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All Journal Sinergi TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Tian, Yingzhong
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Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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 1 
AN FLC-PSO ALGORITHM-CONTROLLED MOBILE ROBOT Suwoyo, Heru; Tian, Yingzhong; Ibnu Hajar, Muhammad Hafizd
SINERGI Vol 24, No 3 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2020.3.002

Abstract

The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.
THE ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT Suwoyo, Heru; Thong, Zhou; Tian, Yingzhong; Adriansyah, Andi; Ibnu Hajar, Muhammad Hafizd
TEKNOKOM Vol. 5 No. 1 (2022): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (734.663 KB) | DOI: 10.31943/teknokom.v5i1.74

Abstract

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE.
Problem solving path planning and path tracking in a 3 DOF hexapod robot using the RRT* algorithm with path optimization and Pose-to-Pose Suwoyo, Heru; Burhanudin, Achmad; Tian, Yingzhong; Andika, Julpri
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.007

Abstract

Path planning is one of the most fundamental problems that must be solved before a robot can navigate and explore autonomously. Path planning needs to be integrated with path tracking to be applied to autonomous robots. This makes path tracking also important for autonomous robot navigation which cannot be separated from path planning. There are two path planning methods, the first is search-based method, the second is sampling-based method. Both have their own advantages, but the popular and commonly used sampling-based algorithm due to its fast convergence is preferred in path planning.  The RRT* algorithm was developed. This improvement initiated a major civilization in sampling-based algorithms, namely parent node selection and rewiring in RRT. Although there has been an improvement in optimality, RRT* still doesn't provide the distance optimality value as expected, due to its character that is still adopted from RRT.  The resulting path is still suboptimal and not smooth (jagged). On the other side, Path tracking has several methods, however, these path tracking methods are difficult to apply to autonomous robots and need to be adapted to the robot used. Based on the description above, there are still problems with path planning, namely paths that are still less than optimal and convergence that is still slow.  This research will add a way to shorten the distance in the RRT* algorithm with the triangular inequality method.  Meanwhile, for path tracking, we will apply the pose-to-pose method, which follows the waypoint that has been made by path planning.
Design of 3 DOF hexapod leg movement using inverse kinematics: bridging gaps in multilegged robot kinematics literature Suwoyo, Heru; Taufikurohman, Nur Aziz; Tian, Yingzhong; Burhanudin, Achmad
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.001

Abstract

Designing the motion of a hexapod robot with 3 Degrees of Freedom (DOF) using the Inverse Kinematics method allows the robot to move by adjusting the angles of its leg joints according to the desired position and direction. This research involves the geometric and structural design of the hexapod robot and the development of an Inverse Kinematics algorithm to calculate the leg joint angles based on the target pose. The study uses the Inverse Kinematics method to design a hexapod robot for movement with 3 DOF. The testing results show an average Inverse Kinematics error of 1.56 mm on the X-axis, 0.88 mm on the Y-axis, and 0.78 mm on the Z-axis. During the forward and backward movement tests covering a distance of 100 cm, the average error was 2.58 cm and 12.38 cm, respectively. For the rotation tests, the average error was 3.6° for a 90° rotation to the right, 3° for a 90° rotation to the left, 13.2° for a 180° rotation to the right, and 3.8° for a 180° rotation to the left. The results indicate that the design of the 3DOF hexapod robot using the Inverse Kinematics method provides a sufficient level of accuracy in controlling movements along the X, Y, and Z axes. Despite some errors, the robot is capable of moving fairly accurately during forward, backward, and rotational movements.
A HBMO-based batch beacon adjustment for improving the Fast-RRT Suwoyo, Heru; Tian, Yingzhong; Adriansyah, Andi; Andika, Julpri
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp107-119

Abstract

Fast-RRT improves on the original rapidly-exploring random trees (RRT) by incorporating two main stages: improved-RRT and fast-optimal. The improved-RRT stage enhances the search process through fast-sampling and random steering, while the fast-optimal stage optimizes the path using fusion and path arrangement. However, path fusion can only be optimal when the newly found path is unique and different from previous paths. This uniqueness rarely occurs in cases with narrow corridors, so path fusion only provides suboptimal conditions. To address this, the study explores using honey bee mating optimization (HBMO) to optimize or replace the fusion stage. HBMO helps determine new beacon coordinates, which are nodes between the start and goal points along the path, through a batch beacon adjustment approach. The results show that integrating HBMO into FastRRT improves its optimality, with a 21.85% reduction in path cost and a 5.22% decrease in completion time across environments with varying difficulty levels. This hybrid algorithm outperforms previous methods in terms of both path optimality and convergence rate, demonstrating its effectiveness in enhancing Fast-RRT’s performance.
An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization Suwoyo, Heru; Adriansyah, Andi; Andika, Julpri; Shamsudin, Abu Ubaidah; Tian, Yingzhong
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.22062

Abstract

Conceptually, Fast-RRT applies fast sampling and random steering which makes the initial path quickly obtained. Referring to the initial path, the optimality of the path is improved by applying path fusion and path optimization. Theoretically, path fusion will only be optimal if there is always a unique/different path to be fused with the previously obtained path. However, in the conditions of solving path planning problems in narrow corridors, the potential for obtaining a different path from the previous one is very small. So that fusion does not run properly, but checking the relationship between nodes to nodes still occurs. Instead of getting an optimal path in conditions like this, the computation will increase, the solution time will be long, and the resulting path will still be sub-optimal. As an effort to solve this problem, Grey Wolf Optimization (GWO) is involved through this study. While an initial path is found, the beacons are repositioned. From the path, the number of nodes is unpredictable, causing the decision variables in optimization to become large. For this reason, the GWO is chosen because it is independent of population representation and is not affected by the number of decision variables. This proposed method is claimed to be more effective in solving path planning problems in terms of convergence rate and optimality. Therefore, the proposed method is evaluated and compared with previous methods and gives the result that the average working speed of Fast-RRT is improved by 90.25% and the optimality average increased by 5.67%.
Co-Authors Andi Adriansyah Burhanudin, Achmad Hossain, Md Musabbir Julpri Andika Li, Long Muhammad Hafizd Ibnu Hajar Shamsudin, Abu Ubaidah Suwoyo, Heru Taufikurohman, Nur Aziz Thong, Zhou Wang, Wenbin