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Selected Papers from Advanced Robotics and Intelligent Systems 2021

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 43899

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Special Issue Editors

Prof. Dr. Jen-Yuan (James) Chang

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Guest Editor

Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
Interests: dynamic systems and control; robotics (AI, soft, bionic, medical, collaborative, and assistive); smart machinery and manufacturing; mechatronics; magnetic recording; data storage systems
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Prof. Dr. Po Ting Lin

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Guest Editor

Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
Interests: design optimization; machine vision; industrial robots; human-robot collaboration; artificial intelligence
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Prof. Dr. Chun-Yeon Lin

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Guest Editor

Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan
Interests: electromagnetic sensor; electrical impedance sensing system; mechatronics
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Special Issue Information

Dear Colleagues,

This Special Issue is focused on post-conference full journal paper publications for technical and research work presented at the 9th International Conference on Advanced Robotics and Intelligent Systems (ARIS 2021). We would like to invite authors of the conference’s best papers and authors who presented novel and original research and developments in the conference to contribute to this Special Issue to disseminate and share the recent progresses and research and development results in the general areas in the fields of advanced robotics and intelligent systems. Potential topics include but are not limited to the following items:

Applications of robotics and industry 4.0;
Brain–computer interface and computational motor control;
Human–machine interaction;
Intelligent autonomous mobile robots;
Optimization and sensing;
Robotic arm and joint modeling, control, and sensing;
Robotic athlete;
Robotics and mechatronics;
Systematic refinements in automation;
Smart machine and manufacturing;
Smart systems and its applications;
Tech-enabled services and applications of intelligent systems.

Prof. Dr. Jen-Yuan (James) Chang
Dr. Po Ting Lin
Dr. Chun-Yeon Lin
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

robotics
intelligent systems
smart machine
sensor
actuator
human–machine interaction
AI

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Published Papers (10 papers)

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Research

34 pages, 32271 KB

Open AccessArticle

Attitude and Altitude Control Design and Implementation of Quadrotor Using NI myRIO

by Jun-Yao Hong, Po-Jui Chiu, Chun-Da Pong and Chen-Yang Lan

Electronics 2023, 12(7), 1526; https://doi.org/10.3390/electronics12071526 - 23 Mar 2023

Cited by 4 | Viewed by 4502

Abstract

Multi-rotor vehicles have demonstrated great potential in many applications, from goods delivery to military service. Its simple structure has drawn much attention in control research, with various feedback control design methods being tested on it. In this study, a quadrotor is constructed with National Instrument (NI) myRIO for its flight controller. Linear controllers are synthesized with a simple model and with a more detailed model, which also considers the actuator dynamic and system time delay, to exploit the limitations and trade-offs posted by hardware and its influence on linear control design and implementation. The simulation results match the real response better if the detailed model is used in the control design. In addition, the linear–quadratic–Gaussian (LQG) control provides the best response in the control design in this study. The constraints posted by the actuator and time delay are clearly observed in the control synthesis process and the experiment result. These constraints also led to poor control performance or even instability if not considered in the control implementation in advance. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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14 pages, 4467 KB

Open AccessArticle

Implementation and Evaluation of 5G MEC-Enabled Smart Factory

by Nadhif Muhammad Rekoputra, Chia-Wei Tseng, Jui-Tang Wang, Shu-Hao Liang, Ray-Guang Cheng, Yueh-Feng Li and Wen-Hao Yang

Electronics 2023, 12(6), 1310; https://doi.org/10.3390/electronics12061310 - 9 Mar 2023

Cited by 9 | Viewed by 4229

Abstract

A 5G network can provide more comprehensive bandwidth connectivity for the industry 4.0 environment, which requires faster and tremendous data transmission. This study demonstrates the 5G network performance evaluation with MEC, without MEC, WiFi 6, and Ethernet networks. Usually, a 5G network engages with Multi-access Edge Computing, providing the computing functions dedicated to the users on edge nodes. The MEC network architecture presents significant facilities, a network schematic, and data transmission routers. The field test performs high-definition streaming video and heavy-traffic load testing to evaluate the performance based on different protocols by comparing throughput, latency, jitter, and packet loss rate. MEC network performance, streaming video performance, and load test evaluation results reveal that the 5G network working with MEC achieved better performance than when it was working without MEC. The MEC can improve data transmission efficiency by dedicated configuration but is only accessible with authentication from mobile network operators (MNOs). Therefore, MNOs should offer industrial private network users partial authentication for accessing MEC functionality to improve network feasibility and efficiency. In conclusion, this work illustrates the 5G network implementation and performance measurement for constructing a smart factory. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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15 pages, 1738 KB

Open AccessArticle

PLC Cybersecurity Test Platform Establishment and Cyberattack Practice

by Ramiro Ramirez, Chun-Kai Chang and Shu-Hao Liang

Electronics 2023, 12(5), 1195; https://doi.org/10.3390/electronics12051195 - 1 Mar 2023

Cited by 12 | Viewed by 6736

Abstract

Programming logic controllers (PLCs) are vital components for conveyors in production lines, and the sensors and actuators controlled underneath the PLCs represent critical points in the manufacturing process. Attacks targeting the exploitation of PLC vulnerabilities have been on the rise recently. In this study, a PLC test platform aims to analyze the vulnerabilities of a typical industrial setup and perform cyberattack exercises to review the system cybersecurity challenges. The PLC test platform is a sorting machine consisting of an automatic conveyor belt, two Mitsubishi FX5U-32M PLCs, and accessories for material sorting, and Modbus is the selected protocol for data communication. The O.S. on the attacker is Kali ver. 2022.3, runs Nmap and Metasploit to exploit the target Modbus registers. On the other hand, the target host runs the O.S., Ubuntu 22.04 in the cyberattack exercises. The selected attack method for this study is packet reply which can halt operations sending custom data packets to the PLC. In summary, this study provides a basic step-by-step offensive strategy targeting register modification, and the testbed represents a typical industrial environment and its vulnerabilities against cyberattacks with common open-source tools. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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18 pages, 8783 KB

Open AccessArticle

Indoor Localization Method for a Mobile Robot Using LiDAR and a Dual AprilTag

by Yuan-Heng Huang and Chin-Te Lin

Electronics 2023, 12(4), 1023; https://doi.org/10.3390/electronics12041023 - 18 Feb 2023

Cited by 20 | Viewed by 5887

Abstract

Global localization is one of the important issues for mobile robots to achieve indoor navigation. Nowadays, most mobile robots rely on light detection and ranging (LiDAR) and adaptive Monte Carlo localization (AMCL) to realize their localization and navigation. However, the reliability and performance of global localization only using LiDAR are restricted due to the monotonous sensing feature. This study proposes a global localization approach to improve mobile robot global localization using LiDAR and a dual AprilTag. Firstly, the spatial coordinate system constructed with two neighboring AprilTags is applied as the reference basis for global localization. Then, the robot pose can be estimated by generating precise initial particle distribution for AMCL based on the relative tag positions. Finally, in pose tracking, the count and distribution of AMCL particles, evaluating the certainty of localization, is continuously monitored to update the real-time position of the robot. The contributions of this study are listed as follows. (1) Compared to the localization method only using LiDAR, the proposed method can locate the robot’s position with a few iterations and less computer power consumption. (2) The failure localization issues due to the many similar indoor features can be solved. (3) The error of the global localization can be limited to an acceptable range compared to the result using a single tag. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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17 pages, 4846 KB

Open AccessArticle

Applying a Neural Network to Predict Surface Roughness and Machining Accuracy in the Milling of SUS304

by Ming-Hsu Tsai, Jeng-Nan Lee, Hung-Da Tsai, Ming-Jhang Shie, Tai-Lin Hsu and Hung-Shyong Chen

Electronics 2023, 12(4), 981; https://doi.org/10.3390/electronics12040981 - 16 Feb 2023

Cited by 10 | Viewed by 3624

Abstract

Surface roughness and machining accuracy are essential indicators of the quality of parts in milling. With recent advancements in sensor technology and data processing, the cutting force signals collected during the machining process can be used for the prediction and determination of the machining quality. Deep-learning-based artificial neural networks (ANNs) can process large sets of signal data and can make predictions according to the extracted data features. During the final stage of the milling process of SUS304 stainless steel, we selected the cutting speed, feed per tooth, axial depth of cut, and radial depth of cut as the experimental parameters to synchronously measure the cutting force signals with a sensory tool holder. The signals were preprocessed for feature extraction using a Fourier transform technique. Subsequently, three different ANNs (a deep neural network, a convolutional neural network, and a long short-term memory network) were applied for training in order to predict the machining quality under different cutting conditions. Two training methods, namely whole-data training and training by data classification, were adopted. We compared the predictive accuracy and efficiency of the training process of these three models based on the same training data. The training results and the measurements after machining indicated that in predicting the surface roughness based on the feed per tooth classification, all the models had a percentage error within 10%. However, the convolutional neural network (CNN) and long short-term memory (LSTM) models had a percentage error of 20% based on the whole-data training, while that of the deep neural network (DNN) model was over 50%. The percentage error for the machining accuracy prediction based on the whole-data training of the DNN and CNN models was below 10%, while that of the LSTM model was as large as 20%. However, there was no significant improvement in the results of the classification training. In all the training processes, the CNN model had the best analytical efficiency, followed by the LSTM model. The DNN model performed the worst. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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20 pages, 7361 KB

Open AccessArticle

Investigation of Machine Learning Methods for Predictive Maintenance of the Ultra-High-Pressure Reactor in a Polyethylene-Vinyl Acetate Production Process

by Shih-Jie Pan, Meng-Lin Tsai, Cheng-Liang Chen, Po Ting Lin and Hao-Yeh Lee

Electronics 2023, 12(3), 580; https://doi.org/10.3390/electronics12030580 - 24 Jan 2023

Cited by 3 | Viewed by 2811

Abstract

Ethylene-Vinyl Acetate (EVA) copolymer was synthesized from ethylene and vinyl acetate at high temperatures and ultra-high pressures. In this condition, any reactor disturbances, such as process or mechanical faults, may trigger the run-away decomposition reaction. This paper proposes a procedure for constructing a conditional health status prediction structure that uses a virtual health index (HI) to monitor the reactor bearing’s remaining useful life (RUL). The piecewise linear remaining useful life (PL-RUL) model was constructed by machine learning regression methods trained on the vibration and distributed control system (DCS) datasets. This process consists of using Welch’s power spectrum density transformation and machine learning regression methods to fit the PL-RUL model, following a health status construction process. In this research, we search for and determine the optimum value for the remaining useful life period (T_RUL), a key parameter for the PL-RUL model for the system, as 70 days. This paper uses four-fold cross-validation to evaluate seven different regression algorithms and concludes that the Extremely randomized trees (ERTs) is the best machine learning model for predicting PL-RUL, with an average relative absolute error (RAE) of 0.307 and a Linearity of 15.064. The Gini importance of the ensemble trees is used to identify the critical frequency bands and prepare them for additional dimensionality reduction. Compared to two frequency band selection techniques, the RAE and Linearity prediction results can be further improved to 0.22 and 8.38. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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24 pages, 8413 KB

Open AccessArticle

Establishing a Real-Time Multi-Step Ahead Forecasting Model of Unbalance Fault in a Rotor-Bearing System

by Banalata Bera, Chun-Ling Lin, Shyh-Chin Huang, Jin-Wei Liang and Po Ting Lin

Electronics 2023, 12(2), 312; https://doi.org/10.3390/electronics12020312 - 7 Jan 2023

Cited by 8 | Viewed by 2746

Abstract

Recently, prognostics and health management (PHM) has garnered a lot of attention in the industrial sector for its cost-effective maintenance and safe operation of the system. In this regard, vibration-based predictive maintenance using sensors plays a significant role in the diagnosis and prognosis of various faults. The need of the hour is to know when and which part must be replaced in advance for efficient and reliable operation. Unbalance is one major fault acting on any rotary system leading to excessive vibration and causing various other faults developing early failure in components directly or indirectly. In this paper, we show how a prognostic model can be built for the identification of future unbalance trend of a rotor-bearing system with the aid of a mathematical model of the system and statistical/machine learning methods. The prognostic model developed is used to forecast the unbalance time-series data of an industrial turbine rotor in real-time which forecasts the month ahead unbalance values. The proposed model is verified for prognostic analysis using datasets from a local plastic company. After careful examination of the results, it is concluded that the proposed model can aid in precisely detecting future system unbalance. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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17 pages, 3474 KB

Open AccessArticle

K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor

by Sathishkumar Subburaj, Chih-Ho Yeh, Brijesh Patel, Tsung-Han Huang, Wei-Song Hung, Ching-Yuan Chang, Yu-Wei Wu and Po Ting Lin

Electronics 2023, 12(1), 210; https://doi.org/10.3390/electronics12010210 - 1 Jan 2023

Cited by 5 | Viewed by 2753

Abstract

Recently, human activity recognition (HAR) techniques have made remarkable developments in the field of machine learning. In this paper, we classify human gestures using data collected from a curved piezoelectric sensor, including elbow movement, wrist turning, wrist bending, coughing, and neck bending. The classification process relies on data collected from a sensor. Machine learning algorithms enabled with K-mer are developed and optimized to perform human gesture recognition (HGR) from the acquired data to achieve the best results. Three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN), are performed and analyzed with K-mer. The input parameters such as subsequence length (K), number of cuts, penalty parameter (C), number of trees (n_estimators), maximum depth of the tree (max_depth), and nearest neighbors (k) for the three machine learning algorithms are modified and analyzed for classification accuracy. The proposed model was evaluated using its accuracy percentage, recall score, precision score, and F-score value. We achieve promising results with accuracy of 94.11 ± 0.3%, 97.18 ± 0.4%, and 96.90 ± 0.5% for SVM, RF, and k-NN, respectively. The execution time to run the program with optimal parameters is 19.395 ± 1 s, 5.941 ± 1 s, and 3.832 ± 1 s for SVM, RF, and k-NN, respectively. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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13 pages, 2513 KB

Open AccessArticle

Contouring Control of a Five-Axis Machine Tool with Equivalent Errors

by Shyh-Leh Chen, Mun-Hooi Khong and Sheng-Min Hsieh

Electronics 2022, 11(16), 2521; https://doi.org/10.3390/electronics11162521 - 12 Aug 2022

Cited by 4 | Viewed by 2823

Abstract

In this study, the contouring control problem of a five-axis machine tool was examined. Due to the rotation axes, there are two coordinate systems involved in the five-axis machine tool, namely the workpiece coordinate system and machine coordinate system. The five-axis machine tool is required to follow a given desired path with tool orientation specified in the workpiece coordinate system. However, the system dynamics of the machine tool is described in the machine coordinates. Kinematic transformations exist between the two coordinate systems. One challenge of the problem is to design a controller in the machine coordinate system that will meet the requirements in the workpiece coordinate system. Another challenge is to minimize both the position contour error and tool orientation error. The method of equivalent errors is adopted to design the contouring controller. The desired path and tool orientation in the workpiece coordinate system are transformed into a five-dimensional hyper-curve in the machine coordinate system. A contouring controller was designed to follow the five-dimensional hyper-curve using the method of equivalent errors. Both numerical and experimental results validate the effectiveness of the proposed contouring control method. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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19 pages, 51501 KB

Open AccessArticle

Development of a Grinding Tool with Contact-Force Control Capability

by Yu-Heng Lin, Ming-Wei Liu and Pei-Chun Lin

Electronics 2022, 11(5), 685; https://doi.org/10.3390/electronics11050685 - 23 Feb 2022

Cited by 5 | Viewed by 5370

Abstract

The grinding normal force is one of the main factors that affect grinding quality. We report on the development of a compact grinding tool which not only can rotate the grind wheel but also can actively control the wheel in the fore/aft direction to modulate the contact force. The two motion degrees of freedom are coupled in the sense of mechanism yet their motions are independent. The designed grinding tool was modeled and its parameters were systemically identified. A repetitive controller was designed to suppress periodic force variation, which was mainly caused by the rotational motion of the grinding wheel, and the results were compared with other control methods. The selection of the grinding parameters was conducted using the Taguchi method. Finally, the grinding tool and the repetitive controller were experimentally evaluated, and the experimental results confirm that the proposed control strategy can reduce the force disturbance and improve the surface quality of the flat and curvy workpieces. Full article

(This article belongs to the Special Issue Selected Papers from Advanced Robotics and Intelligent Systems 2021)

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