scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

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.

scholarly journals Hardware Simulation of Rear-End Collision Avoidance System Based on Fuzzy Logic

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Noor Cholis Basjaruddin ◽  
Didin Saefudin ◽  
Anggun Pancawati
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Inference System ◽  
Fuzzy Logic Algorithm ◽  
Pile Up ◽  
Assistance Systems ◽  
Collision Avoidance System

Rear-end collisions are the most common type of traffc accident. On the highway, a real-end collision may involve more than two vehicles and cause a pile-up or chain-reaction crash. Referring to data released by the Australian Capital Territory (ACT), rear-end  collisions which occurred throughout 2010 constituted as much as 43.65% of all collisions. In most cases, these rear-end collisions are caused by inattentive drivers, adverse road conditions and poor following distance. The Rear-end Collision Avoidance System (RCAS) is a device to help drivers to avoid rear-end collisions. The RCAS is a subsystem of Advanced Driver Assistance Systems (ADASs) and became an important part of the driverless car. This paper discusses a hardware simulation of a RCAS based on fuzzy logic using a remote control car. The Mamdani method was used as a fuzzy inference system and realized by using the Arduiono Uno microcontroller system. Simulation results showed that the fuzzy logic algorithm of RCAS can work as designed.

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

2020 ◽  
Vol 10 (11) ◽  
pp. 3919 ◽  
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.

scholarly journals The Design of a Portable Municipal Waste Incinerator With Fuzzy Logic Based Support for Emission Estimation

2016 ◽  
Vol 5 (3) ◽  
Author(s):  
Jude C. Akpe ◽  
Olatunde A. Oyelaran ◽  
Ibrahim O. Abdulmalik
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Low Carbon Steel ◽  
Maximum Temperature ◽  
Waste Incinerator ◽  
Low Carbon ◽  
Possibility Distribution ◽  
Inference System ◽  
Complete Combustion ◽  
Input Variables

A fuzzy logic interface system to estimate oxygen requirement for complete combustion as well as the level of pollution from incinerator gas flue in order to manage solid waste from domestic, institutional, medical and industrial sources was designed. The designed incinerator is double chambered operating with a maximum temperature of 760 °C in the lower chamber and 1000°C in the upper chamber.  The insulating wall is made up of a refractory brick of 55mm in thickness having a 2mm thickness low carbon steel as the outer wall.  Hydrogen Chloride (HCl) and Nitrous oxides (NO)x are the gases was used to demonstrate the Fuzzy Inference System (FIS) model. The FIS was built with five input variables (Food, PVC, Polythene, Paper and Textile) and three input variables with two membership functions. The FIS was developed to estimation the degree of possibility distribution of pollution that should be expected when a certain composition of waste is incinerated. The plots of composition of waste high in food against oxygen require for combustion gives a possibility distribution of about 0.9 which is high according to the fuzzy set definition while the plot of waste composition high in PVC against HCL shows linearity.

scholarly journals Determination of Difficulty Level for Garment Model with Fuzzy Logic Method

2021 ◽  
Vol 28 (121) ◽  
pp. 39-47
Author(s):  
Hilal Bilgiç ◽  
Yusuf Kuvvetli ◽  
Pınar Duru Baykal
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Real Life ◽  
Operation Time ◽  
Difficulty Level ◽  
Decision Framework ◽  
Inference System ◽  
Fuzzy Logic Approach ◽  
Input Variables ◽  
Fabric Weight

The purpose of this study is a rule-based fuzzy logic approach is proposed for determining model difficulty in manufacturing top clothing for ladies. A decision framework concerned with different scenarios (main pattern types and material types) is proposed for determining the model difficulty. Each scenario modeled as a Mamdani type fuzzy inference system which is known as one of the best approximator fuzzy logic models. The fuzzified input variables are unit operation time, second quality rate and fabric weight. Moreover, two different defuzzification methods which are centroid and middle of maxima are compared for finding best fuzzy logic structure over the six different test instances. According to the results, both deffuzzification methods find similar model difficulty determinations. A graphical user interface of the proposed decision framework is designed in order to apply this to real-life applications. Finally, six different clothing models are identified to be simple, medium-hard, hard and very hard. The results of this study showed that defuzzification methods is not significantly effected the model difficulty decisions off is systems regarding different test instances. The model difficulty values range between 0-10. In order to find a useful difficulty assignment (linguistic), the model difficulty is determined by using the closeness to center value (a2) of membership functions. This research offers a solution to determine the difficulty levels of the garment models.

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

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.

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

2019 ◽  
Vol 8 (2S6) ◽  
pp. 347-351
Keyword(s):  
Collision Avoidance ◽  
Fuzzy Inference System ◽  
Information Exchange ◽  
Fuzzy Inference ◽  
Algorithm Design ◽  
Inference System ◽  
Safe Navigation ◽  
Message Transmission ◽  
Autonomous Surface Vehicle ◽  
International Regulations

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

scholarly journals PENILAIAN MUTU DOSEN TERHADAP TRI DHARMA PERGURUAN TINGGI DENGAN MENERAPKAN LOGICA FUZZY LOGIC DI STIKES NAN TONGGA

2019 ◽  
Vol 8 (2) ◽  
pp. 175
Author(s):  
Tri Monarita Johan ◽  
Renty Ahmalia
Keyword(s):  
Higher Education ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Teaching Staff ◽  
Output Variable ◽  
Inference System ◽  
Quality Value ◽  
Input Variables ◽  
Collection Methods ◽  
Functional Position

Tri Dharma of Higher Education is an activity that must be carried out by every Lecturer. In this study an application was designed to apply Fuzzy logic to calculate the quality value of Lecturers on the implementation of Higher Education Tri Dharma. Higher Education has the aim of producing quality qualifications. Therefore we need competent teaching staff needed. The background of this research is to study the results obtained from the application and calculation using Fuzzy logic, also help the lecturer evaluation in the field of quality control. The Mamdani Method is often also known as the Max-Min Method. This method was introduced by Ebrahim Mamdani in 1975. To get results, four stages are needed: 1. The formation of the fuzzy set; 2. Application function implications (rules); 3. Composition of rules; 4. Affirmation (deffuzy). The results obtained in this study the value of the function that has been optimized where lecturers will get the best in performance. Data collection methods in the fuzzy inference system function meeting, the author requires input data consisting of three variables and one output variable. Input variables consist of: 1. Research Variables 2. Dedication Variables 3. Teaching Variables. 4. Functional Position Variables After calculations and experiments, the results obtained using the Fuzzy Mamdani method with Matlab

scholarly journals Line Follower Robot Optimization based Fuzzy Logic Controller Using Membership Function Tuning

2018 ◽  
Vol 7 (2.2) ◽  
pp. 112
Author(s):  
Supriadi Supriadi ◽  
Ansar Rizal ◽  
Didi Susilo Budi Utomo ◽  
Agusma Wajiansyah
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Fuzzy Inference ◽  
Inference System ◽  
On Line ◽  
Input Variables ◽  
The Right ◽  
Triangular Membership Function

The study was aimed to measure the performance of Fuzzy Logic Controller (FLC) on Line Follower Robot (LFR). FLC output is a deviation value of Pulse Width Modulation (PWM) to determine the rotational speed of the left and the right wheel. As input variables are current and previous line sensors. Tuning was applied to input and output variables in each membership function (MF) to conduct the best performance. This study used triangular membership function that consists of three MF. Mamdani Fuzzy Inference System (FIS) is used using nine rules. The result obtains that after MF tuning, the performance of the LFR settling time is 0.63s faster compare to that without tuning.  

scholarly journals A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Arati M. Dixit ◽  
Harpreet Singh
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Source Identification ◽  
Field Programmable Gate Array ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Field Programmable ◽  
Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

Intelligent cooperative collision avoidance via fuzzy potential fields

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Daegyun Choi ◽  
Anirudh Chhabra ◽  
Donghoon Kim
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Potential Fields ◽  
Rule Base ◽  
Local Minima ◽  
Inference System ◽  
Aerial Vehicles ◽  
Different Shapes ◽  
Simulation Results

Summary This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

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