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All Journal IAES International Journal of Robotics and Automation (IJRA) Indonesian Journal of Electronics and Instrumentation Systems Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Muhammad Auzan
Universitas Gadjah Mada
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering

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Swarm Intelligence Autotune For Differential Drive Wheeled Mobile Robot Muhammad Auzan; Roghib Muhammad Hujja; M Ridho Fuadin; Danang Lelono
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 3: September 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i3.3528

Abstract

Differential Drive Wheeled Mobile Robot (DDWMR) is a nonholonomic robot with constrained movement. Such constraint makes robot position control more difficult. A closed-loop control system such as PID can control robot position. However, DDWMR is a Multiple-Input-Multiple-Output system. There will be many feedback gains to be tuned, and the wrong value will make the system unstable. Therefore this research proposes an offline autotune method to choose optimal feedback gain that minimizes a fitness function. The fitness function uses Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE). These works propose to autotune feedback gain for DDWMR Jetbot, which implements a PI control system with six feedback gains. The methods used to tune the feedback gain are Particle Swarm Optimization (PSO) and Bird Swarm Algorithm (BSA). There are four different scenarios to do the autotune. The autotune result performance shows that those two methods can find an optimal gain to make the robot follow four different continuous trajectories without much trajectory deformation. PSO and BSA can do an autotune PI gain with six variables to minimize the Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE).
Humanoid robot balance control system during backward walking using linear quadratic regulator Muhammad Arsyi; Andi Dharmawan; Bakhtiar Alldino Ardi Sumbodo; Muhammad Auzan; Jazi Eko Istiyanto; Oskar Natan
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i4.pp472-482

Abstract

Humanoid robots are designed to replicate human activities, including tasks in hazardous environments. However, maintaining balance during backward walking remains a significant challenge due to center of mass (CoM) shifts beyond the support polygon and limited knee joint motion. This study proposes a control strategy that integrates a linear quadratic regulator (LQR) with optimized walking patterns to enhance dynamic stability. The approach combines LQR-based control with CoM trajectory planning to ensure safe and stable backward walking. The methodology includes inverse kinematics for generating walking patterns and the use of Inertial Measurement Unit (IMU) sensors to estimate the CoM trajectory. LQR parameters were tuned through simulation to improve responsiveness to disturbances. Evaluation metrics focused on CoM deviation, rise time, settling time, and overshoot. Experimental results demonstrate that the proposed LQR system effectively maintains the CoM within 5% of the support polygon boundary. The system achieved rise times under one second and settling times below two seconds, while minimizing pitch and roll overshoots. Compared to proportional control, the proposed method significantly improves stability and reduces the risk of falling. This research advances control strategies for humanoid robots, contributing to improved mobility and operational safety. Moreover, it supports Sustainable Development Goal (SDG) 9 by promoting innovation in intelligent robotic systems that can assist in complex or high-risk environments.
BOUNDARY BALLS DETECTION SYSTEM FOR UNMANNED SURFACE VEHICLE USING YOLOV4-TINY Diva Diansari Hanggraeni; Ika Candradewi; Muhammad Auzan
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 16, No 1 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.75869

Abstract

Unmanned Surface Vehicle (USV) technology requires a reference such as a camera to determine the direction of its motion. The classical digital image processing is still less accurate for detecting boundary objects due to changes in light intensity and the influence of the aquatic environment. This study used hyperparameter subdivision and learning rate analysis of YOLOv4-tiny to detect boundary balls and trajectory obstacles. The training model is used in video testing under various conditions to determine the system's robustness.The training was carried out with subdivision 8 and a learning rate of 0.00261, which obtained a mAP of 97.02%, 95.1% F1-score, and 87.73% avg IoU. This system is robust for various testing times in terms of the diversity of light intensity and object reflections as well as conditions when there are waves or not. The system can work optimally during the day with an average performance of 98.13% F1-score, 99.18% mAP, and 88.75% avg IoU. In the conditions where there are waves, the system shows that the performance is not much different when there are no waves, namely mAP 91.93%, F1-score 94.62%, and avg IoU 87.33%. The average detection processing speed on the Jetson Nano 4GB is 14.2 FPS.
Co-Authors Andi Dharmawan Bakhtiar Alldino Ardi Sumbodo Diva Diansari Hanggraeni Ika Candradewi Jazi Eko Istiyanto M Ridho Fuadin Muhammad Arsyi Oskar Natan Roghib Muhammad Hujja