Line Follower with Obstacle Information System Using ZigBee

Kokare, Snehal; Shastri, Rajveer; Kolhar, Shrikrishna

doi:10.1109/iccubea.2018.8697470

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…When this sensor is in front of a white track, the infrared beam is strongly reflected and the phototransistor is then satured. When this sensor is in front of a black track, the infrared beam is practically not reflected and the phototransistor is then blocked [14]. There is a wide variety of IR sensors, but the CNY70 was chosen directly because of its small size (7mm x 7mm x 6mm) and the accuracy required by the proposed principle of following a line [15].…”

Section: Sensors 221 Infrared Ray Sensorsmentioning

confidence: 99%

Design and implementation of line follower and obstacle detection robot

Bendimrad

Amrani

2020

IJPEDS

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<p>In this paper, we propose a method for a line follower robot based on the instantaneous computation of the radius of curvature of this line, using infrared line sensors. The number and layout of its sensors, as well as the method chosen, play an important role in the robot's response to the line, with the desired accuracy and speed. In addition, the robot must be equipped with an anti-collision system, using an ultrasonic distance sensor, to detect and avoid obstacles in several situations, especially at line crossovers, when other robots share a common complex line.</p>

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Section: Sensors 221 Infrared Ray Sensorsmentioning

confidence: 99%

Design and implementation of line follower and obstacle detection robot

Bendimrad

Amrani

2020

IJPEDS

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“…As shown in Figure 3, the circuit is composed of several circuit blocks with installed components according to their functions, and are electrically combined to become a system that is suitable for module design. There are several blocks installed in this system; (a) System minimum [31], [32], (b) sensor circuit, and (c) 16x2 LCD.…”

Section: B System Hardwarementioning

confidence: 99%

Gas Pressure Measurement Device and Medical Vacuum Design

Tunggal

Sanjaya

Widodo

et al. 2020

jrc

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Two methods are used in a digital pressure meter available in the market, namely positive pressure, and negative pressure. The positive one is used to measure the air pressure on a sphygmomanometer and medical gas pressure at an outlet in the treatment room so that operators can easily check medical gas pressure for the safety of the patient, or they can routinely check to find out how much pressure of the medical gas. Based on the background, the research aimed to design a digital pressure meter equipped with a medical gas measurement mode so that the device can be used to calibrate the sphygmomanometer, suction pump, and measure the medical gas pressure available at the medical gas outlet in each treatment room.

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“…Numerous studies [6][7][8] have highlighted the usefulness of infrared sensors for line detection systems. These are located on the underside of the robot base and emit a beam of infrared light, which allows us to detect the amount of infrared light re lected from the ground surface.…”

Section: Introductionmentioning

confidence: 99%

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

Cesar Minaya,

Rosero,

Zambrano

et al. 2024

JAMRIS

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The paper presents an approach for controlling a line-following robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following Robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following Robot on a running track with different configurations is then evaluated. The results show that the line-following Robot responded more efficiently with an artificial neural network control algorithm than a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the Robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following Robot to adapt to environmental conditions and overcome obstacles on the track more effectively.

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Line Follower with Obstacle Information System Using ZigBee

Cited by 6 publications

References 7 publications

Design and implementation of line follower and obstacle detection robot

Design and implementation of line follower and obstacle detection robot

Gas Pressure Measurement Device and Medical Vacuum Design

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

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