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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer International Journal of Engineering Continuity Journal of Embedded Systems, Security and Intelligent Systems

Yingzhong Tian

Shanghai University

Author-ID : 2458459

Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 5 Documents Claim Missing Document

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Articles

Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm Heru Suwoyo; Yingzhong Tian; Wenbin Wang; Long Li; Andi Adriansyah; Fengfeng Xi; Guangjie Yuan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.16223

The smooth variable structure filter (ASVSF) has been relatively considered as a new robust predictor-corrector method for estimating the state. In order to effectively utilize it, an SVSF requires the accurate system model, and exact prior knowledge includes both the process and measurement noise statistic. Unfortunately, the system model is always inaccurate because of some considerations avoided at the beginning. Moreover, the small addictive noises are partially known or even unknown. Of course, this limitation can degrade the performance of SVSF or also lead to divergence condition. For this reason, it is proposed through this paper an adaptive smooth variable structure filter (ASVSF) by conditioning the probability density function of a measurementto the unknown parameters at one iteration. This proposed method is assumed to accomplish the localization and direct point-based observation task of a wheeled mobile robot, TurtleBot2. Finally, by realistically simulating it and comparing to a conventional method, the proposed method has been showing a better accuracy and stability in term of root mean square error (RMSE) of the estimated map coordinate (EMC) and estimated path coordinate (EPC).

Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller Heru Suwoyo; Yingzhong Tian; Chenwei Deng; Andi Adriansyah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

A wall-following robot needs a robust controller that navigate robot based on the specified distance from the wall. The usage of PID controller has been successfully minimizing the dynamic error of wall-following robot. However, a manual setting of three unknown parameters of PID-controller often precisely increase instability. Hence, recently there are many approaches to solve this issue. This paper presents an approach to obtaining those PID parameters automatically by utilizing the role of Genetic Algorithm. The proposed method was simulated using MATLAB and tested in a real robot. Based on several experiments results it has been showing the effectiveness of reducing the dynamic error of the wall-following robot.

THE ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT Heru Suwoyo; Zhou Thong; Yingzhong Tian; Andi Adriansyah; Muhammad Hafizd Ibnu Hajar
TEKNOKOM Vol. 5 No. 1 (2022): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

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.

The Role of Block Particles Swarm Optimization to Enhance The PID-WFR Algorithm Heru Suwoyo; Abdurohman Abdurohman; Yifan Li; Andi Adriansyah; Yingzhong Tian; Muhammad Hafizd Ibnu Hajar
International Journal of Engineering Continuity Vol. 1 No. 1 (2022): ijec
Publisher : Sultan Publisher

In the conventional Proportional Integral Derivation (PID) controller, the parameters are often adjusted according to the formulas and actual application. However, this empirical method will bring two disadvantages. First, testing the program takes much time and usually needs help to reach the optimal solution. Second, the PID parameters will not adapt to the new environment when the situation changes. This paper proposed a method by employing a Block Particles Swarm Optimization (BPSO) to enhance the conventional Proportional Integral Derivation (PID) algorithm to overcome the mentioned disadvantages. The genetic algorithm (GA) first optimized the PID parameters. However, its optimization time is relatively long. Then, a Block Particle Swarm Optimization (BPSO) algorithm is designed to solve the problem of long optimization time. This method was then applied to the wall-following robot problem by realistically simulating it to confirm the performance. After Compared with conventional methods, the proposed method shows a relatively stable solution.

An Enhanced Informed-RRT* Integrating Bidirectional Exploration, Fast Sampling, and Local Path Optimization Heru Suwoyo; Taufik Hidayat; Yingzhong Tian; Julpri Andika; Andi Adriansyah
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.12223

Purpose – This study aims to improve the efficiency and path quality of Informed-RRT* for mobile robot path planning in complex environments by integrating bidirectional exploration, fast sampling, and local path optimization. Design/methods/approach – The proposed method follows a two-stage framework. In the initial exploration phase, two search trees are expanded bidirectionally using fast sampling to accelerate feasible path discovery. An ancestor-based parent selection mechanism based on the triangular inequality principle is then applied to improve local path quality and reduce inefficient rewiring. After an initial path is found, the algorithm switches to an informed optimization phase using ellipsoidal sampling while maintaining simultaneous two-tree expansion. The proposed method was evaluated in three two-dimensional static environments with different levels of complexity and compared with Fast-RRT* and Informed-RRT*. Performance was assessed using path cost, computation time, number of iterations, and Wilcoxon signed-rank testing. Findings – The results show that the proposed method consistently achieves lower path cost, shorter computation time, and fewer iterations than the baseline methods. Across simple to maze environments, the method reduces computation time by approximately 24–75% and improves path optimality by approximately 3.9–4.8%. Statistical testing confirms significant improvements in more constrained and maze-like environments. Research implications/limitations – The study demonstrates that combining accelerated exploration and local optimization can improve sampling-based path planning performance. However, the evaluation is limited to two-dimensional static environments and selected baseline methods. Originality/value – The proposed framework provides an enhanced Informed-RRT* approach that jointly addresses initial exploration efficiency, local path refinement, and informed optimization within a unified planning strategy.

Co-Authors Abdurohman Abdurohman Andi Adriansyah Chenwei Deng Fengfeng Xi Guangjie Yuan Julpri Andika Long Li Muhammad Hafizd Ibnu Hajar Suwoyo, Heru Taufik Hidayat Wenbin Wang Yifan Li Zhou Thong

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