. By using CFS to convexify the collision avoidance constraints, collision-free trajectories can be computed in real time. We analyze the potential deadlocks and show that a deadlock can be resolved by changing vehicles' desired speeds. The MPC structure ensures that our algorithm is robust to low-level tracking errors. The proposed distributed method has been tested in multiple challenging multi-vehicle environments, including unstructured road, intersection, crossing, platoon formation, merging, and overtaking scenarios. The numerical results and comparison with other approaches (including a centralized MPC and reciprocal velocity obstacles) show that the proposed method is computationally efficient and robust, and avoids deadlocks.

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Coordination of Multiple Vessels Via Distributed Nonlinear Model Predictive Control

Cited by 28 publications

References 18 publications

A Survey of Distributed Optimization Methods for Multi-Robot Systems

A Survey of Distributed Optimization Methods for Multi-Robot Systems

A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM

Distributed Motion Coordination Using Convex Feasible Set Based Model Predictive Control

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