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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization
ULURRASYADI, FAIZ
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Rule-Based Learning untuk Robot Humanoid T-FLoW Belajar Berjalan ULURRASYADI, FAIZ; BARAKBAH, ALIRIDHO; DEWANTO, RADEN SANGGAR; PRAMADIHANTO, DADET
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 1: Published January 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i1.46

Abstract

ABSTRAKRiset tentang penggunaan learning dalam motion robot humanoid telah banyak dilakukan di seluruh dunia. Salah satunya adalah melakukan learning gerakan berjalan pada robot. Penelitian ini akan menjelaskan suatu metode learning “Rule Based” yang simple dan cepat dalam menemukan solusi gerakan berjalan yang stabil pada robot humanoid T-FLoW . Robot diibaratkan seperti anak kecil yang belajar berjalan, dia tahu cara berjalan, akan tetapi tidak tahu seberapa besar dia harus menggerakkan sendi-sendi atau joint di kakinya agar dapat berjalan seimbang. Oleh karena itu sistem learning akan menemukan nilai point-point trayektori yang cocok untuk berjalan dengan stabil. Dengan menggunakan software simulasi CoppeliaSim, kami menerapkan metode tersebut. Hasilnya, robot humanoid T-FLoW dapat berjalan dengan stabil sejauh 170 langkah hanya dengan melakukan learning sebanyak 400 episode.Kata kunci: Robot humanoid T-FLoW, Rule-Based Learning, Learning, CoppeliaSim, Trayektori. ABSTRACTResearch about the use of learning in motion of humanoid robot has been done in many countries. One of them was done by learning a stable walking gait in humanoid robot. This research will explain a fast and simple Rule Based learning method to find the solution of stable walking motion for T-FLoW humanoid robot. A robot was assumed like a child trying to walk, he knows how to walk, but doesn’t know how much he has to move his legged joints to get a stable walking. So, our learning system will find those trajectory point values that is suitable to walk stably. By using CoppeliaSim software, we implement our method. The result is, T-FLoW humanoid robot was able to walk stably for about 170 steps with only 400 episodes of learning.Keywords: T-FLoW humanoid robot, Rule-Based Learning, Learning, CoppeliaSim, Trajectory.
Concept and Design of Anthropomorphic Robot Hand with a Finger Movement Mechanism based on a Lever for Humanoid Robot T-FLoW 3.0 Apriandy, Kevin Ilham; Ulurrasyadi, Faiz; Dewanto, Raden Sanggar; Dewantara, Bima Sena Bayu; Pramadihanto, Dadet
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1793

Abstract

This work described a concept and design of an anthropomorphic robot hand for the T-FLoW 3.0 humanoid robot, which featured a mechanism based on a lever as its finger movement. This work aimed to provide an affordable, modular, lightweight, human-like robot hand with a mechanism that minimizes mechanical slippage. The proposed mechanism works based on the push/pull of a lever attached to the finger to generate its finger flexion/extension movement. The finger’s lever is pushed/pulled through a servo horn and a rigid bar by the affordable TowerPro MG90S micro-servo. Our hand is developed only as necessary to become close to human hands by only applying five fingers and six joints, where each joint has its actuator. The combination of 3D printing technology with PLA filament accelerates and streamlines the manufacturing process, provides a realistic appearance, and achieves a lightweight, affordable, and easy maintenance product. Structural analysis simulations show that our finger design constructed with PLA material could withstand a load of about 30 N. We verified our finger mechanism by repeatedly flexing and extending the finger 30 times, and the results showed that the finger movements could be performed well. Our hand offered excellent handling for the mechanical issues brought on by finger movements, one of the issues that robot hand researchers have encountered. Our work could provide significant benefits to the T-FLoW 3.0 developers in enhancing the ability of humanoid robots involving hands, such as grasping and manipulating objects.
Co-Authors Ali Ridho Barakbah APRIANDY, KEVIN ILHAM Bima Sena Bayu Dewantara Dadet Pramadihanto Dewanto, Raden Sanggar