Journal of Advances in Artificial Life Robotics

Volume 3, Issue 4 March 2023


Research Article
1.A Nonlinear Oscillator Coupling Model to Provide the Hough Transform Function Without the Discrete Voting Procedure
Amarbold Purev, Hiroaki Wagatsuma
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu 808-0196, Japan
pp. 185–192
ABSTRACT
The most common algorithm for detecting straight lines in a digital image is the Hough Transform method. The method involves two consecutive steps: transforming the image edge location onto parameter space and voting procedure to acquire line characteristics. The former deals in continuous space, and the latter counts votes in a discrete matrix, making the algorithm sensitive to noise. We transformed each image pixel location to the parameter space as a Hough line and generated a nonlinear oscillator to attract them into the intersection with other lines. This concept could contribute to a line detection problem in image processing using a consistent mathematical description.

ARTICLE INFO
Article History
Received 25 November 2022
Accepted 26 April 2023

Keywords
Hough transform
Line detection
Nonlinear dynamics
Coupled nonlinear oscillators
Kuramoto model

JAALR3401

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Research Article
2.Exploring the Research Landscape of Automated Emotion Recognition System Adoption in Malaysia: A Systematic Literature Review
Muhammad Nadzree Mohd Yamin¹, Kamarulzaman Ab. Aziz¹, Tan Gek Siang¹, Nor Azlina Ab. Aziz^2
1Faculty of Business, Multimedia University, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka
²Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka
pp. 193–204
ABSTRACT
Artificial intelligence (AI) has revolutionized the way we interact with technology, and one of its key subcategories is the automated emotion recognition system (ERS). ERS is a technology that enables AI to recognize human emotional states, and it has the potential to transform human-computer interaction (HCI) as we move towards the fifth industrial revolution (IR 5.0). ERS has significant implications for various sectors, including healthcare, education, and the workplace. For example, in healthcare, ERS can be used to identify and treat mental health disorders, while in the workplace, it can be used to monitor employee well-being and improve productivity. However, the adoption of ERS is crucial for its success, and there is a need to understand the factors that affect its adoption. This paper aims to explore the landscape of ERS adoption in Malaysia by conducting a systematic literature review of studies published over the past decade between 2011 and 2022. The review will focus on understanding the current levels of adoption of ERS in Malaysia, identifying the factors that hinder or facilitate its adoption, and discussing the potential benefits and drawbacks of using ERS in different contexts. The findings of this review will provide insights into the current ERS research landscape in Malaysia and inform future research on the adoption and use of ERS in the country.

ARTICLE INFO
Article History
Received 17 November 2022
Accepted 01 June 2023

Keywords
Emotion recognition system (ERS)
Artificial intelligence (AI)
Affective computing (AC)s
The fourth industrial revolution (IR 4.0)
The fifth industrial revolution (IR 5.0)
Technology adoption

JAALR3402

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Research Article
3.Utilizing Blockchain to Monitor the Functioning of Devices in Industrial Control Systems
I-Hsien Liu¹, Chien-Hsin Wu¹, Jung-Shian Li¹, Chu-Fen Li^2
1Department of Electrical Engineering / Institute of Computer and Communication Engineering, National Cheng Kung University, No.1, University Rd., East Dist., Tainan City 701401, Taiwan
²Department of Finance, National Formosa University, No.64, Wunhua Rd., Huwei Township, Yunlin County 632301, Taiwan
pp. 205–208
ABSTRACT
Programmable logic controllers, or PLCs for short, is ubiquitous in the field of industrial automation. These devices are used to control and monitor a variety of processes related to production, ensuring that everything runs smoothly and efficiently. Unfortunately, there are currently no reliable means of verifying the accuracy and reliability of these devices, which can lead to serious consequences in the event of an error or malfunction. To address this issue, researchers are exploring the use of advanced technologies such as blockchain to provide greater transparency and security PLC operations.

ARTICLE INFO
Article History
Received 08 October 2022
Accepted 04 May 2023

Keywords
Cyber security
Blockchain
PLC
ICS security

JAALR3403

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Research Article
4.QoS Balancing Optimization in Aggregated Robot Processing Architecture: Rate and Buffer
Abdul Jalil, Jun Kobayashi, Takeshi Saitoh
Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi, Fukuoka, 820-8502, Japan
pp. 209–213
ABSTRACT
This study developed an optimization algorithm to balance the Quality of Service (QoS) rates and buffer size for robot data communication in the Aggregated Robot Processing (ARP) architecture. Robot Operating System 2 (ROS 2) is robotic software that uses a set of QoS policies to control the quality of data transmission in robotic networks, such as DEADLINE to determine the rates, and DEPTH to determine the buffer size. An unbalanced DEADLINE and DEPTH configuration in ROS 2 node communication can result in high packet latency and packet loss in RELIABLE connections. This happens when the DEADLINE sets the rates at high frequencies, and the DEPTH sets the buffers with small sizes. The results of this study show that the optimization algorithm developed in this study can determine the rate and buffer size through a balancing configuration for better quality robot data transmission in ARP architecture, influence the improvement of latency and reduce the packet loss.

ARTICLE INFO
Article History
Received 25 November 2022
Accepted 30 May 2023

Keywords
QoS
Optimization
Aggregated robot processing
ROS 2

JAALR3404

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Research Article
5.Design of Intelligent Fish Box Based on Machine Vision and Internet of Things Technology
Suqing Duan, Jiangyu Wu, Shuai Chen, Yizhun Peng
College of Electronic Information and Automation, Tianjin University of Science and Technology, China
pp. 214–223
ABSTRACT
The rapid economic development has fueled the demand for enhancing lifestyle and home aesthetics, leading to the growing popularity of leisure activities and home decoration. As a response, the ornamental fish industry has flourished, prompting fish enthusiasts to seek efficient ways to care for their fish. Smart fish box has emerged as popular solutions, offering features such as remote control and monitoring. Smart fish box incorporates machine vision and Internet of Things technologies, allowing users to remotely control lighting, water changing, feeding, and oxygen pump operations. Temperature sensors transmit data to a mobile app, enabling users to monitor and adjust water temperature. These boxes also features built-in cameras for real-time monitoring and send notifications when fish food is running low. This innovative design addresses several challenges in ornamental fish care. This paper presents the mechanical structure, control circuitry, and vision algorithm of the smart fish box. By utilizing collected data, a neural network is trained on the Raspberry Pi platform, successfully recognizing fish health status.

ARTICLE INFO
Article History
Received 20 November 2022
Accepted 28 June 2023

Keywords
Machine vision
Internet of things
Remote control
Neural network
Machine learning

JAALR3405

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Research Article
6.A real and synthetic dataset for Robotic Vision in Outdoor Beach Environment – BCRobo
Tan Chi Jie¹, Takumi Tomokawa¹, Shintaro Ogawa¹, Ayumu Tominaga², Sakmongkon Chumkamon¹, Eiji Hayashi^1
1Department of Mechanical Information Science and Technology, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka-City, Fukuoka, 820-8502, Japan
²Department of Creative Engineering Robotics and Mechatronics Course, National Institute of Technology Kitakyushu College, 5-20-1 Shii, Kokuraminamiku, Kitakyushu, Fukuoka, 802-0985, Japan
pp. 224–229
ABSTRACT
Datasets are one of the key elements which determine the performance of a deep learning network. Urban environments datasets receive much attention nowadays due to the rise of autonomous cars but off-road environment on the other hand lacks quality datasets. Offroad environments need equal attention as only 55% of the world’s population lives in urban areas. This paper tackles this issue to close the gap of robotic visual perception on the beach, one of the common offroad environments that lack attention by presenting a real and synthetic dataset, namely BCRobo.

ARTICLE INFO
Article History
Received 20 November 2022
Accepted 28 June 2023

Keywords
Synthetic dataset
Computer vision
Real dataset
Field robotics
Image segmentation

JAALR3406

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Research Article
7.Error Recovery Patterns Focusing on the Revival Process from Failures in Manipulation Tasks
Akira Nakamura¹, Kensuke Harada^2
1Department of Information Systems, Faculty of Engineering, Saitama Institute of Technology, 1690 Fusaiji, Fukaya, Saitama 369-0293, Japan
²Robotic Manipulation Research Group, Systems Innovation Department, Graduate School of Engineering Science, Osaka University,
1-3 Machikaneyama, Toyonaka 560-8531, Japan
pp. 230–237
ABSTRACT
In recent years, robots have played an important role in various places, including factories of the manufacturing industry as well as homes where people live. The number of robotic tasks with a high degree of difficulty is increasing because they are required to perform various types of tasks, and failures are likely to occur. Therefore, there is a growing demand for error recovery techniques. We propose an error recovery method that considers task stratification and error classification. This allows various error-recovery paths to be derived for a single error. In this study, recovery paths were classified into several patterns to facilitate selection of the optimal path.

ARTICLE INFO
Article History
Received 21 November 2022
Accepted 22 July 2023

Keywords
Planning
Modeling
Revival process of failure
Manipulation skill

JAALR3407

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Research Article
8.Development of Beach Litter Detection System using Deep Learning on Beach Clean-up
Shintaro Ogawa¹, Tan Chi Jie², Takumi Tomokawa², Sylvain Geiser², Sakmongkon Chumkamon², Ayumu Tominaga³,
Eiji Hayashi^2
1Department of Creative Informatics, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka-city, Fukuoka, 820-8502, Japan
²Department of Mechanical Information Science and Technology, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka-city, Fukuoka, 820-8502, Japan
³Department of Creative Engineering Robotics and Mechatronics Course, National Institute of Technology Kitakyushu Colllege, 5-20-1 Shii, Kokuraminamiku, Kitakyushu-city, Fukuoka, 802-0985, Japan
pp. 238–241
ABSTRACT
This paper proposed a deep learning-based beach litter detector specifically designed for assessing litter levels on beaches effectively. This litter detector was created utilizing a HTC, also known as the Hybrid Task Cascade network, and its performance was compared to that of the traditional mask R-CNN network in order to judge its effectiveness. The findings uncovered that the HTC network possessed heightened sensitivity towards small and tiny litters taken within the RGB colored images.

ARTICLE INFO
Article History
Received 02 December 2022
Accepted 20 July 2023

Keywords
Mask R-CNN
HTC
Field robotics
Object detection
Deep learning
TACO

JAALR3408

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Research Article
9.An Analysis of Robot Speed Efficiency for Mobile Robot Adapted Three Omni Rollers Using Linear Transformation
Kenji Kimura¹, Yuki Shigyo², Kazuo Ishii^3
1Department of Control Engineering, National Institute of Technology, Matsue College, 14-4 Nishi-ikuma-cho, Matsue-shi, Shimane, 690-8518, Japan
²Digital Solution Div, DX Engagement Dept, Fujitsu, 3-5-20 Minamikamata. Ota City, Tokyo 144-0035, Japan
³Graduate School of Life Science and engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu-shi, 808-0196, Fukuoka, Japan
pp. 242–249
ABSTRACT
RoboCup Middle-sized-League soccer robots are mainly equipped with the three omni-roller mechanism. These mobile robots are expected to function efficiently in fields, such as logistics. These systems are easy to control and allow omnidirectional movement. However, theoretical research on these systems’ exercise efficiencies has not been conducted. In this research, we assume a mechanism that can arbitrarily change the roller arrangement based on a circular mechanism and evaluate the roller arrangement from a speed efficiency perspective. Additionally, we define an evaluation function using the theory of linear transformation to examine the roller arrangement.

ARTICLE INFO
Article History
Received 10 November,2022
Accepted 05 July 2023

Keywords
Omni-roller
Transformation matrix
Motion analysis of mobile robot

JAALR3409

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Research Article
10.Proposal and prototype of an IoT self-tuning PI control device using Wi-Fi
Shinichi Imai
Graduate school, Tokyo Gakugei University, 4-1-1, Nukuikita-machi, Koganei, Tokyo, 184-8501, Japan
pp. 250–253
ABSTRACT
In this paper, development of IoT control device using Wi-Fi. In recent years, IoT has been attracting attention, and there are growing expectations in the industrial world for the utilization of data obtained from many sensors. These data are stored in databases in real time through communication between sensors and the cloud and communicating with the cloud. Meanwhile, digital controllers are widely used in the process industry as general-purpose controllers. However, it is difficult to incorporate AI, machine learning, and databases into general-purpose controllers due to data memory limitations. Therefore, in this paper, we develop an IoT self-tuning controller using Wi-Fi. As a result of experiments, the controller and computer were connected via Wi-Fi, self-tuning was performed on the computer side, and the calculated PID gains could be sent to the controller to achieve control.

ARTICLE INFO
Article History
Received 14 October 2022
Accepted 30 July 2023

Keywords
Wi-Fi
IoT
Control
Self-tuning
Prototype

JAALR3410

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