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 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 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

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.

scholarly journals Regional Collision Risk Prediction System at a Collision Area Considering Spatial Pattern

2021 ◽  
Vol 9 (12) ◽  
pp. 1365
Author(s):  
Ho Namgung ◽  
Joo-Sung Kim
Keyword(s):  
Spatial Pattern ◽  
Risk Prediction ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Prediction System ◽  
Collision Risk ◽  
Inference System ◽  
Optimal Decisions

To reduce the risk of collision in territorial sea areas, including trade ports and entry waterways, and to enhance the safety and efficiency of ship passage, the International Maritime Organization requires the governing body of every country to establish and operate a vessel traffic service (VTS). However, previous studies on risk prediction models did not consider the locations of near collisions and actual collisions and only employed a combined collision risk index in surveillance sea areas. In this study, we propose a regional collision risk prediction system for a collision area considering spatial patterns using a density-based spatial clustering of applications with noise (DBSCAN). Furthermore, a fuzzy inference system based on a near collision (FIS-NC) and long short-term memory (LSTM) is adopted to help a vessel traffic service operator (VTSO) make timely optimal decisions. In the local spatial pattern stage, the ship trajectory was determined by identifying the actual-collision and near-collision locations simultaneously. Finally, the system was developed by learning a sequence dataset from the extracted trajectory of the ship when a collision occurred. The proposed system can recommend an action faster than the fuzzy inference system based on the near-collision location. Therefore, using the developed system, a VTSO can quickly predict ship collision risk situations and make timely optimal decisions at dangerous surveillance sea areas.

scholarly journals Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 103 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
Do-Hyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Significant Risk ◽  
Assessment Method ◽  
Logic Model ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

scholarly journals Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 182 ◽  
Author(s):  
Muhammad Fayaz ◽  
Shabir Ahmad ◽  
Lei Hang ◽  
DoHyeun Kim
Keyword(s):  
Water Supply ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Residential Buildings ◽  
Risk Index ◽  
Inference System ◽  
Full Structure ◽  
Implementation Time ◽  
Novel Method ◽  
Water Supply Pipeline

As populations grow, facilities such as roads, bridges, railways lines, commercial and residential buildings, etc., must be expanded and maintained. There are extensive networks of underground facilities to fulfil the demand, such as water supply pipelines, sewage pipelines, metro structures, etc. Hence, a method to regularly assesses the risk of the underground facility failures is needed to decrease the chance of accidental loss of service or accidents that endanger people and facilities. In the proposed work, a cohesive hierarchical fuzzy inference system (CHFIS) was developed. A novel method is proposed for membership function (MF) determination called the heuristic based membership functions determination (HBMFD) method to determine an appropriate MF set for each fuzzy logic method in CHFIS. The proposed model was developed to decrease the number of rules for the full structure fuzzy inference system with all rule implementation. Four very crucial parameters were considered in the proposed work that are inputs to the proposed CHFIS model in order to calculate the risk of water supply pipelines. In order to fully implement the proposed CHFIS just 85 rules are needed while using the traditional Mamdani fuzzy inference system, 900 rules are required. The novel method greatly reduces implementation time and rule design sets that are extremely time consuming to develop and difficult to maintain.

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.

Multi-robot path planning based on improved artificial potential field and fuzzy inference system1

2020 ◽  
Vol 39 (5) ◽  
pp. 7621-7637 ◽  
Author(s):  
Tao Zhao ◽  
Haodong Li ◽  
Songyi Dian
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Fuzzy Inference ◽  
Target Position ◽  
Assessment Method ◽  
Artificial Potential Field ◽  
Collision Risk ◽  
Inference System ◽  
Field Function ◽  
Multi Robot

In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems.

Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

2022 ◽  
pp. 036119812110702
Author(s):  
Shunchao Wang ◽  
Zhibin Li ◽  
Bingtong Wang ◽  
Jingfeng Ma ◽  
Jingcai Yu
Keyword(s):  
Motion Planning ◽  
Collision Avoidance ◽  
Motion Tracking ◽  
Collision Risk ◽  
Automated Vehicles ◽  
Vehicle Collision ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Velocity Obstacle ◽  
Vehicle Dynamic Model

This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

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