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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Sinergi Jurnal Teknologi Elektro Proceeding of the Electrical Engineering Computer Science and Informatics InComTech: Jurnal Telekomunikasi dan Komputer TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Jurnal Abdi Masyarakat (JAM) Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Abdidas Journal FORTEI-JEERI Journal of Community Service International Journal of Engineering Continuity
Suwoyo, Heru
Universitas Mercu Buana
Aerospace Engineering Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Sinergi

 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.
ENHANCING THE PERFORMANCE OF THE WALL-FOLLOWING ROBOT BASED ON FLC-GA Heru Suwoyo; Yingzhong Tian; Muhammad Hafizd Ibnu Hajar
SINERGI Vol 24, No 2 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.577 KB) | DOI: 10.22441/sinergi.2020.2.008

Abstract

Determination of the improper speed of the wall-following robot will produce a wavy motion. This common problem can be solved by adding a Fuzzy Logic Controller (FLC) to the system. The usage of FLC is very influential on the performance of the wall-following robot. Accuracy in the determination of speed is largely based on the setting of the membership function that becomes the value of its input. So manual setting on membership function can still be enhanced by approaching the certain optimization method. This paper describes an optimization method based on Genetic Algorithm (GA). It is used to improving the ability of FLC to control the wall-following robot controlled by FLC. To provide clarity, the wall-following robot that controlled using an FLC with manual settings will be simulated and compared with the performance of wall-following robots controlled by a fuzzy logic controller optimized by a Genetic Algorithm (FLC-GA). According to comparative results, the proposed method has been showing effectiveness in terms of stability indicated by a small error.
A MAPAEKF-SLAM ALGORITHM WITH RECURSIVE MEAN AND COVARIANCE OF PROCESS AND MEASUREMENT NOISE STATISTIC Heru Suwoyo; Yingzhong Tian; Wenbin Wang; Md Musabbir Hossain; Long Li
SINERGI Vol 24, No 1 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (859.498 KB) | DOI: 10.22441/sinergi.2020.1.006

Abstract

The most popular filtering method used for solving a Simultaneous Localization and Mapping is the Extended Kalman Filter. Essentially, it requires prior stochastic knowledge both the process and measurement noise statistic. In order to avoid this requirement, these noise statistics have been defined at the beginning and kept to be fixed for the whole process. Indeed, it will satisfy the desired robustness in the case of simulation. Oppositely, due to the continuous uncertainty affected by the dynamic system under time integration, this manner is strongly not recommended. The reason is, improperly defined noise will not only degrade the filter performance but also might lead the filter to divergence condition. For this reason, there has been a strong manner well-termed as an adaptive-based strategy that commonly used to equip the classical filter for having an ability to approximate the noise statistic. Of course, by knowing the closely responsive noise statistic, the robustness and accuracy of an EKF can increase. However, most of the existed Adaptive-EKF only considered that the process and measurement noise statistic are characteristically zero-mean and responsive covariances. Accordingly, the robustness of EKF can still be enhanced. This paper presents a proposed method named as a MAPAEKF-SLAM algorithm used for solving the SLAM problem of a mobile robot, Turtlebot2. Sequentially, a classical EKF was estimated using Maximum a Posteriori. However, due to the existence of unobserved value, EKF was also smoothed one time based on the fixed-interval smoothing method. This smoothing step aims to keep-up the derivation process under MAP creation. Realistically, this proposed method was simulated and compared to the conventional one. Finally, it has been showing better accuracy in terms of Root Mean Square Error (RMSE) of both Estimated Map Coordinate (EMC) and Estimated Path Coordinate (EPC).       
AN FLC-PSO ALGORITHM-CONTROLLED MOBILE ROBOT Heru Suwoyo; Yingzhong Tian; Muhammad Hafizd Ibnu Hajar
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.
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.
The use of Fuzzy Logic Controller and Artificial Bee Colony for optimizing adaptive SVSF in robot localization algorithm Suwoyo, Heru; Hajar, Muhammad Hafizd Ibnu; Indriyanti, Prastika; Febriandirza, Arafat
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.003

Abstract

The objective of solving feature-based localization problems is to estimate the path of the robot referring to a given map. Thus, it is not surprising that robust estimators such as Smooth Variable Structure Filter (SVSF) are often used to handle this problem. Basically, its use is highly dependent on an accurate system model and known statistical noise. Where neither of these are available by definition. Therefore, the conventional way is not recommended and the use of an adaptive filter approach can be involved. Based on this and although only partially, Innovation Adaptive Estimation (IAE) has been considered to have a positive influence on improving the performance of the estimator. But not infrequently the solutions offered by this approach also lead to divergences due to unmapped dynamic conditions. Moreover, in this proposal, IAE is enhanced by applying Artificial Bee Colony-Tuned Fuzzy Logic. The hope is that there is quality control for the process noise covariance Q and R measurements by updating them based on the output of this ABC-Tuned FLC.
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 Bidirectional-RRT*-Connect-Assisted RRT*-Smart for a path planning algorithm Suwoyo, Heru; Hastomi, Yudi; Andika, Julpri
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.017

Abstract

Although Rapidly Exploring Random Tree Star (RRT*) has been considered to be able to achieve convergence to an optimal solution, this method has a slow convergence speed and requires an infinite amount of time to produce a truly optimal solution. For this reason, RRT*-Smart which includes path optimization and intelligent sampling processes was introduced. Although the addition of these methods can directly complete infinite-duration RRT* searches, they will work once the initial path obtained with RRT* is available. The effectiveness of reducing the optimality time is determined by the initial path formed. If this path is not close to optimal, the path optimization and intelligent sampling process will take a long time, and vice-versa. For this reason, RRT*-Connect, which has the advantage of searching from two directions, is proposed in this study. The goal is to replace the RRT* algorithm to produce a more optimal initial formed path. Based on this approach, this method will be named Connect-RRT*-Smart. Several methods, such as RRT, RRT*, RRT*-Connect, and RRT*-Smart, are compared to see their performance in producing the feasible path. Regarding this comparative result, the proposed method shows better performance in terms of convergence speed and path optimality. 
Design of path planning robot simulator by applying sampling based method Suwoyo, Heru; Andika, Julpri; Adriansyah, Andi
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.016

Abstract

This research aims to create a simulator for solving the global path planning of mobile robots. Various sampling-based methods such as Rapidly-exploring Random Tree (RRT), RRT*, and Fast-RRT, along with other derivative algorithms, have been widely used to solve path-planning problems in mobile robots. The level of computational efficiency, path optimality, and the ability to adapt to variant environments are some of the issues that still arise, although these techniques have shown good results in many cases. Although the existing solutions are innovative, comparison between the existing methods is still difficult due to significant differences in convergence speed, implementation complexity, and quality of the resulting paths. This makes choosing the most suitable method for a particular application difficult. The simulator uses sampling-based path planning algorithms such as RRT*, Fast RRT*, RRT*-Smart, informed-RRT*, and Honey Bee Mating Optimization-based Fast-RRT*. With this simulator, users can easily compare the performance of each algorithm and see the characteristics and efficiency of each algorithm in various situations. By running all methods through this simulator, the user can easily compare the methods based on convergence speed and optimality. Therefore, it will effectively help users understand robot navigation, improve the quality of learning, and promote the development of path-planning technology for mobile robots.
Co-Authors Abdurohman Abdurohman Adriansyah, Andi Akhmad Wahyu Dani, Akhmad Wahyu Alfiani, Raudah Andi Adriansyah Andi Adriansyah Andi Adriansyah Andi Adriansyah Angela Dirman Arrais, Muchammad Taufiqi Burhanudin, Achmad Chenwei Deng Deng, Chenwei Dinata, Rizky Fadli Sirait Fahrudin Fahrudin Febriandirza, Arafat Fengfeng Xi Ferryawan Harris Kristanto Fina Supegina Guangjie Yuan Hastomi, Yudi Hossain, Md Musabbir Hutomo, Fajri Rezki Indriyanti, Prastika Jibran, Alwan Julpri Andika Ketty Siti Salamah Kusuma, Hiero Kusuma, Prima Wijaya Lestari, Reza Ayu Li, Long Long Li Long Li Mahardika, Erlintang Md Musabbir Hossain Muhammad Hafizd Ibnu Hajar Muhammad Ilham Muhammad Yusuf Nazori AZ Putra, Firoos Safana Rachmat Muwardi Shamsudin, Abu Ubaidah Taufik Hidayat Taufikurohman, Nur Aziz Thong, Zhou Tian, Yingzhong Tian, Yingzhong Tong Zhou Trie Maya Kadarina Triwidya Mochtar, Thathit Gumilar Triyanto Pangaribowo Wang, Wenbin Wenbin Wang Wenbin Wang Winiyoga, January Dwidasa Yifan Li Yingzhong Tian Yingzhong Tian Yingzhong Tian Yingzhong Tian Yingzhong Tian Yudhi Gunardi Yudhi Gunardi Yuliza Yuliza Zendi Iklima Zhou Thong