Decision-Making Model for Multi-Ship Collision Avoidance Based on Adaptive Genetic Algorithm

2007 ◽  
Author(s):  
Jiang-ling Hao ◽  
Li-ning Zhao ◽  
Jing-feng Hu ◽  
Xiao-bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Collision Avoidance ◽  
Adaptive Genetic Algorithm ◽  
Ship Collision ◽  
Decision Making Model

scholarly journals Ship-Collision Avoidance Decision-Making Learning of Unmanned Surface Vehicles with Automatic Identification System Data Based on Encoder—Decoder Automatic-Response Neural Networks

2020 ◽  
Vol 8 (10) ◽  
pp. 754
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Time Series ◽  
Collision Avoidance ◽  
Time Series Data ◽  
Series Data ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Response ◽  
Ship Collision

Intelligent unmanned surface vehicle (USV) collision avoidance is a complex inference problem based on current navigation status. This requires simultaneous processing of the input sequences and generation of the response sequences. The automatic identification system (AIS) encounter data mainly include the time-series data of two AIS sets, which exhibit a one-to-one mapping relation. Herein, an encoder–decoder automatic-response neural network is designed and implemented based on the sequence-to-sequence (Seq2Seq) structure to simultaneously process the two AIS encounter trajectory sequences. Furthermore, this model is combined with the bidirectional long short-term memory recurrent neural networks (Bi-LSTM RNN) to obtain a network framework for processing the time-series data to obtain ship-collision avoidance decisions based on big data. The encoder–decoder neural networks were trained based on the AIS data obtained in 2018 from Zhoushan Port to achieve ship collision avoidance decision-making learning. The results indicated that the encoder–decoder neural networks can be used to effectively formulate the sequence of the collision avoidance decision of the USV. Thus, this study significantly contributes to the increased efficiency and safety of maritime transportation. The proposed method can potentially be applied to the USV technology and intelligent collision-avoidance systems.

The Uncertainty and Uncoordination of Mariners' Behaviour in Collision Avoidance at Sea

1995 ◽  
Vol 48 (3) ◽  
pp. 425-435 ◽  
Author(s):  
J. Zhao ◽  
W. G. Price ◽  
P. A. Wilson ◽  
M. Tan
Keyword(s):  
Decision Making ◽  
Simulation Model ◽  
Collision Avoidance ◽  
Simulation Models ◽  
Fuzzy Programming ◽  
Assessment Model ◽  
The Other ◽  
Entropy Theory ◽  
Close Proximity ◽  
Decision Making Model

It is well known that many collisions occur because one ship turns right whilst the other turns left when in close proximity to one another. Little is known as to why this occurs and, although some simulation models have been established using entropy theory, the problem remains unsolved.In this paper, an assessment model for uncertainty is reviewed briefly. The concepts of uncertainty and uncoordination of mariners' behaviour in collision avoidance are discussed. A simulation model in conjunction with a DCPA (distance to the closest point of approach) decision-making model using fuzzy programming is introduced to discuss coordination.

scholarly journals Evaluation of efficiency and reliability of airport processes using simulation tools

2021 ◽  
Vol 23 (4) ◽  
pp. 659-669
Author(s):  
Paweł Gołda ◽  
Tomasz Zawisza ◽  
Mariusz Izdebski
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Simulation Tool ◽  
Simulation Tools ◽  
Aircraft Landing ◽  
Mutation Operators ◽  
Decision Making Model ◽  
Crossover And Mutation ◽  
Efficiency And Reliability

The purpose of this paper is to evaluate the efficiency of airport processes using simulation tools. A critical review of selected scientific studies relating to the performance of airport processes with respect to reliability, particularly within the apron, has been undertaken. The developed decision-making model evaluates the efficiency of airport processes in terms of minimizing penalties associated with aircraft landing before or after the scheduled landing time. The model takes into account, among other things, aircraft take-offs and landings and separation times between successive aircraft. In order to be able to verify the correctness of the decision-making model, a simulation tool was developed to support decision making in the implementation of airport operations based on a genetic algorithm. A novel development of the structure of a genetic algorithm as well as crossover and mutation operators adapted to the determination of aircraft movement routes on the apron is presented. The developed simulation tool was verified on real input data.

Autonomous decision-making scheme for multi-ship collision avoidance with iterative observation and inference

2020 ◽  
Vol 197 ◽  
pp. 106873 ◽  
Author(s):  
Tengfei Wang ◽  
Qing Wu ◽  
Jinfen Zhang ◽  
Bing Wu ◽  
Yang Wang
Keyword(s):  
Decision Making ◽  
Collision Avoidance ◽  
Autonomous Decision ◽  
Ship Collision

scholarly journals Multi-Ship Collision Avoidance Decision-Making Based on Collision Risk Index

2020 ◽  
Vol 8 (9) ◽  
pp. 640
Author(s):  
Yingjun Hu ◽  
Anmin Zhang ◽  
Wuliu Tian ◽  
Jinfen Zhang ◽  
Zebei Hou
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Risk Analysis ◽  
Real Time ◽  
Collision Avoidance ◽  
Evaluation Method ◽  
Risk Index ◽  
Collision Risk ◽  
Ship Collision ◽  
Risk Thresholds

Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

Sensory Network for Mobile Robotic Systems with Structured Intelligence

1998 ◽  
Vol 10 (4) ◽  
pp. 338-349 ◽  
Author(s):  
Naoyuki Kubota ◽  
◽  
Toshio Fukuda ◽  
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Mobile Robot ◽  
Collision Avoidance ◽  
Environmental Conditions ◽  
Fuzzy Controller ◽  
Robotic Systems ◽  
Delta Rule ◽  
Simulation Results ◽  
Collision Avoidance Problems

This paper deals with a sensory network for mobile robotic systems with structured intelligence. A mobile robot requires close linkage of sensing, decision making, and action. To realize this, we propose structured intelligence for robotic systems. In this paper, we focus on the sensing ability for a mobile robot with a fuzzy controller tuned by the delta rule and whose architecture is optimized by a genetic algorithm. We apply the sensory network for controlling attention ranges for external sensors and for adjusting fuzzy controller output from the metalevel. As a simulation example, we apply the proposed method to mobile robot collision avoidance problems. Simulation results show that sensory networks control the attention range for perception and adjust fuzzy controller output based on given environmental conditions. We show the experimental results of mobile robot collision avoidance in work space including several obstacles.

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