The Real-Time Object Detection System on Mobile Soccer Robot using YOLO v3

Soebhakti, Hendawan; Prayoga, Senanjung; Fatekha, Rifqi Amalya; Fashla, M. Buana

doi:10.1109/icae47758.2019.9221734

Cited by 14 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In YOLO version 5 calculations were carried out to evaluate the degree of inconsistency between the results of the prediction model and ground thrust. This degree is calculated from three components, namely bounding box loss [9], [10].…”

Section: Methodsmentioning

confidence: 99%

The Design of People Follower Robot to Transport Harvests Using Yolo V5 Detection

Imron,

Satria,

Nuraini

2023

SemanTIK

View full text Add to dashboard Cite

Robots transporting harvested products is commonly use automatic navigation systems with sensors such as lidar, cameras and ultrasonic proximity sensors are important for detecting obstacles and avoiding collisions. Mapping and positioning algorithm are crucial for precise the robot determine position in the field. A dependable drive system, adaptable to diverse terrains, and a robust motor are essential components. Efficient energy requirements must be addressed by using long-lasting battery systems and, where possible, automatic charging solutions. Robots picking mechanisms that can adapt to different types of plants, as well as storage systems that protect vegetables from damage during transport, also need to be considered. This writing utilizes does not huge robot harvest hardware as a prototype due to research scope limitations. Instead, it involves instructing a Jetson Nano and Arduino Uno to direct the camera towards the tracked object using coding through python and Arduino IDE. Computer mini used Jetson Nano for running the best model object tracking. Output serial connected with Arduino for control actuator like motor DC and servo motor. The method used is YOLOv5, while the image processing technique employs object detection, which likely serves for object identification and localization. The additional computer vision facilitates the camera to adapt to the objects. Through testing, the system successfully detects objects, particularly vest, with an average confidence value of 0.91%, indicating that the detection system has a very high level of accuracy in identifying and detecting vest. Keywords; Robot, Jetson nano, Arduino Uno, Object Detection, Yolov5

show abstract

Section: Methodsmentioning

confidence: 99%

The Design of People Follower Robot to Transport Harvests Using Yolo V5 Detection

Imron,

Satria,

Nuraini

2023

SemanTIK

View full text Add to dashboard Cite

show abstract

“…This method was used on robots because the robot requires a detection system that had a fairly high level of accuracy and was able to work in various light conditions. Apart from that, This methode [5] uses a Deep Learning system which can learn an object or case by itself.…”

Section: Object Detectionmentioning

confidence: 99%

Localization System on Wheel Robot Soccer

Pratama,

Soebhakti,

Prayoga

et al. 2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

View full text Add to dashboard Cite

Wheeled robot soccer is a robot designed to play soccer. In Fact, the robot must be able to play like humans in wheeled robot soccer competition. The positioning of robot is one of the most important things in the manufacture of automatic robots. Robots had to know their position and the opponent robot position . One way to find out how far the robot moves is by using a rotary encoder equipped with a gyroscope. However, this method is easily affected by the environment like slips condition on the rotary encoder wheel, by resulting integral error value the error value was bigger than robot movement. Based on the result of experiment, experiments using the combination of odometry and vision methods are much better than using only odometry because it is not affected by slip. Which the average of x coordinate at 13.64 cm and average of y coordinate at 12.44 cm and the odometry methode the number of errors will continue to grow as more robots move which the average of x coordinate at 13.26 cm and average of y coordinate at 20.97 cm.

show abstract