A very challenging problem in mobile robot systems is mostly in obstacle avoidance strategies. This study aims to describe how the obstacle avoidance system on mobile robots works. This system is designed automatically using fuzzy logic control (FLC) developed using Matlab to help the mobile robots to avoid a head-on collision. The FLC designs were simulated on the mobile robot system. The simulation was conducted by comparing FLC performance to the proportional integral derivative (PID) controller. The simulation results indicate that FLC works better with lower settling time performance. To validate the results, FLC was used in a mobile robot system. It shows that FLC can control the velocity by braking or accelerating according to the sensor input installed in front of the mobile robot. The FLC control system functions as ultrasonic sensor input or a distance sensor. The input voltage was simulated with the potentiometer, whereas the output was shown by the velocity of DC motor. This study employed the simulation work in Simulink and Matlab, while the experimental work used laboratory scale of mobile robots. The results show that the velocity control of DC motors with FLC produces accurate data, so the robot could avoid the existing obstacles. The findings indicate that the simulation and the experimental work of FLC for mobile robot in obstacle avoidance are very close.
A Pico hydro power plant is one good solution to support energy independence for rural areas in Indonesia. The problem that normally emerged is the uncertainty of water debit. This may cause the damage of the Pico hydro power plant equipment. The main objective of this study is to design a water flow monitoring (WAFLOW-MT) device based on the Internet of Things (IoT). This device may help the technicians at the Pico hydro power plant in monitoring the speed of water flow at the river so that the water debit is recorded all the time. The design and development process of the WAFLOW-MT device was done through 4 stages: 1) need analysis; 2) system design; 3) system development, and 4) system testing. The data was collected from the water flow sensor and sent via IoT to the webserver thingspeak.com by 6 days duration of testing. According to the testing result, it can be concluded that the WAFLOW-MT device successfully monitored the water flow of the river.
The purpose of this article is to find out validity level PLC controlled automatic water filling and capping machine as trainer kit for learning in industry automation competence expertise in vocational high school. Research type used in this research is research and development (R&D) according to Robert Maribe Branch. carried out steps are; Analyze, Design, Develop, Implement, and Evaluate. Instrument used is questionnaire and observation sheet. Collected data is analyzed descriptively. Research result shown that validation toward automatic water filling and capping machine prototype as trainer kit for PC practical learning in vocational high school which done by media expert, material expert, and student shown result in very satisfactory category, indicated by: (1) media experts give average score 87.5% (very satisfactory); (2) material experts give average score of 86,9% (very satisfactory); and (3) student give average score of 95,3% (Very Good)
This study presents a robot movement in tracking 2D objects. This input image is transformed into another image by certain techniques. In this study, by utilizing image processing, the robot can work to detect objects in the form of hexagons. Apart from detecting the detected shape, this image processing is also for detecting color. So that a hexagon-shaped 2D object will be detected with the magenta color that has been set in advance. The movement of this robot is to follow the motion of objects horizontally. While the object is shifted to the right, the robot will move to the right, and if the object is shifted to the left, the robot will move to the left. Robot movement is controlled by fuzzy logic. There are 5 membership functions to divide the object’s position area and 5 membership functions output to adjust the speed and direction of the robot’s motion.
The development of image processing science is needed to solve problems that are often faced by humans, especially in the field of computer vision. One application of the image processing system is on a package delivery mission during the Covid-19 pandemic. Drones are used to send packages by detecting the presence of Qr Code to determine the point of delivery location. In this study, tests will be carried out on the maximum distance (vertical and horizontal) that can be detected by the Qr Code detection system and the length of time to detect the presence of the Qr Code (time spent). The test shows that the greater the data collection distance (vertical and horizontal), the longer the system detects the presence of the Qr Code. The maximum horizontal distance that the Qr Code can detect is 155 cm, while the vertical distance is 115 cm. The detection distance at vertical is smaller than horizontal because the vertical distance is affected by the field of view (FoV).
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