Lane tracking and obstacle avoidance for Autonomous Ground Vehicles

Al-Zaher, Tamer S. Abd; Bayoumy, A. M.; Sharaf, A. M.; El-Din, Y. Hossam

doi:10.1109/mecatronics.2012.6451019

Cited by 9 publications

(10 citation statements)

References 5 publications

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“…The system employs camera for collecting data in real time and images captured by camera were used to recognize the lanes lines and obstacle dimension. A PID controller used to predict the vehicle-heading angle [27]. Wan Rahiman designed PI controller for velocity control of a mobile robot.…”

Section: Proportional Integral Derivative (Pid)mentioning

confidence: 99%

A Review of Control Algorithm for Autonomous Guided Vehicle

Gul

2020

IJEECS

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The autonomous guided vehicle is a efficient and effective platform for control system. Their non-linear nature helps in analysing the control algorithms more efficiently and effectively. The main objective of path planning is to find the optimal and shortest path avoiding the time complexity so environment can be modelled completely for vehicle. The paper includes explanation of different systems together with numerous algorithms have been discussed with advantages and disadvantages for example: Fuzzy control, Neural Control, Back-stepping control, Adaptive control, Sliding mode control and PID control and linear quadratic regulator. The conclusion includes the hybrid system integration based on the advantages and disadvantages presented in this paper.

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Section: Proportional Integral Derivative (Pid)mentioning

confidence: 99%

A Review of Control Algorithm for Autonomous Guided Vehicle

Gul

2020

IJEECS

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“…Another approach discussed in the literature [17] is a mechatronics system comprising a PID controller which predicts and controls the vehicle heading angle in order to follow the lane or to avoid the obstacles.…”

Section: Computer-vision-based Approach Using Pid Controllermentioning

confidence: 99%

Obstacle Detection and Track Detection in Autonomous Cars

Iqbal¹

2020

Autonomous Vehicle and Smart Traffic

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This chapter illustrates the history and recent advancements in the field of Autonomous Vehicles with regard to two important concerns that play the most vital role in successful implementation and working of an Autonomous Car: (1) Obstacle Detection and (2) Track Detection. The car should be able to detect the obstacles for smooth and efficient working in order to avoid accident and collision. It should also be able to calculate the distance of the obstacle from the car. Similarly, Track Detection is also important as the autonomous car should stay within a predefined track and has to keep itself within the yellow lines on both sides of the road. This chapter elaborates the technologies and advancements that have been presented in the literature till date that deal with Obstacle Detection and Track Detection in Autonomous Cars/Vehicles.

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“…In [9] it presents an experimental results of an autonomous vehicle. This vehicle is able to detect lane and tracking it by using digital camera.…”

Section: Fig 2 a Block Diagram Of A Pid Controller In A Feedback Loopmentioning

confidence: 99%

Implementation of Vision-Based Trajectory Control for Autonomous Vehicles

Desoky¹,

Bayoumy²,

Hassaan³

2017

Eng. Sci. and Milit. Techno.

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This paper demonstrates building, implementing, and developing a trajectory tracking control system based on computer vision for autonomous vehicles. The main goal of this system is to enforce the autonomous vehicle to be able to track road lane. This system includes a single digital camera, an embedded computer, and a microcontroller board. The digital camera is mounted at the top of the vehicle along its longitudinal axis. It captures a real-time sequence of images during vehicle motion. The captured images are then processed using Open-CV library for Python compiler over Linux operating system. These software packages are running on the embedded computer (Raspberry Pi 2) to obtain geometrical data of road lane. From this data, the observable errors can be determined. These errors are vehicle lateral offset and a heading error. Finally, a steering controller utilizes these errors in control law to compute the steering command. This command corrects offset and heading errors to ensure that the vehicle is in its way. The embedded computer then paths this command to Arduino microcontroller board to adjust the steering servomotor. The proposed implementation also demonstrates the integration between the embedded computer and microcontroller using Ethernet. During this work, a set of autonomous driving experiments is performed. Significant results are obtained that demonstrate the accuracy and robustness of the lane detection and control algorithms.

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Lane tracking and obstacle avoidance for Autonomous Ground Vehicles

Cited by 9 publications

References 5 publications

A Review of Control Algorithm for Autonomous Guided Vehicle

A Review of Control Algorithm for Autonomous Guided Vehicle

Obstacle Detection and Track Detection in Autonomous Cars

Implementation of Vision-Based Trajectory Control for Autonomous Vehicles

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