IAES International Journal of Robotics and Automation (IJRA)
Robots are becoming part of people's everyday social lives and will increasingly become so. In future years, robots may become caretaker assistants for the elderly, or academic tutors for our children, or medical assistants, day care assistants, or psychological counselors. Robots may become our co-workers in factories and offices, or maids in our homes. The IAES International Journal of Robotics and Automation (IJRA) is providing a platform to researchers, scientists, engineers and practitioners throughout the world to publish the latest achievement, future challenges and exciting applications of intelligent and autonomous robots. IJRA is aiming to push the frontier of robotics into a new dimension, in which motion and intelligence play equally important roles. Its scope includes (but not limited) to the following: automation control, automation engineering, autonomous robots, biotechnology and robotics, emergence of the thinking machine, forward kinematics, household robots and automation, inverse kinematics, Jacobian and singularities, methods for teaching robots, nanotechnology and robotics (nanobots), orientation matrices, robot controller, robot structure and workspace, robotic and automation software development, robotic exploration, robotic surgery, robotic surgical procedures, robotic welding, robotics applications, robotics programming, robotics technologies, robots society and ethics, software and hardware designing for robots, spatial transformations, trajectory generation, unmanned (robotic) vehicles, etc.
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<![CDATA[Potential challenges of collaborative robot implementation in Vietnamese garment manufacturing ]]>
<![CDATA[Nguyen, Kim Phung]]>;
<![CDATA[Ma, Yoon Jin]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp283-292
<![CDATA[Collaborative robots (cobots) are a new significant technology in the integration of Industry 4.0 (I4.0) and Industry 5.0 (I5.0). Working alongside humans in open environments, cobots can boost safety and productivity. However, manufacturers are facing some potential challenges in adopting cobots, such as technological challenges, social problems, cost barriers, and labor issues. Vietnam has a great potential for outsourcing in the top supply chains for many famous fashion brands globally, with thousands of textile and garment factories. The purpose of this study was to explore potential challenges of cobot implementations in the context of Vietnam’s garment factories from factory employees’ perspectives. Data were collected from 29 garment factory managers in Vietnam. Findings revealed a rapid change in fashion trends and many unskilled workers may limit cobots’ flexibility, precision, and innovation. Furthermore, cobot implementation is affected by the cost of cobots, infrastructure upgrades, and risks of possible failure in deployment. Cobot firms, application partners, technology programmers, and manufacturers need to discuss how to maximize cobots’ benefits in diverse aspects of the garment manufacturing setting. These insights could boost the industry’s economy and sustainability.]]>
<![CDATA[Vector synthesis of fault testing map for logic ]]>
<![CDATA[Hahanov, Vladimir]]>;
<![CDATA[Gharibi, Wajeb]]>;
<![CDATA[Chumachenko, Svetlana]]>;
<![CDATA[Litvinova, Eugenia]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp293-306
<![CDATA[Vector synthesis of fault testing (simulation) map for logic is proposed, which without simulation allows to determine of all faults detected on test sets, as well as determining test sets to detect specified faults. For synthesis, a superposition of smart data structures is used, containing: a deductive matrix D, as a derivative of the logical vector L, test truth table T, and fault truth table F. The deductive matrix is seen as the gene of functionality and base of fault simulation mechanism to solve all the problems of testing and verification. In the matrix synthesis, an axiom is used: all the mentioned tables are identical in shape to each other and always interact with each other convolutionally T⊕L⊕F=0. A universal deductive reversing converter “test-faults” and “faults-test” for logical functionalities of any dimension is proposed. Converter functions: fault simulation on test sets T→F and synthesis of test sets F→T to detect the specified faults. The converter can be used as a test generation and fault simulation service for IP-core system-on-chip (SoC) under the IEEE 1500 SECT standard. Based on the deductive matrix, a fault testing map for logic is built, where each test set is matched with the logic-detected faults of the input lines.]]>
<![CDATA[Agricultural path detection systems using Canny-edge detection and Hough transform ]]>
<![CDATA[Sasmita, Windi Antania]]>;
<![CDATA[Mubarok, Husni]]>;
<![CDATA[Widiyasono, Nur]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp247-254
<![CDATA[Navigation is one of the crucial aspects of automation technology within the field of agriculture, such as robotics systems or autonomous agricultural vehicles. Despite many navigation systems having been developed for agricultural land, due to their high development and component costs, these systems are difficult to access for farmers or organizations with limited capital. In this study, the Canny-edge detection and Hough transform methods are implemented in a path detection system on agricultural land to find an alternative, cost-effective navigation system for autonomous farming robots or vehicles. The system is tested on ground-level view images, which are captured from a low perspective and under three different lighting conditions. The testing and experimentation process involves adjusting the parameters of the Canny-edge detection and Hough transform methods for different lighting conditions. Subsequently, an evaluation is conducted using Intersection over Union to obtain the best accuracy results, followed by fine-tuning of the canny-edge detection and Hough transform method parameters. The identified parameters, specifically a 15×15 Gaussian kernel, low threshold of 50, high threshold of 150, Hough threshold, minimum line length of 150, and maximum line gap, have been discerned as optimal for the canny-edge and Hough transform algorithms under medium lighting conditions (G=1.0). The observed efficacy of these parameter configurations suggests the method’s viability for implementation in path detection systems for agricultural vehicles or robots. This underscores its potential to deliver reliable performance and navigate seamlessly across diverse lighting scenarios within the agricultural context.]]>
<![CDATA[An approach for modern gardening through monitoring and maintenance of plant health ]]>
<![CDATA[Swami, Siddharth]]>;
<![CDATA[Singh, Rajesh]]>;
<![CDATA[Gehlot, Anita]]>;
<![CDATA[Sharma, Sameer Dev]]>;
<![CDATA[Kumar, Dharmendra]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp307-313
<![CDATA[The study explores the use of internet of things (IoT) devices in agriculture to improve sustainable practices and environmental concerns. It uses the ESP8266 microcontroller and the Blynk platform to create a revolutionary plant health monitoring and automated care system. The system is designed to handle continuous monitoring and plant maintenance in various environmental conditions. Sensors measuring light, temperature, humidity, and soil moisture are strategically placed to receive real-time data. The ESP8266 microcontroller analyzes this information and links it to the Blynk cloud for accessibility via mobile or web applications. The system is effective in monitoring ideal growing conditions, such as soil moisture and weather conditions. Automated care elements like irrigation and supplemental lighting have been shown to improve plant growth and health. The study contributes to smart farming by offering an affordable and easy way to automate and monitor plant health, demonstrating how IoT technologies can enhance agricultural practices, conserve resources, and enable remote management of plant ecosystems.]]>
<![CDATA[A hybrid gradient climbing algorithm for a swarm robot-based gas leak detector ]]>
<![CDATA[Adegunsoye, Adeola Erastus]]>;
<![CDATA[Ubochi, Brendan]]>;
<![CDATA[Macaulay, John]]>;
<![CDATA[Akingbade, Kayode Francis]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp255-263
<![CDATA[Methane emissions from leak sources can have a negative climate impact, in addition to contributing to the risk of explosions in urban environments. These risks can be minimized by developing systems that provide for an accurate and timely detection and localization of a gas leakage point. This research used a swarm of robots to detect and locate a leakage point. The localization algorithm derives from further optimization of the gradient climbing algorithm using fireflies acting as opportunistic agents. Firefly agents are characterized by their bioluminescent communication which guides them to dynamically adjust their positions and intensities based on the quality of the gradient information available to them. The proposed research focuses on enhancing gas leak detection through the development of a hybrid gradient climbing algorithm. This algorithm integrates gradient climbing techniques with swarm intelligence, utilizing the strengths of both approaches. This simulation resulted in the hybrid algorithm leading to a reduced convergence time and path lengths when compared to the swarm without opportunistic agents. The suggested approach can be important especially in gas distribution systems or in areas where human intervention is considered to be unsafe.]]>
<![CDATA[Wireless sensor networks protocols, applications, and network-on-chip communications ]]>
<![CDATA[Ompal, Ompal]]>;
<![CDATA[Kumar, Niraj]]>;
<![CDATA[Mishra, Vishnu Mohan]]>;
<![CDATA[Kumar, Adesh]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp338-350
<![CDATA[A wireless sensor network (WSN) is a network consisting of self-governing sensors that are deployed in space and communicate with each other using wireless technology to monitor physical or environmental variables. These networks generally include compact, inexpensive sensor nodes equipped with sensing, processing, and communication functionalities. WSNs are specifically engineered to gather data from their immediate environment, do local data processing, and subsequently communicate pertinent information either to a central hub or to other interconnected nodes within the network. Continuous research in the domain of WSNs is devoted to advancing security concerns, developing novel sensing technologies, and optimizing communication protocols. The advancements in these domains enhance the ongoing development and efficiency of WSNs. The WSNs are very important for getting information from the real world in many situations. WSNs are flexible tools for keeping an eye on and controlling different environments because they have sensor nodes, wireless communication, and distributed processing. WSNs use network-on-chip (NoC) communication architecture to connect sensor nodes. The article explains the introduction to WSN, the background of wireless communication, motivation, ZigBee protocol, and WSN applications.]]>
<![CDATA[The color features and k-nearest neighbor algorithm for classifying betel leaf image ]]>
<![CDATA[Hamdani, Hamdani]]>;
<![CDATA[Septiarini, Anindita]]>;
<![CDATA[Puspitasari, Novianti]]>;
<![CDATA[Tejawati, Andi]]>;
<![CDATA[Alameka, Faza]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp330-337
<![CDATA[Piper betle L. (betel) is a species that belongs to the genus Piper and is a type of medicinal plant that is quite well known to the general population. The varieties of the leaf color may distinguish are red, green, and black betel. However, consumers still need assistance determining the differences between the many types of betel leaf. Therefore, using image processing techniques, this research contributes to building a classification method for distinguishing betel leaves based on color attributes. This approach anoints for the region of interest detection, feature extraction, and classification. In addition, three different classifiers, naïve Bayes, support vector machine, and k-nearest neighbors (k-NN), were used during the classification process. The evaluation for this study used a percentage split to divide a total of 180 images between the training and testing phases. The method’s performance provided the highest accuracy value possible, 100%, by utilizing the color characteristics with the k-NN classifier.]]>
<![CDATA[Inverse kinematics of six degrees of freedom robot manipulator based on improved dung beetle optimizer algorithm ]]>
<![CDATA[Haohao, Ma]]>;
<![CDATA[As’arry, Azizan]]>;
<![CDATA[Haoyang, Zhang]]>;
<![CDATA[Ismail, Mohd Idris Shah]]>;
<![CDATA[Rashidi Ramli, Hafiz]]>;
<![CDATA[Zuhri, Mohd Yusoff Moh]]>;
<![CDATA[Delgoshaei, Aidin]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp272-282
<![CDATA[Inverse kinematics is a basic problem in robotics, which aims to solve the robot’s joint angles according to the end effector’s position and orientation. This paper proposed an improved spiral search multi-strategy dung beetle optimizer (DBO) algorithm for solving the inverse kinematics problem. The improved DBO algorithm considers not only the error between the target value and the current value but also the previous position of the robot to ensure minimum displacement during the movement. To solve the end position error and orientation error of the robot end effector more accurately, the quaternion is introduced as a penalty factor in the optimization objective function, which is of great significance for reducing the orientation error. Through the improved DBO algorithm, the position error is still accurate, and the orientation error is reduced from 9.5901 to 1.8718. Experimental results show that the proposed algorithm outperforms other swarm-intelligent algorithms in terms of accuracy and convergence speed. Overall, the proposed spiral search multi-strategy DBO algorithm provides an effective and efficient solution to the inverse kinematics problem in robotics.]]>
<![CDATA[Switching regulator based on an adaptive DC-DC buck converter for a lithium-ion battery charging interface ]]>
<![CDATA[Rahali, Ahmed]]>;
<![CDATA[El Khadiri, Karim]]>;
<![CDATA[Qjidaa, Hassan]]>;
<![CDATA[Tahiri, Ahmed]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp351-360
<![CDATA[A switching regulator based on an adaptive DC-DC buck converter for a Li-ion battery charging interface is introduced in this paper with the aim of improving the efficiency of charging the Li-ion battery during the whole charging process. By using the battery voltage as feedback, an adaptive reference is generated. This reference is employed by the converter, which is in continuous conduction mode (CCM), to produce a wide adaptive output voltage that closely tracks the battery voltage, intended to serve as the power source for the multimode charging interface. The converter was implemented in a 180 nm complementary metal oxide semiconductor (CMOS) process and simulated using the Cadence Virtuoso tool. With an input voltage of 5 V and a switching frequency selected at 500 kHz, the simulation results show that the converter produces different charging currents for each battery charging mode, and an adaptive output voltage ranging from 2.8 V to 4.38 V, with the current ripple of 38 mA in CC mode and voltage ripple factor less than 1% in constant voltage (CV) mode. The average converter efficiency is 83.5%.]]>
<![CDATA[Development of robotic arm control using Arduino controller ]]>
<![CDATA[Chenchireddy, Kalagotla]]>;
<![CDATA[Dora, Radhika]]>;
<![CDATA[Mulla, Gouse Basha]]>;
<![CDATA[Jegathesan, Varghese]]>;
<![CDATA[Sydu, Shabbier Ahmed]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v13i3.pp264-271
<![CDATA[The advance of Arduino-based technology has spurred innovation in the realm of robotic arm control, offering a cost-effective and accessible platform for enthusiasts and professionals alike. This paper presents the development of robot arm control using an Arduino controller. The work involves the integration of Arduino microcontrollers and sensors to enable precise and dynamic control of a robotic arm. The proposed robot is controlled by 4 servo motors, the motors rotate left, right, front, and back. The paper discusses the challenges encountered during the development process and proposes solutions, paving the way for further advancements in this burgeoning field. With Arduino's widespread availability and affordability, the presented robotic arm control system holds promise for expanding the accessibility of robotics education and fostering innovation in automation technologies. This paper provides a glimpse into the promising synergy between Arduino and robotic arm control, highlighting the contributions and implications of this technology in shaping the future of automation.]]>
