This study investigates the performance of LiDAR and Kinect sensors in trajectory tracking for XiaoQiang Automated Guided Vehicles (AGVs) across both real-world and simulated environments. Using ROS, Gazebo, and MATLAB, the research evaluates bidirectional navigation between two fixed points under varying conditions, including obstacle-free and obstacle-rich scenarios. A total of 400 trajectory trials and 100 parking tests were conducted to assess path accuracy, processing time, and sensor reliability. Results show that Gazebo simulations closely replicate real-world behavior, while MATLAB simulations align more closely with idealized paths but lack real-world adaptability. LiDAR demonstrated superior robustness and obstacle detection in complex environments, maintaining an average distance error of 1.7 cm and angular deviation of 2.9°, whereas Kinect offered faster processing in open spaces but showed reduced angular stability, with 2.4 cm and 4.1° deviations respectively. These findings highlight the limitations of simulation fidelity and the need for sensor fusion strategies to bridge the gap between virtual and physical performance. The study contributes novel empirical insights into AGV sensor behavior and advocates for improved simulation calibration to enhance autonomous navigation reliability.

This paper presents a novel approach to address the escalating challenges in urban parking management by developing a LoRaWAN-based autonomous smart parking system (UrbanPark). With urbanization and increased vehicle populations, there is a pressing need for innovative solutions to optimize resource utilization and enhance user experience of vehicle parking. Leveraging the capabilities of Long-Range Wide Area Network (LoRaWAN), this system integrates a mobile application, a web-based interface, and LoRaWAN-enabled smart parking modules to provide users and administrators with real-time parking space availability information. The mobile application offers a user-friendly interface for drivers to locate and reserve parking spaces, while the web-based interface empowers administrators with comprehensive oversight and management functionalities. The LoRaWAN-enabled smart parking modules autonomously monitor parking space occupancy and transmit data efficiently over long distances with minimal energy consumption and are implemented in Raspberry Pi as the server. Through this integration, the system offers scalable, cost-effective, and sustainable solutions to urban parking management challenges, contributing to the development of smarter, more connected urban environments.

Automated Guided Vehicles (AGVs) play a crucial role in industrial automation, streamlining material handling and logistics operations. This paper investigates the performance of eleven classical pathfinding algorithms—including Dijkstra, Depth First Search (DFS), Floyd Warshall, Binary Search, and Prim’s MST on an Ackermann steered electric vehicle (EV) based AGV navigating six charging stations in a simulated urban environment. Algorithms are classified into By Station and By Edge , with implementations adapted to meet their specific routing requirements. AGV performance is evaluated using conventional metrics runtime, travel distance, cost, speed, and station visitation alongside a novel Energy Consumption Score (ECS) that balances operational efficiency with computational overhead. Results show that classical algorithms can efficiently handle static, predictable networks, though some prioritize cost over station coverage. ECS provides a unified framework to rank algorithm suitability. The obtained results shows, Based on the tested algorithms: Floyd Warshall achieved the best overall performance in terms of minimal cost and ECS. For maximizing station coverage, TSP and Prim’s MST were the most effective. Edge-based algorithms proved reliable for navigating predefined routes but were generally less efficient in terms of overall cost and energy consumption.Future work will explore heuristic and hybrid approaches, dynamic networks, multi AGV coordination, and energy aware routing for improved urban path planning.

This investigation is centrally focused on the comprehensive evolution and enhancement of FOODIEBOT(shortened name for food delivery robot, an adaptive service automaton with a wide range of functionalities. Its capabilities encompass sophisticated image processing methods, seamlessly integrated via mobile applications (APP) and web interfaces, tailored specifically for intricate object manipulation in dining hall settings. During its developmental phase, the precise calibration of PID controller coefficients emerged as an essential requirement. The model underwent meticulous scrutiny through detailed simulations using MATLAB software. Following this phase, its operational efficiency navigating through circular, elliptical, spiral, and octagonal trajectories underwent rigorous examination, utilizing optimization methodologies like Beetle Antennae Search (BAS) Algorithm, Particle Swarm Optimization (PSO), Pelican Optimization Algorithm (POA), and Equilibrium Optimizer (EO). The exposition emphasizes the diverse dispersion of optimized coefficients within each algorithmic framework. The pinnacle of this effort involved a comprehensive evaluation of pathway performance, amalgamating insights from each optimization paradigm. The discussion extensively delineates both simulated and real-time performance metrics of the robot, validating the accuracy and reliability of simulation in deriving PID controller values. In the comprehensive evaluation of methodologies and the robotic system's effectiveness, the BAS technique excels in operational efficiency. This method consistently outperforms its counterparts in execution time, primarily due to its meticulous optimization of particle count. The comparative analysis across various trajectories reveals intriguing insights. The EO approach showcases outstanding accuracy in Path 1, while the POA method achieves optimal precision in Path 3. Impressively, the BAS technique demonstrates unparalleled accuracy in Path 4. Furthermore, in terms of solution optimization, the BAS method consistently displays the shortest execution times across all traversed pathways. When examining maximum velocity along these routes, the PSO method excels in Paths 1, 3, and 4, consistently achieving the highest speeds. Notably, Path 2 uniquely displays the peak velocity attained by the POA method. This article presents comprehensive insights into the constituent elements of the robotic system's design. The inquiry delves into the intricate nuances of optimization methodologies, elucidating their profound impact on the service automaton's performance across diverse orientations. The pragmatic implications underscore the critical role of temporal considerations in the judicious selection of these methodologies. The observed congruence between simulated and practical performance serves as a definitive validation, affirming the precision of simulation computations and the subsequent derivation of PID controller values.

In this paper, a secure exam proctoring assistant ‘EMTIHAN’ (which means exam in Arabic/Persian/Urdu/Turkish languages) is developed to address concerns related to online exams for handwritten topics by allowing students to submit their answers online securely via their mobile devices. This system is designed with an aim to lessen the student’s burden of exam submission by offering portable hardware and easy-to-use cloud infrastructure. The main contribution of this research is to design an innovative system for online examination (in remote or distance learning scenarios), and using mixed methods i.e. both quantitative and qualitative data collection and analysis to verify its performance. The comparative results regarding submission time and security features have proven the efficacy of EMTIHAN against manual submission with participants across 5 countries using purposive sampling and currently available solutions e.g. Chaoxing MOOC and Google Classroom with 94.5

This study focuses on the importance of monitoring student attendance in education and the challenges faced by educators in doing so. Existing methods for attendance tracking have drawbacks, including high costs, long processing times, and inaccuracies, while security and privacy concerns have often been overlooked. To address these issues, the authors present a novel internet of things (IoT)-based self-lecture attendance system (SLAS) that leverages smartphones and QR codes. This system effectively addresses security and privacy concerns while providing streamlined attendance tracking. It offers several advantages such as compact size, affordability, scalability, and flexible features for teachers and students. Empirical research conducted in a live lecture setting demonstrates the efficacy and precision of the SLAS system. The authors believe that their system will be valuable for educational institutions aiming to streamline attendance tracking while ensuring security and privacy.

Nowadays, the unmanned aerial vehicle (UAV) has a wide application in transportation. For instance, by leveraging it, we are able to perform accurate and real-time vehicle speed detection in an IoT-based smart city. Although numerous vehicle speed estimation methods exist, most of them lack real-time detection in different situations and scenarios. To fill the gap, this paper introduces a novel low-altitude vehicle speed detector system using UAVs for remote sensing applications of smart cities, forging to increase traffic safety and security. To this aim, (1) we have found the best possible Raspberry PI’s field of view (FOV) camera in indoor and outdoor scenarios by changing its height and degree. Then, (2) Mobile Net-SSD deep learning model parameters have been embedded in the PI4B processor of a physical car at different speeds. Finally, we implemented it in a real environment at the JXUST university intersection by changing the height (0.7 to 3 m) and the camera angle on the UAV. Specifically, this paper proposed an intelligent speed control system without the presence of real police that has been implemented on the edge node with the configuration of a PI4B and an Intel Neural Computing 2, along with the PI camera, which is armed with a Mobile Net-SSD deep learning model for the smart detection of vehicles and their speeds. The main purpose of this article is to propose the use of drones as a tool to detect the speeds of vehicles, especially in areas where it is not easy to access or install a fixed camera, in the context of future smart city traffic management and control. The experimental results have proven the superior performance of the proposed low-altitude UAV system rather than current studies for detecting and estimating the vehicles’ speeds in highly dynamic situations and different speeds. As the results showed, our solution is highly effective on crowded roads, such as junctions near schools, hospitals, and with unsteady vehicles from the speed level point of view.

Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods.

After nearly 30 years of development, service robot technology has made important achievements in the interdisciplinary aspects of machinery, information, materials, control, medicine, etc. These robot types have different shapes, and mainly in some are shaped based on application. Till today various structure are proposed which for the better analysis’s need to have the mathematical equation that can model the structure and later the behaviour of them after implementing the controlling strategy. The current paper discusses the various shape and applications of all available service robots and briefly summarizes the research progress of key points such as robot dynamics, robot types, and different dynamic models of the differential types of service robots. The current review study can be helpful as an initial node for all researchers in this topic and help them to have the better simulation and analyses. Besides the current research shows some application that can specify the service robot model over the application.

The term telemedicine was first used in the 1920s, although used many years ago and has continued to evolve today. Medical diagnoses usually require visual information, but remote display systems have recently become a special place due to the constant unavailability of the treating physician or the remoteness of medical centers and the constant need of some patients for round-the-clock care. In this article, an Arduino-based heart rate information system is designed and implemented. Due to the reasonable price and easy accessibility of the created system in the fraction, it has many applications. The results of the designed system showed the system's capabilities to track and know the person's heart rate.

Accounting for people is the first step of every manpower-based organization in today's world. Hence, it takes up a signification amount of energy and value in the form of money from respective organizations for both implementing a suitable system for manpower management as well as maintaining that same system. Although this amount of expenditure for big organizations is near to nothing, rather just a formality, it does not hold as much truth for small organizations such as schools, colleges, and even universities to a certain degree. This is the first point. The second point for discussion is that much work has been done to solve this issue. Various technologies like Biometrics, RFID, Bluetooth, GPS, QR Code, etc., have been used to tackle the issues of attendance collection. This study paves the path for researchers by reviewing practical methods and technologies used for existing attendance systems.

SCADA (Supervisory Control and Data Acquisition) and DCS (Distributed Control System) are both famous terms in automation and they both have critical rules. There is rarely an industry in the world which are not auditing their outcome based on one of these terms. But IOT developed in recent years, mixing the functionality of these two terms has been going on too with various security challenges involved. Even some previous issues like the price of software and easy extendibility of them make the way for small industries to have their benefits. In this research paper along with the designing and comparison, two small SCADA systems for agricultural applications tries to reduce the size as well as address a zonal method to investigate the security problems. Till these days' researchers have shown lots of development to reduce the SCADA cost in different manner but, having the compact, powerful and small size host for SCADA system has not been reported before. One of the novel aspects in this design is hiring the Raspberry Pi as the smallest computer instead of PC or laptop which along with reducing the system cost can also reduce the whole design system. Besides, Web based SCADA system has been successfully designed here and used. This paper discusses the context of local as well as remote monitoring of an agricultural plant environment with the help of a Raspberry Pi network through which a SCADA System was established. This paper aims to prove that a far more efficient and cost-effective along a secure SCADA System for monitoring is possible with the lowest of efforts. So, having zonal idea will help system to achieve the aim of secure sensor monitoring without any kind of outside interference. The result shows the successful design and implementation of zonal idea as well as the perfect usage of Raspberry Pi as a small and reliable host.

Controlling a system can be done in various ways and methods. The classical method which even now a day as a solution works is PID which in that with some method three-parameter of controller called P (Proportional), I (Integral), D (Derivational) tuned to have the best controlling response from a system. The AGV robot as the abbreviation of the Automated Guided Vehicle is as a famous robot platform which used in various industries relies on PID controllers in various ways. Each AGV or Machine has its own set of function, hence, in order to accomplish the exact set of workload more efficiently one need to actually tune the PID parameters accordingly so that there cannot be an intolerable amount of energy loss, inefficiency rate, lag, lack of robustness etc. In this paper over than introduction of PID controller and see the effect of each parameter on the real system the compassion between hired methods on AGV robot are investigated. As this review indicates that various PID tune method are used based on system requirements with the help of Lyapunov Direct Method, traditional Ziegler Nichols, Fuzzy controller, human immune system called the humoral, neural network, etc to control the speed and steering of an AGV systems.