Search Results (53)

This study presents the design and implementation of a low-cost, open-source, 3D-printed drone platform for university-level STEM education in mechatronics, robotics, control theory, and artificial intelligence. The platform addresses key limitations of existing educational drones, such as high cost, the proprietary nature of systems, and limited customizability, by integrating accessible materials, Arduino-compatible microcontrollers, and modular design principles, with all design files and instructional materials openly available. This work introduces technical improvements, including enhanced safety features and greater modularity, alongside pedagogical advancements such as structured lesson plans, a workflow bridging simulation, and hardware implementation. Educational impact was evaluated through a case study in a postgraduate course with 39 students participating in project-based activities involving 3D modeling, electronics integration, programming, and flight testing. Data collected via a Technology Acceptance Model-based survey and researcher observations showed high student engagement and satisfaction, with average scores of 4.49/5 for overall experience, 4.31/5 for perceived usefulness, and 4.38/5 for intention to use the drone in future activities. These results suggest the platform is a practical and innovative teaching tool for academic settings. Future work will extend its educational evaluation and application across broader contexts. Full article

In this paper, we present a mechatronic platform that must be used for the handling of thermally processed samples using laser equipment, after which it is cooled at low temperatures. In addition to the laser and cryogenic equipment, the mechatronic platform includes one robotic arm (with a new modular structure that allows it to adapt to different working places) for sample transfer between storage areas, a controlling system for the robotic arm based on a new haptic device with physical feedback, a laser system, a cryogenic system, and an optical thermal processing measurement system. A new VR application enables remote control of a robotic arm, ensuring user safety using a haptic device based on the VR model. We exemplified the process of manufacturing the parts for the robotic arm and glove using a 3D printing method. Full article

This paper presents the design and implementation of an XY plotter system for playing Tic-Tac-Toe against a human opponent. The mechatronic system utilizes stepper motors controlled via a microcontroller and a CNC module, enabling precise bidirectional movement. A vision-based algorithm detects user moves and processes game logic through a Minimax strategy for optimal decision-making. The study highlights the integration of robotics and human–computer interaction, demonstrating potential applications in automation, education, and interactive entertainment. Experimental results validate the system’s accuracy and efficiency in real-time gameplay scenarios. Additionally, the work emphasizes the reliability and predictability of a mathematics-based approach—embodied by the deterministic Minimax algorithm—over AI-driven methods, which may involve uncertainties or probabilistic failure. This highlights the advantage of using well-defined algorithmic logic for tasks requiring consistent performance and outcome guarantees. Full article

The integration of Large Language Models (LLMs), particularly Visual Language Models (VLMs), into robotics promises more intuitive human–robot interactions; however, challenges remain in efficiently translating high-level commands into precise physical actions. This paper presents a novel architecture for vision-based object manipulation that leverages a VLM’s reasoning capabilities while incorporating symmetry principles to enhance operational efficiency. Implemented on a Yahboom DOFBOT educational robot with a Jetson Nano platform, our system introduces a prompt-based framework that uniquely embeds symmetry-related cues to streamline feature extraction and object detection from visual data. This methodology, which utilizes few-shot learning, enables the VLM to generate more accurate and contextually relevant commands for manipulation tasks by efficiently interpreting the symmetric and asymmetric features of objects. The experimental results in controlled scenarios demonstrate that our symmetry-informed approach significantly improves the robot’s interaction efficiency and decision-making accuracy compared to generic prompting strategies. This work contributes a robust method for integrating fundamental vision principles into modern generative AI workflows for robotics. Furthermore, its implementation on an accessible educational platform shows its potential to simplify complex robotics concepts for engineering education and research. Full article

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

This study presents a case of community-integrated project-based learning (PBL) at a Japanese National Institute of Technology (KOSEN). Three students collaborated to design and build a rideable 5-inch gauge railway system, integrating mechanical design, brushless motor control, and computer vision. The project was showcased at public events and a partner high school, providing authentic feedback and enhancing learning relevance. Over 15 weeks, students engaged in hands-on prototyping, interdisciplinary teamwork, and real-world problem-solving. The course design was grounded in four educational frameworks: experiential learning, situated learning, constructive alignment, and self-regulated learning (SRL). SRL refers to students’ ability to plan, monitor, and reflect on their learning—a key skill for managing complex engineering tasks. A mixed-methods evaluation—including surveys, reflections, classroom observations, and communication logs—revealed significant gains in technical competence, engagement, and learner autonomy. Although limited by a small sample size, the study offers detailed insights into how small-scale, resource-conscious PBL can support meaningful interdisciplinary learning and community involvement. This case illustrates how the KOSEN approach, combining technical education with real-world application, can foster both domain-specific and transferable skills, and provides a model for broader implementation of authentic, student-driven engineering education. Full article

In recent years, the educational field has evolved rapidly owing to the integration of several technologies, especially experiments in remote laboratories in the engineering area. Therefore, this article addresses the development of an innovation system for automatically correcting experiments in remote laboratories in mechatronics using digital twins, convolutional neural networks (CNNs), and generative artificial intelligence technologies. This system was designed to overcome the limitations of physical laboratories and teacher’s availability and assist in learning, enabling automatic acquisitions at any time. The digital twin captures data from the teacher’s and student’s experiments, allowing accurate comparisons to identify successes and errors. The application of CNNs serves to validate the results of the experiments through image analysis, whereas generative AI helps to identify patterns. The system was evaluated in a didactic plant, effectively correcting experiments with digital inputs and outputs. In addition, it provides students with detailed feedback on their performance, including specific errors and suggestions for improvement. With a three-layer architecture, i.e., experiments, didactics, and management, the system efficiently processes data from teachers and students, contributing to correcting experiments and optimizing teaching in remote environments. Full article

This paper presents an innovative approach to teaching mechatronics at the bachelor’s level, using the design and construction of an Automated Guided Vehicle (AGV) as a comprehensive example of a mechatronic system. The course, titled Laboratory of Electronic Systems, is part of a newly established professionalizing bachelor’s degree program at the University of Pisa, focused on techniques for mechanics and production. This program was developed to meet industry demands for technically skilled personnel with an engineering-related background but without the need for a full traditional engineering education. The course is designed to provide students with hands-on experience, integrating fundamental concepts from mechanical, electronic, and control engineering, along with software development. The curriculum emphasizes practical applications rather than theoretical depth, aligning with the program’s goal of preparing students for operational roles in industrial settings. We present the course structure, educational objectives, and the interdisciplinary nature of mechatronics as addressed in this teaching approach. A dedicated section outlines the critical steps involved in the AGV prototype development, highlighting practical challenges and learning opportunities. The effectiveness of the course is assessed through the evaluation of student projects, specifically via a technical report and a final discussion on the design of a mechatronic system. The results demonstrate the value of a project-based learning approach in equipping students with the practical skills and knowledge required for careers in mechatronics and industrial automation. Full article

This paper provides a comprehensive guide for educators on how multirotor UAV platforms can be utilized to achieve various learning outcomes in undergraduate mechatronics education. This study is based on a PX4 ecosystem combined with the MATLAB Simulink programming environment, covering both hardware and software aspects to support engineering education. The paper explains (i) which learning outcomes can be obtained, (ii) how mathematical models can be derived and implemented in simulation software, (iii) which hardware components are essential, their approximate costs, and possible upgrades based on available budgets, and (iv) which experiments students can perform using the UAV platform. A proposed educational prototype integrates airframe parts produced using additive manufacturing technologies with standard multirotor components. Additionally, a series of experiments were designed, including extensive testing of the multirotor control module. Three learning outcomes related to UAV hardware were incorporated into the engineering curriculum, while two software-related outcomes were addressed through student workshops. Future plans include the implementation of multiple UAV platforms in the educational process to further enhance learning outcomes. Full article

Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air quality in the campus, endangering the respiratory and cognitive health of students and staffs. Besides, in universities in Indonesia, automobile emissions of harmful substances such as carbon monoxide (CO), nitrogen dioxide (NO₂), and hydrocarbon (HC) have been a serious problem for a long time. Almost everybody is using a motorbike or a car every day in daily life, while the number of students is continuously increasing. However, people in many campuses including managements do not be aware these problems, since air quality is not monitored. In this paper, we present a real-time air quality monitoring system utilizing Internet of Things (IoT) integrated sensors capable of detecting pollutants and measuring environmental conditions to visualize them. By transmitting data to the SEMAR IoT application server platform via an ESP32 microcontroller, this system provides instant alerts through a web application and Telegram notifications when pollutant levels exceed safe thresholds. For evaluations of the proposed system, we adopted three sensors to measure the levels of CO, NO₂, and HC and conducted experiments in three sites, namely, Mechatronics Laboratory, Power and Emission Laboratory, and Parking Lot, at the State Polytechnic of Malang, Indonesia. Then, the results reveal Good, Unhealthy, and Dangerous for them, respectively, among the five categories defined by the Indonesian government. The system highlighted its ability to monitor air quality fluctuations, trigger warnings of hazardous conditions, and inform the campus community. The correlation of the sensor levels can identify the relationship of each pollutant, which provides insight into the characteristics of pollutants in a particular scenario. Full article

The monitoring and control of an assembly/disassembly/replacement (A/D/R) multifunctional robotic cell (MRC) with the ABB 120 Industrial Robotic Manipulator (IRM), based on IoT (Internet of Things)-cloud, VPN (Virtual Private Network), and digital twin (DT) technology, are presented in this paper. The approach integrates modern principles of smart manufacturing as outlined in Industry/Education 4.0 (automation, data exchange, smart systems, machine learning, and predictive maintenance) and Industry/Education 5.0 (human–robot collaboration, customization, robustness, and sustainability). Artificial intelligence (AI), based on machine learning (ML), enhances system flexibility, productivity, and user-centered collaboration. Several IoT edge devices are engaged, connected to local networks, LAN-Profinet, and LAN-Ethernet and to the Internet via WAN-Ethernet and OPC-UA, for remote and local processing and data acquisition. The system is connected to the Internet via Wireless Area Network (WAN) and allows remote control via the cloud and VPN. IoT dashboards, as human–machine interfaces (HMIs), SCADA (Supervisory Control and Data Acquisition), and OPC-UA (Open Platform Communication-Unified Architecture), facilitate remote monitoring and control of the MRC, as well as the planning and management of A/D/R tasks. The assignment, planning, and execution of A/D/R tasks were carried out using an augmented reality (AR) tool. Synchronized timed Petri nets (STPN) were used as a digital twin akin to a virtual reality (VR) representation of A/D/R MRC operations. This integration of advanced technology into a laboratory mechatronic system, where the devices are organized in a decentralized, multilevel architecture, creates a smart, flexible, and scalable environment that caters to both industrial applications and educational frameworks. Full article

Remote laboratories are essential in addressing access and quality challenges in technical education. They enable students from various locations to engage with real equipment, overcome geographic and economic constraints, and provide solutions during crises, such as pandemics, when in-person learning is limited. As a key element of Education 4.0, remote labs promote technical skill development, enhance engineering education, and support diverse learning approaches. This study presents a remote laboratory based on Field Programmable Gate Arrays (FPGAs), developed using a waterfall methodology integrating IoT and Cloud Computing technologies to facilitate close interaction between hardware and software. The lab focuses on controlling DC, servo, and stepper motors, allowing students to apply theoretical concepts such as digital signals, pulse-width modulation (PWM), and data representation in bits in a practical setting. The testing phase involved 50 robotics and mechatronics engineering students who participated in hands-on sessions for one month, followed by a structured survey evaluating their experience, interaction, and the educational relevance of the platform. The survey shows high student satisfaction, highlighting the platform’s strengths and identifying areas for improvement. The results also underscore the system’s potential to significantly enhance the educational experience in remote environments, aligning with the United Nations Sustainable Development Goals (SDGs). Full article

This paper discusses the development of an automated sorting machine designed as a comprehensive mechatronics educational project. The project integrates mechanical and electrical design, incorporating a robot arm, a microcontroller, sensors, and actuators. The sorting machine uses color identification to sort wooden blocks of three different colors. The blocks are stacked and dropped onto a conveyor belt by a hopper system that employs a solenoid actuator and a servo to release one block at a time at specific intervals. As the belt runs continuously, each block passes under a color sensor, which monitors the color and signals one of three servo-powered mechanical arms to guide the block into the appropriate chute. Each chute is equipped with a capacitive proximity sensor that sends a voltage signal to the robot controller, queuing commands for the robot to pick up the blocks from the bottom of each chute and return them to the hopper to form a continuously running sorting system. This paper details the design and integration of the system’s various elements and the development of the control software. The designed system can drop blocks every 8.05 s, sort each block within 5 s of being sensed, and return them to the sorting system every 12 s. It has a color-sensing accuracy of 97%, with a failure rate of around 7%. The system achieved quick and reliable sorting using various low-cost, accessible, and open-source parts. The project exemplifies a cost-effective solution suitable for mechatronics education, demonstrating the numerous challenges involved in developing automated sorting systems. Full article