Algorithm Design of Navigation Intention Message Transmission for Collision Avoidance of Maritime Autonomous Surface Vehicle

For safe navigation of ship at sea, it is essential to provide navigation intention message to target ship for collision avoidance. Therefore it must be considered for the MASS to transmit navigation intention message to the target ship after making a decision of method for collision avoidance in the encountering situation. This paper presents an algorithm of navigation intention message transmission through the MASS, which is able to evaluate the risk of collision and apply international regulations for collision avoidance. The Fuzzy inference system is used to assess the risk of collision. In case that the risk of collision exceeds the pre-designated threshold, the navigation intention message is transmitted from the MASS to the target ship. Before the collision situation occurs, the target ship is possible to be aware of the navigation intention from the MASS. Proposed the algorithm contributes to providing systematic information exchange between the MASS and the target ship

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Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

Journal of Marine Science and Engineering ◽

10.3390/jmse9010049 ◽

2021 ◽

Vol 9 (1) ◽

pp. 49

Author(s):

Tanja Brcko ◽

Andrej Androjna ◽

Jure Srše ◽

Renata Boć

Keyword(s):

Fuzzy Logic ◽

Collision Avoidance ◽

Decision Model ◽

Fuzzy Inference ◽

Weather Conditions ◽

Target Vessel ◽

Planning Tool ◽

Inference System ◽

International Regulations ◽

Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

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Collision Avoidance of Unmanned Aerial Vehicles Using Fuzzy Inference System-Aided Enhanced Potential Field

10.2514/6.2022-0272 ◽

2022 ◽

Author(s):

Daegyun Choi ◽

Anirudh Chhabra ◽

Donghoon Kim

Keyword(s):

Unmanned Aerial Vehicles ◽

Collision Avoidance ◽

Fuzzy Inference System ◽

Potential Field ◽

Fuzzy Inference ◽

Inference System ◽

Aerial Vehicles

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Interval Type-2 Fuzzy Inference System Based on Closest Point of Approach for Collision Avoidance between Ships

Applied Sciences ◽

10.3390/app10113919 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3919 ◽

Cited By ~ 2

Author(s):

Sung Wook Ohn ◽

Ho Namgung

Keyword(s):

Fuzzy Logic ◽

Collision Avoidance ◽

Membership Function ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Collision Risk ◽

Inference System ◽

Interval Type

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( D C P A ) and Time to Closest Point of Approach ( T C P A ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size “±5%” (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments.

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Local Route Planning for Collision Avoidance of Maritime Autonomous Surface Ships in Compliance with COLREGs Rules

Sustainability ◽

10.3390/su14010198 ◽

2021 ◽

Vol 14 (1) ◽

pp. 198

Author(s):

Ho Namgung

Keyword(s):

Collision Avoidance ◽

Fuzzy Inference ◽

Human Life ◽

Risk Index ◽

Route Planning ◽

Inference System ◽

Surface Ship ◽

International Regulations ◽

Planning Algorithm ◽

Velocity Obstacle

A maritime autonomous surface ship (MASS) ensures safety and effectiveness during navigation using its ability to prevent collisions with a nearby target ship (TS). This avoids the loss of human life and property. Therefore, collision avoidance of MASSs has been actively researched recently. However, previous studies did not consider all factors crucial to collision avoidance in compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) Rules 5, 7, 8, and 13–17. In this study, a local route-planning algorithm that takes collision-avoidance actions in compliance with COLREGs Rules using a fuzzy inference system based on near-collision (FIS-NC), ship domain (SD), and velocity obstacle (VO) is proposed. FIS-NC is used to infer the collision risk index (CRI) and determine the time point for collision avoidance. Following this, I extended the VO using the SD to secure the minimum safe distance between the MASS and the TS when they pass each other. Unlike previous methods, the proposed algorithm can be used to perform safe and efficient navigation in terms of near-collision accidents, inferred CRI, and deviation from the course angle route by taking collision-avoidance actions in compliance with COLREGs Rules 5, 7, 8, and 13–17.

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Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol ◽

10.15575/telka.v3i1.51 ◽

2017 ◽

Vol 3 (1) ◽

pp. 36-48

Author(s):

Erwan Ahmad Ardiansyah ◽

Rina Mardiati ◽

Afaf Fadhil

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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