Computer
Noor Abdul Khaleq Zghair; Ahmed S. Al-Araji
Abstract
A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. ...
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A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. Several algorithms for path planning and obstacle avoidance have been presented by various researchers, each with its own set of benefits and drawbacks. This paper focuses on two parts; the first part finds the short and smooth collision-free path for a mobile robot to navigate in a static environment based on two proposed hybrid algorithms. The first hybrid is between Firefly Algorithm (FA) and Modify Chaotic Particle Swarm Optimization (MCPSO), namely (HFACPSO), while the other hybrid is between Genetic Algorithm (GA) and MCPSO, namely (HGACPSO).The second part suggests an algorithm planner for improving the efficiency of the route-planning algorithm with moving obstacle avoidance by adjusting the velocity or re-planning the path for the mobile robot. To demonstrate the effectiveness of the proposed algorithms in terms of the shortest path length and collision-free, as well as obtaining optimal or near-optimal wheel velocities with the minimum number of iterations. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equal to (0.82%) compared to the firefly algorithm (FA). Moreover, the hybrid (GAMCPSO) algorithm enhancement on the path length equals (0.67%) compared to the genetic algorithm (GA). All methods are simulated in a static and dynamic obstacle environment using MATLAB 2018b.
Communication
Abdulqader Falhi Jabbar; Rana Fareed Ghani; Asia Ali Salman
Abstract
Road traffic accidents are one of the leading causes of mortality globally. Reducing the number of traffic-related incidents has become a serious socio-economic and public health problem, given the ever-increasing number of cars on the road. As a result, this paper proposes an intelligent vehicle prediction ...
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Road traffic accidents are one of the leading causes of mortality globally. Reducing the number of traffic-related incidents has become a serious socio-economic and public health problem, given the ever-increasing number of cars on the road. As a result, this paper proposes an intelligent vehicle prediction communication mechanism that alerts drivers to any autos that may be overtaking or bypassing the targeted vehicle. The primary goal of this paper is to leverage modern Internet of Things (IoT) and wireless sensor technologies to predict any potential accident that may occur as a result of car accidents. This paper proposes the Collision Prediction of a Moving Vehicle (CPMV) system. The information acquired by CPMV will alert the driver to divert the vehicle in a reasonable amount of time before any harm occurs. It redirects the inbound object that emitted the Ultrasound signal which was received by the vehicle, to a safe location. The proposed system predicts collision between vehicles through Wi-Fi and Bluetooth, using a set of sensors with a precision of 360 degrees and a distance of collision prediction of one meter and at a speed of 200-300 revolutions per minute. The python programming language was utilized to code the programs that control the vehicle during the implementation of this project. The Raspberry Pi 4 is utilized as the controller to examine the vehicle’s spatial data. The test results showed that using this application to deal with an approaching object can be a successful strategy in the three proposed scenarios at different angles and directions.