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Proprioceptive sensor data, including inertial measurement units (IMU), joint encoders, and torque sensors, plays a critical role in state estimation for quadruped robots operating in dynamic and unstructured environments. However, these signals are often degraded by various sources of error, such as high-frequency noise, bias, drift, and contact-induced disturbances, which directly affect estimation accuracy and stability. This study presents a systematic analysis of sensor-specific noise characteristics and evaluates the effectiveness of preprocessing methods tailored to each sensor modality. Specifically, moving average filtering is applied to encoder signals to mitigate noise amplification during differentiation, while first-order low-pass filtering is employed for IMU and torque signals to suppress high-frequency noise. Experimental results on a publicly available quadruped dataset demonstrate that encoder velocity RMSE is reduced by 12.09%, high-frequency energy decreases by 59.63%, and signal-to-noise ratio (SNR) improves by 145.6%. However, variance reductions remain limited (3.39% for IMU and 4.05% for torque), indicating the persistence of impulsive, non-Gaussian noise caused by contact events. These findings highlight that linear preprocessing methods are effective for attenuating high-frequency noise but insufficient for handling non-Gaussian disturbances. The study provides practical insights into the effectiveness and limitations of preprocessing strategies, serving as a foundation for developing more robust signal processing and state estimation frameworks in quadruped robotics.

The development of automation and robotics technology has driven innovation in various industrial fields, particularly in automatic sorting systems. Manual sorting processes often lead to inefficiencies and human errors, creating the need for an automatic, fast, and accurate system. This research employs a qualitative method which includes experimentation, testing, and system documentation. The system is designed as a robotic arm for sorting objects based on color, utilizing a TCS3200 color sensor and an ESP32 microcontroller. An ultrasonic sensor detects the presence of objects, while the sorting results are displayed through a real-time web monitoring system. The test results show that the prototype successfully sorts four primary colors (red, green, blue, and yellow) with a high level of accuracy. This research is expected to serve as a reference for the development of automation systems and robotics learning tools in both educational and industrial applications. In addition, this research also contributes to the development of technology that can increase efficiency and accuracy in industrial production processes and provide more environmentally friendly solutions by reducing the need for manual labor.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

 Autonomous robot navigation in dynamic and unstructured environments remains a critical challenge due to unpredictable obstacles, sensor uncertainty, and limited adaptability of traditional planning algorithms. Although conventional navigation methods such as graph-based, potential field–based, and sampling-based approaches have been widely adopted, their performance under real-time dynamic conditions is still constrained. This study aims to design and implement a comprehensive experimental framework to evaluate the effectiveness and limitations of conventional navigation algorithms for autonomous mobile robots operating in dynamic unstructured environments. The research adopts an experimental and comparative methodology by implementing A*, Dijkstra, Artificial Potential Field (APF), and Rapidly-Exploring Random Tree (RRT) algorithms in simulated static and dynamic scenarios. Performance is assessed using quantitative metrics including path length, computation time, success rate, collision rate, and path smoothness. The experimental results demonstrate that graph-based algorithms achieve high success rates and optimal path efficiency in static environments but exhibit limited adaptability to dynamic changes. APF offers fast computation but suffers from high collision rates due to local minima, while RRT shows better adaptability in dynamic environments at the cost of longer and less smooth paths. These findings confirm that conventional navigation methods are insufficient for robust autonomous navigation in highly dynamic and unstructured environments. The study highlights the necessity of adaptive and learning-based navigation frameworks, such as deep reinforcement learning, to enhance real-time decision-making, robustness, and autonomy in future robotic systems.

The increasing adoption of collaborative robots in modern manufacturing environments requires reliable perception systems that can ensure both safety and operational efficiency during human–robot collaboration. This study proposes a CNN-based real-time computer vision system for object and human detection in shared robotic workspaces. The research focuses on developing and evaluating a single-stage deep learning detection model optimized for real-time performance while maintaining high detection accuracy. The proposed methodology includes dataset preparation, model training using transfer learning, real-time system implementation, and comprehensive performance evaluation. Experimental results demonstrate that the developed system achieves high detection accuracy, as reflected by strong precision, recall, and mean Average Precision (mAP) values, while maintaining low inference latency suitable for real-time operation. The system consistently operates above real-time frame-rate thresholds, ensuring timely perception updates required for safety-related decision-making in collaborative robotic environments. Graphical and quantitative analyses further confirm the stability of inference performance under dynamic interaction scenarios involving human movement and multiple objects. Compared with existing approaches, the proposed system provides a balanced trade-off between accuracy and computational efficiency, making it practical for deployment in safety-aware human–robot collaboration scenarios. Overall, the findings indicate that CNN-based real-time object detection systems can effectively support perception and situational awareness in collaborative robotics, contributing to safer and more efficient industrial automation.

The rapid development of digitalization and innovation has become a key driver in improving business processes and the competitiveness of organizations worldwide. This study is the first comprehensive bibliometric analysis examining the relationship between digitalization and innovation in business processes, to map the intellectual structure of this field, track the development of its themes, and identify remaining research gaps. This analysis, which utilizes data from Scopus processed using VOSviewer and Biblioshiny software, covers publications from 2010 to 2024 and employs co-occurrence, co-authorship, and thematic evolution techniques. The results show a rapid growth in publications since 2016, peaking at over 110 publications in 2024. Eight key thematic clusters stand out: Industry 4.0, artificial intelligence, robotic process automation, blockchain, drivers, and agile business process management. Despite the field's maturity, it still suffers from high fragmentation, strong geographic concentration, and a reliance on cross-sectoral research designs. As a result, longitudinal insights remain limited, and digital transformation failure rates remain high, reaching up to 70%. This research presents the first quantitative and visual roadmap of global knowledge flows in this domain and underscores the need for longitudinal, geographically inclusive, and people-centric research to move beyond single-point understandings to a sustainable, context-sensitive framework that enhances both the theoretical depth and practical success of digital-based business process innovation

A wall follower robot is a type of autonomous robot that is designed to move by following a wall at a certain distance. This research aims to design and build a Wall follower robot equipped with a Fuzzy-PID control system to improve navigation performance. The robot uses five HC-SR04 ultrasonic sensors to detect the distance to the wall and the surrounding obstacles. The data from the sensor is then processed by a Fuzzy-PID algorithm that combines the advantages of conventional PID control with fuzzy logic, resulting in a more adaptive response to environmental conditions. The test results showed that the robot with Fuzzy-PID control was able to maintain the stability of the distance to the wall more consistently compared to the pure PID control. In addition, the system exhibits better adaptability to complex environmental conditions, such as sharp turns, uneven wall surfaces, and the presence of resistance variations. The application of Fuzzy-PID control has been shown to improve the stability, response speed, and accuracy of the robot's navigation. These findings are expected to contribute to the development of robotic navigation systems for a wide range of practical applications, including automated cleaning robots, environmental exploration, and industrial systems that require reliable autonomous mobility.

Plastic waste detection in indoor environments is an essential challenge in the development of intelligent cleaning systems and robotic automation. Small and medium-sized plastic debris is often difficult to identify using conventional methods due to variations in color, shape, and reflectance. This study proposes an image-processing-based approach that combines thresholding and contour detection techniques to improve the accuracy of detecting plastic objects on floor surfaces. The initial stage involves converting the image into a color space that is more stable under varying illumination, such as HSV or grayscale, to reduce the influence of lighting intensity. Subsequently, adaptive thresholding is applied to separate plastic objects from the background by using dynamic threshold values tailored to the image’s conditions. The segmentation results are refined through morphological operations such as opening and closing, enabling the removal of small noise and enhancing the clarity of object boundaries. The core stage of the system employs contour detection to extract object shapes and areas, allowing the identification of plastic waste based on size, perimeter, and specific geometric characteristics. Experiments were conducted under different lighting conditions and various floor types, and the results demonstrate that the proposed approach successfully detects plastic debris with satisfactory accuracy and relatively fast processing time. Therefore, this method is suitable for implementation in robotic cleaning systems, indoor cleanliness monitoring devices, and other computer vision applications requiring real-time and efficient object detection.

Robotic extracurricular activities have the potential to develop students' creativity. The implementation of robotics extracurricular programs in improving students' creativity is an activity or activity that is determined outside of class hours, usually aimed at expanding students' insight or knowledge and skills related to technology, operation or development of human machines and human machine systems. The purpose of this study is to describe the design, process, and assessment of robotics extracurricular programs in improving students' creativity at MIN 2 Kediri. This research is a qualitative research with a case study research type. Data collection techniques use interviews, observation, and documentation. Meanwhile, for data analysis, researchers perform data reduction, data presentation, and drawing conclusions. This study produces findings that the robotics extracurricular program in improving students' creativity at MIN 2 Kediri has been running effectively. The methods used in the robotics extracurricular program are the PjBL method, STEM, demonstration, and practice.

In facing the challenges of the dichotomy of knowledge in the modern era, madrasas as Islamic educational institutions need to adjust the curriculum design that is relevant to the changing times, one form of development is by implementing an international standard madrasa curriculum. The purpose of this study is to reveal the strategies, challenges, obstacles and implications in implementing the development of a madrasa curriculum with international standards at MTs Tahfidz Yanbu'ul Qur'an Menawan Kudus. This study applies a Qualitative approach that is a case study, with data collection carried out through interviews, observations, and related documents. The findings of this study indicate that the method of implementing the curriculum at MTs Tahfidz Yanbu'ul Qur'an Menawan Kudus is formed from the integration of the Foundation curriculum, the Ministry of Religion, and the Innovative Curriculum which is then accumulated into intracurricular and extracurricular activities. The challenges and obstacles are caused by limited human resources, both teachers and student abilities. The implications for students are that it is able to produce students who have memorized the Qur'an and excel in the fields of foreign languages, research, science and technology, as shown by increased achievements in olympiads, science and robotics competitions.  

This research presents the design, development, and implementation of a mini smart car prototype that operates using Internet of Things (IoT) technology. The system is built around the ESP8266 microcontroller (Amica version), which functions as the core processing unit responsible for handling Wi-Fi communication and data processing. The motion of the car is controlled by an L298 motor driver module that regulates the operation of DC motors. The entire system is powered by a 3.7-volt rechargeable battery, ensuring portability and energy efficiency. The study discusses in detail the hardware configuration, software programming, and integration of IoT-based control through a web or mobile interface. Functional testing of the prototype, named MINIOT, focuses on evaluating the responsiveness, stability, and reliability of remote control operations. The results are expected to show that the system can effectively receive and execute user commands while transmitting real-time telemetry data, such as motor status and connection indicators. This project demonstrates the feasibility of low-cost IoT-based automation for small-scale robotic applications.

This research presents the design, development, and implementation of a mini smart car prototype that operates using Internet of Things (IoT) technology. The system is built around the ESP8266 microcontroller (Amica version), which functions as the core processing unit responsible for handling Wi-Fi communication and data processing. The motion of the car is controlled by an L298 motor driver module that regulates the operation of DC motors. The entire system is powered by a 3.7-volt rechargeable battery, ensuring portability and energy efficiency. The study discusses in detail the hardware configuration, software programming, and integration of IoT-based control through a web or mobile interface. Functional testing of the prototype, named MINIOT, focuses on evaluating the responsiveness, stability, and reliability of remote control operations. The results are expected to show that the system can effectively receive and execute user commands while transmitting real-time telemetry data, such as motor status and connection indicators. This project demonstrates the feasibility of low-cost IoT-based automation for small-scale robotic applications.

Conventional robotic surgical systems, while offering enhanced dexterity and 3D visualization, suffer from a critical limitation: the absence of tactile sensation. This sensory disconnect can lead to inadvertent tissue damage from excessive force application and complicates delicate maneuvers that rely on the surgeon's sense of touch. This research proposes and validates a novel surgical robotic system architecture designed to bridge this sensory gap by integrating high-fidelity 3D visual input with accurate, real-time force feedback from tactile sensors mounted on the end-effector. To rigorously evaluate this innovation, a structured comparative methodology was employed. A cohort of surgeons performed standardized surgical tasks, including suturing and tissue manipulation, on realistic soft-tissue phantoms. The performance of a conventional (visual-only) system was benchmarked against that of the proposed (visual-haptic) system. A comprehensive dataset was collected, which included objective metrics such as task completion time, precision deviation from the ideal tool path, and the magnitude of applied forces. Concurrently, subjective evaluations from the participating surgeons were gathered to assess perceived control, cognitive workload, and overall task confidence. The test data revealed statistically significant improvements when using the visual-haptic system. Participants not only completed tasks with greater speed and accuracy but also applied considerably lower and more consistent forces. The analysis underscores that haptic feedback, enabled by advanced sensor fusion, not only restores a crucial 'sense of touch' to the surgeon but also reduces the incidence of excessive force application, potentially minimizing tissue trauma and improving patient recovery. These findings confirm the hypothesis that haptic-visual integration constitutes a new paradigm in robotic surgery, shifting the paradigm from purely visual guidance to a more intuitive, multi-sensory surgical experience. This study also discusses future challenges and opportunities, including the potential for AI-driven partial autonomy, such as creating virtual safety boundaries or automating sub-tasks, and the development of next-generation sensor technologies to further enhance clinical outcomes.

A lopography or barium enema colostomy is a radiographic examination of the colon using a contrast medium inserted through a colostomy. This examination aims to evaluate the condition of the colon after the colostomy, including detecting the presence of obstructions or masses in the gastrointestinal tract. The contrast medium used is a type of non-ionic soluble water that is inserted through the stoma and anus orifice until it fills the entire colon, characterized by the exit of contrast through the stoma. The examination was carried out using several radiographic projections, including plain photographs of the AP abdomen, as well as post-contrast projections of the Antero Posterior (AP), Right Posterior Oblique (RPO), Left Posterior Oblique (LPO), and lateral. This study aims to find out the procedure for the Lopography examination at the Radiology Installation of Caruban Hospital and the medical reasons for the use of two channels of input of contrast media, namely through the stoma and anus. The method used is qualitative descriptive research with a case study approach. The study was carried out from November to May 2025, with one post-colostomy patient as a sample. Data collection was carried out through direct observation, interviews with medical personnel, and documentation of radiographic procedures. Data analysis includes data reduction, data presentation, and conclusion drawn. The results of the study showed that the procedure of Robotic examination did not require any special preparation before the procedure. The non-ionic contrast medium used is mixed with aquades at a ratio of 1:3 (about 200 cc), and inserted through both the stoma and the anus to obtain a thorough visualization of the colon. The selection of two input paths aims to ensure that there are no blockages or masses that block the contrast flow. The projections used are adjusted to provide maximum information about the structure of the colon after colostomy

ABSTRACT Information services on campus are often delayed due to reliance on staff, resulting in long queues and inefficient waiting times. This study aims to design and develop a robotic interaction system based on speech recognition and Natural Language Processing (NLP), equipped with a virtual button as an alternative activation method. The system allows users to interact with the robot using voice, while the virtual button provides an additional option for users who are more comfortable with touch-based interaction. The research method employed is prototype development, which includes the design, implementation, and evaluation of the system. Testing was conducted to assess the effectiveness of the system in delivering information services quickly and accurately. The results show that the developed system can enhance service efficiency, reduce dependence on staff, and facilitate faster and more practical interactions between users and the robot. This study is expected to contribute to the development of technology-based public service systems, especially in the campus environment. Keywords: robotic interaction, speech recognition, NLP, virtual button, public service

Object detection in digital images is a crucial aspect of image processing and computer vision, with applications ranging from surveillance systems and robotics to image-based search. One commonly used approach is template matching, a technique that compares a template image with sections of the target image to identify similar patterns. This study explores the implementation of the template matching method for object recognition in digital images. The process begins with image preprocessing to enhance data quality, followed by a matching procedure using normalized cross-correlation. Experimental results indicate that this method can accurately detect objects under stable lighting and scale conditions. However, its performance decreases when images undergo rotation or scale variations. Therefore, while template matching proves effective under ideal conditions, further methodological development is needed to improve its robustness against geometric transformations.s

In this have a look at, a new mathematical model for FDAE-based smart manage systems is proposed. The model carries fractional derivatives blended with algebraic constraints to symbolize prolonged memory results. We describe a numerical method to solve the proposed device and practice this version to robotics, self-reliant cars, and sensible prosthetics. The Fractional Collocation Method is employed to resolve FDAEs, making sure accuracy and balance. To validate the proposed method, we introduce 3 examples: a simple FDAE demonstrating the accuracy of the numerical solution, a device of FDAEs modeling interdependent dynamic variables with algebraic constraints, and an FDAE with a nonlinear algebraic constraint, highlighting the approach's capability to handle complicated, nonlinear dynamics. Simulation results verify that FDAEs offer a more practical and powerful tool for designing and reading wise manage systems as compared to classical techniques.

Minimally invasive surgery offers several advantages, including reduced blood loss, smaller incisions, less pain, and a lower risk of complications than open surgery. This approach enhances patient comfort and supports faster recovery. When guided by optimal path planning, surgical robots can accurately navigate the body to remove malignant tumors with high precision. This study proposes a Modified Particle Swarm Optimization (MPSO) algorithm to determine the optimal path for robotic-assisted minimally invasive surgery targeting brain tumors. The algorithm improves upon standard PSO by modifying the velocity update equation and incorporating an adaptive inertia weight, enhancing convergence speed, global search ability, and solution accuracy. Experimental results show that the proposed MPSO achieves a maximum fitness value of 19.10 in a sparse obstacle environment, outperforming standard PSO and IPSO in quality and in the required number of iterations. The approach effectively balances path efficiency and obstacle avoidance, making it well-suited for complex surgical scenarios. In conclusion, the MPSO-based method provides a reliable and precise solution for robotic surgical navigation, improving outcomes and safety in minimally invasive procedures.

This research focuses on designing the shortest path to a docking station for the charging system of a robotic coffee delivery system on a cruise ship. With the growing demand for more efficient and innovative services in the maritime industry, autonomous robots are increasingly being used for tasks such as delivering coffee to passengers. However, these robots face unique challenges in the confined and dynamic environment of a ship, particularly in terms of power management and recharging. The goal of this research is to create an efficient charging system using a power bank and Internet of Things (IoT) technology to minimize downtime and ensure continuous robot operation with minimal human intervention. By utilizing sensors such as the MPU 6050 for motion detection and the TCS 3200 for color recognition, the robot can autonomously navigate the shortest path to the docking station for charging, while IoT enables real-time battery capacity monitoring. This helps reduce the time spent on non-operational tasks and improves the overall efficiency of the system. This research not only enhances the robot’s ability to operate autonomously in complex environments but also provides a flexible and scalable solution for the maritime industry to integrate advanced robotics into their service operations. The results of this study contribute to the increased adoption of autonomous systems in the maritime sector, particularly in improving service efficiency on board, reducing the need for human intervention, and ensuring smoother service delivery to passengers.

SMK Negeri 3 Jayapura is one of the vocational schools specializing in technology engineering in Jayapura City. There are three primary issues at SMK Negeri 3 Jayapura. First, the limited competence of teachers in microcontroller programming. Second, there is a lack of facilities and infrastructure for microcontroller practical work. Third, the low achievement and interest of students in the latest technological developments. The proposed solutions in this community service activity include: conducting training on Arduino Uno and Espressif microcontrollers using the Arduino IDE programming platform, developing practical modules for microcontrollers, sensors, and actuators, providing a master file for Arduino Uno programs, installing the necessary software on school computers, conducting robotics training for students. All five solutions were successfully implemented and received positive appreciation from both students and teachers at SMK Negeri 3 Jayapura.