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All Journal Perfecting a Video Game with Game Metrics Jurnal Biosains Pascasarjana INTEGER: Journal of Information Technology Darmabakti Cendekia: Journal of Community Service and Engagements Indonesian Applied Physics Letters
Winarno, Winarno
Departemen Fisika Fakultas Sains Dan Teknologi Universitas Airlangga
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Materials Science & Nanotechnology Physics Public Health Social Sciences

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Journal : Perfecting a Video Game with Game Metrics

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Object detection for KRSBI robot soccer using PeleeNet on omnidirectional camera Winarno Winarno; Ali Suryaperdana Agoes; Eva Inaiyah Agustin; Deny Arifianto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.15009

Abstract

Kontes Robot Sepak Bola Indonesia (KRSBI) is an annual event for contestants to compete their design and robot engineering in the field of robot soccer. Each contestant tries to win the match by scoring a goal toward the opponent's goal. In order to score a goal, the robot needs to find the ball, locate the goal, then kick the ball toward goal. We employed an omnidirectional vision camera as a visual sensor for a robot to perceive the object’s information. We calibrated streaming images from the camera to remove the mirror distortion. Furthermore, we deployed PeleeNet as our deep learning model for object detection. We fine-tuned PeleeNet on our dataset generated from our image collection. Our experiment result showed PeleeNet had the potential for deep learning mobile platform in KRSBI as the object detection architecture. It had a perfect combination of memory efficiency, speed and accuracy.
Application of EMG and Force Signals of Elbow Joint on Robot-assisted Arm Training Riky Tri Yunardi; Eva Inaiyah Agustin; Risalatul Latifah; Winarno Winarno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 6: December 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i6.11707

Abstract

Flexion-extension based on the system's robotic arm has the potential to increase the patient's elbow joint movement. The force sensor and electromyography signals can support the biomechanical system to detect electrical signals generated by the muscles of the biological. The purpose of this study is to implement the design of force sensor and EMG signals application on the elbow flexion motion of the upper arm. In this experiments, the movements of flexion at an angle of 45º, 90º and 135º is applied to identify the relationship between the amplitude of the EMG and force signals on every angle. The contribution of this research is for supporting the development of the Robot-Assisted Arm Training. The correlation between the force signal and the EMG signal from the subject studied in the elbow joint motion tests. The application of sensors tested by an experimental on healthy subjects to simulating arm movement. The experimental results show the relationship between the amplitude of the EMG and force signals on flexion angle of the joint mechanism for monitoring the angular displacement of the robotic arm. Further developments in the design of force sensor and EMG signals are potentially for open the way for the next researches based on the physiological condition of each patient.
Co-Authors Agustin, Eva Inaiyah Aji Akbar Firdaus Ali Suryaperdana Agoes Alim Prasaja Anny Setijo Rahaju, Anny Setijo Arifianto, Deny Hadiyyatan Waasilah Kadek Juni Setiawati Mohammad Kholil Novian Patria Uman Putra Riries Rulaningtyas Risalatul Latifah Yunardi, Riky Tri