Automation

The field of haircutting robots is poised for a significant transformation, driven by advancements in artificial intelligence, mechatronics, and humanoid robotics. This perspective paper examines the emerging market for haircutting robots, propelled by decreasing hardware costs and a growing demand for automated grooming services. We review foundational technologies, including advanced hair modeling, real-time motion planning, and haptic feedback, and analyze their application in both teleoperated and fully autonomous systems. Key technical requirements and challenges in safety certification are discussed in detail. Furthermore, we explore how cutting-edge technologies like direct-drive systems, large language models, virtual reality, and big data collection can empower these robots to offer a human-like, personalized, and efficient experience. We propose a business model centered on supervised autonomy, which enables early commercialization and sets a path toward future scalability. This perspective paper provides a theoretical and technical framework for the future deployment and commercialization of haircutting robots, highlighting their potential to create a new sector in the automation industry. Full article

A pressure regulator is essential for pressure control in a gas transmission system. The traditional maintenance approaches for pressure regulators involve equipment disassembly that disrupts normal production. In response, this paper proposes a support vector machine (SVM) model improved by the grey wolf optimization algorithm (GWO) and autoencoder (AE), i.e., the AE-GWO-SVM model. It achieves intelligent fault diagnosis on pressure regulators based on operation data collected by the SCADA system. Firstly, a number of actual pressure regulator faults are counted, and it is found that internal and external leakages are key problems faced by pressure regulators. To address the limited fault data available in field practice, a dataset of pressure regulator faults is constructed using numerical simulation. Additionally, 18 new features are obtained through feature combination, and an improved distance evaluation method (IDE) is utilized to select highly correlated features as input for the machine learning model. Furthermore, an autoencoder (AE) is employed to overcome the interference from abnormal data, which significantly enhances the fault identification process of GWO-SVM. To verify the performance of the AE-GWO-SVM model, an experimental platform for pressure regulators is designed and constructed. Compared with conventional SVM, the accuracy of the AE-GWO-SVM model increases from 60.4% to 86.7%, indicating its strong diagnosis capability for pressure regulator faults. Full article

This paper proposes a learning-driven passivity-based control (PBC) strategy for sensorless induction motors, combining a nonlinear adaptive observer with recurrent neural networks (RNNs) to improve robustness and estimation accuracy under dynamic conditions. The main novelty is the integration of neural learning into the passivity framework, enabling real-time compensation for un-modeled dynamics and parameter uncertainties with only one gain adjustment across a broad speed range. Lyapunov-based analysis guarantees the global stability of the closed-loop system. Experiments on a 1.1 kW induction motor confirm the approach’s effectiveness over conventional observer-based and fuzzy-enhanced methods. Under torque reversal and flux variation, the proposed controller achieves a torque mean absolute error (MAE) of 0.18 Nm and flux MAE of 0.21 Wb, compared to 1.58 Nm and 0.85 Wb with classical PBC. When peak torque deviation drops from 42.52% to 30.85% of the nominal, torque symmetric mean absolute percentage error (SMAPE) improves by 7.6%, and settling time is reduced to 985 ms versus 1120 ms. These results validate the controller’s precision, adaptability, and robustness in real-world sensorless motor control. Full article

In vision-guided autonomous robots, object detectors play a crucial role in perceiving the environment for path planning and decision-making. However, adaptive adversarial patch attacks undermine the resilience of detector-based systems. Strengthening object detectors against such adaptive attacks enhances the robustness of navigation systems. Existing defenses against patch attacks are primarily designed for stationary scenes and struggle against adaptive patch attacks that vary in scale, position, and orientation in dynamic environments. In this paper, we introduce Ad_YOLO+, an efficient and effective plugin that extends Ad_YOLO to defend against white-box patch-based image attacks. Built on YOLOv5x with an additional patch detection layer, Ad_YOLO+ is trained on a specially crafted adversarial dataset (COCO-Visdrone-2019). Unlike conventional methods that rely on redundant image preprocessing, our approach directly detects adversarial patches and the overlaid objects. Experiments on the adversarial training dataset demonstrate that Ad_YOLO+ improves both provable robustness and clean accuracy. Ad_YOLO+ achieves $85.4\%$ top-1 clean accuracy on the COCO dataset and $74.63\%$ top-1 robust provable accuracy against pixel square patches anywhere on the image for the COCO-VisDrone-2019 dataset. Moreover, under adaptive attacks in AirSim simulations, Ad_YOLO+ reduces the attack success rate, ensuring tracking resilience in both dynamic and static settings. Additionally, it generalizes well to other patch detection weight configurations. Full article

This paper presents a proof-of-concept real-time safety alerting system for safety-critical environments such as construction sites. Key components of the system include Bluetooth Low Energy (BLE) devices for indoor localization, integrated with a customized Android application using the Framework for Internal Navigation and Discovery (FIND). Administrative control and data management are handled by a server-side component, supported by an interactive website for real-time safety monitoring. The architecture supports safety zoning and employs machine learning algorithms, including k-NN, Random Forest, and SVM, for analyzing localization data. Experimental validation in a laboratory setup demonstrates a localization accuracy of 97%, a response time of 1.2 s, and a maximum spatial error of 1.2 m. These results highlight the system’s reliability and potential for enhancing safety compliance in real-world deployment scenarios. Full article

This paper presents a comprehensive study of an ESP32 microcontroller-based self-balancing mobile robot system designed in conjunction with an Android app for Bluetooth control. The robot employs an MPU6050 accelerometer/gyroscope to execute dynamic equilibrium control for robotic balance. This study explores the design of a system composed of an ESP32-based dual-platform architecture. The firmware for the ESP32 executes real-time motor control and sensor processing, while the Android application provides the user interface, data visualization, and command transmission. The system achieves stable operation with tilt angle variations of ±2.5° ($\sigma =0.8°$, n = 50 trials) during normal operation with a PID controller tuned to KP = 6.0, KI = 0.1, and KD = 1.5. In experimental tests, control latency was measured at 38–72 ms (mean = 55 ms, $\sigma =12$ ms) over distances of 1–10 m with a robust Bluetooth connection. Extended operational tests indicated the reliability of both autonomous obstacle avoidance mode and manual control exceeding 95%. Key contributions include gyro drift compensation using a progressive calibration scheme, intelligent battery management for operational efficiency, and a dual-mode control interface to facilitate seamless transition between manual and autonomous operation. Processing of real-time telemetry on the Android application allows visualization of important parameters like tilt angle, motor speeds, and sensor readings. This work contributes to a cost-effective mobile robotics platform (total cost: USD 127) through the provision of detailed design specifications, implementation strategies, and performance characteristics. Full article

Industry Revolution Five (Industry 5.0) will shift the focus away from technology and rely more on to the collaboration between humans and AI-powered robots. This approach emphasizes a more human-centric perspective, enhanced resilience, optimized workplace processes, and a stronger commitment to sustainability. The humanoid robot market has experienced substantial growth, fueled by technological advancements and the increasing need for automation in industries such as service, customer support, and education. However, challenges like high costs, complex maintenance, and societal concerns about job displacement remain. Despite these issues, the market is expected to continue expanding, supported by innovations that enhance both accessibility and performance. Therefore, this article proposes the design and implementation of low-cost, remotely controlled humanoid robots via a mobile application for home-assistant applications. The humanoid robot boasts an advanced mechanical structure, high-performance actuators, and an array of sensors that empower it to execute a wide range of tasks with human-like dexterity and mobility. Incorporating sophisticated control algorithms and a user-friendly Graphical User Interface (GUI) provides precise and stable robot operation and control. Through an in-house developed code, our research contributes to the growing field of humanoid robotics and underscores the significance of advanced control systems in fully harnessing the capabilities of these human-like machines. The implications of our findings extend to the future development and deployment of humanoid robots across various industries and societal contexts, making this an ideal area for students and researchers to explore innovative solutions. Full article

Wireless sensor networks (WSNs) have gained significant attention across various industries and scientific fields. Localization, a crucial aspect of WSNs, involves accurately determining node positions to track events and execute actions. Despite the development of numerous localization algorithms, real-world environments pose challenges such as anisotropy, noise, and faults. To improve accuracy amidst these complexities, researchers are increasingly adopting advanced methodologies, including soft computing, software-defined networking, maximum likelihood estimation, and optimization techniques. Our comprehensive review from 2020 to 2024 reveals that approximately 29% of localization solutions employ optimization techniques, 48% of which utilize nature-inspired swarm-based algorithms. These algorithms have proven effective for node localization in a variety of applications, including smart cities, seismic exploration, oil and gas reservoir monitoring, assisted living environments, forest monitoring, and battlefield surveillance. This underscores the importance of swarm intelligence algorithms in sensor node localization, prompting a detailed investigation in our study. Additionally, we provide a comparative analysis to elucidate the applicability of these algorithms to various localization challenges. This examination not only helps researchers understand current localization issues within WSNs but also paves the way for enhanced localization precision in the future. Full article

This paper presents a new strategy for wind energy harvesting to enhance the performance of the electric-wind vehicle (EWV). A wind turbine is mounted on the front of an electric-wind vehicle model to capture wind that blows in the opposite direction of the moving vehicle. When the primary battery runs low, the generator switches on, converting wind energy into electricity and storing it in a backup battery. The switching circuit is developed to alternate between the main battery and the backup battery based on the battery capacity level. During the movement of the EWV, the backup battery charges using the wind turbine while the main battery discharges. A modified sliding mode control is used to track the reference speed of the EWV and to regulate the speed by switching between the two batteries. Several scenarios are applied to investigate the proposed strategy. The results show that the proposed strategy can save power by 30% compared to the conventional strategy. Moreover, the modified sliding mode control enhances the EWV’s dynamic performance in contrast to PID control, which shows poor performance (low rise time and low overshoot). Full article

The rapid growth of Internet of Things (IoT) applications is reshaping countless sectors and, in the process, exposing the limitations of existing connectivity solutions—especially in rugged regions like South America’s Andean highlands, where conventional infrastructure networks are scarce. To address this gap, this research introduces an automated system that captures uplink and downlink data from LoRaWAN nodes in real time. The system continuously monitors essential indicators—RSSI, SNR, transmit power, spreading factor, bandwidth, device speed, and packet interval—and stores them for later analysis. Thanks to its modular design, the system adapts easily to urban, semi-urban, and challenging rural topographies. Field trials show that our tool gathers reliable performance data while cutting the time and manual effort typical of traditional measurement campaigns. These results streamline IoT roll-outs in demanding terrain and lay the foundation for scalable LoRaWAN deployments throughout the Andean region. Full article

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

Poultry waste composting is a necessary technique for agricultural farm sustainability. Composting is a dynamic process influenced by multiple variables. Humidity and temperature play fundamental roles in analyzing its different phases according to the environment and composting technique. Current developments for monitoring these variables include automation via intelligent Internet of Things (IoT)-based sensor networks for variable tracking. These advancements serve as efficient tools for modeling that facilitate the simulation and prediction of composting process variables to improve system efficiency. Therefore, this paper presents the dynamic modeling of composting via forced aeration processes in high-mountain climates, with the intent of estimating biomass temperature dynamics in different phases using system identification techniques. To this end, four dynamic model estimation structures are employed: transfer function (TF), state space (SS), process (P), and Hammerstein–Wiener (HW). The and model quality, fitting results, and standard error metrics of the different models found in each phase are assessed through residual analysis from each structure by validation with real system data. Our results show that the second-order underdamped multiple-input–single-output (MISO) process model with added noise demonstrates the best fit and validation performance. Full article

Deep learning with unmanned aerial vehicles (UAVs) is transforming maritime search and rescue (SAR) by enabling rapid object identification in challenging marine environments. This study benchmarks the performance of YOLO models for maritime SAR under diverse weather conditions using the SeaDronesSee and AFO datasets. The results show that while YOLOv7 achieved the highest mAP@50, it struggled with detecting small objects. In contrast, YOLOv10 and YOLOv11 deliver faster inference speeds but compromise slightly on precision. The key challenges discussed include environmental variability, sensor limitations, and scarce annotated data, which can be addressed by such techniques as attention modules and multimodal data fusion. Overall, the research results provide practical guidance for deploying efficient deep learning models in SAR, emphasizing specialized datasets and lightweight architectures for edge devices. Full article

In the context of the mining industry, the belt conveyor is a critical piece of equipment. The motor constitutes the primary component of the belt conveyor apparatus, and its stable and accurate operation can significantly influence the performance of the belt conveyor apparatus. This paper introduces an integrated control approach combining vector control methodology with active disturbance rejection control (ADRC) for velocity regulation and model predictive control (MPC) for current tracking. The ADRC framework actively compensates for load disturbances and parameter variations during speed control, while MPC achieves precise current regulation with minimal tracking error. Validation involved comprehensive MATLAB/Simulink R2024a simulations modeling PMSM behavior under mining-specific operating conditions. The results demonstrate substantial improvements in dynamic response characteristics and disturbance rejection capabilities compared to conventional control strategies. The proposed methodology effectively addresses critical challenges in mining conveyor applications, enhancing operational reliability and system longevity. Full article

This paper focuses on the complex dynamics that concern assembly line balance in the context of mass customization within manufacturing. In fact, the increase in demand for customized products has heightened the complexities associated with achieving optimal efficiency, productivity, product quality, and customer satisfaction. The research proposes a multi-criteria analysis of statistical methods to determine the fluctuation of parameters affecting the state of balance of an assembly line. A 3D matrix model is suggested to analyze the parameters managing the assembly line. This representation is executed using the MATLAB R2024b tool, and a methodology for finding the variability of parameters affecting balance through statistical approaches is proposed. We observed that changes in parameters such as task times, worker efficiency, or material flow led to significant changes in the line’s overall balance. As a result, static balancing becomes inadequate to deal with the complexities introduced by these highly variable parameters. The novelty of this paper consists of the innovative integration of multi-criteria statistical analysis and 3D matrix modeling to detect parameter variability and optimize assembly line balancing. Conventional static approaches are often unable to capture the process-dynamic aspect of modern manufacturing. This work presents a systematic methodology capable of identifying, quantifying, and moderating the variability of key operating parameters. This methodology, carried out using MATLAB-based simulations, is based on principal component analysis (PCA) and correlation analysis to detect critical factors influencing balancing efficiency. By structuring assembly line parameters in a 3D matrix representation, this research gives a holistic, data-based method for improving decision-making in balancing procedures. The research goes beyond theoretical modeling by applying the approach to a real automotive assembly line, validating its effectiveness and demonstrating its practical applicability in industrial conditions. Full article

Disturbance rejection in localized continuous linear systems remains challenging due to the interplay between spatial constraints, exact invariance conditions, and discretization effects. Existing methods either lack rigorous guarantees for exact rejection in continuous time or fail to address the subtleties of weak rejection in discrete-time implementations. In this work, we first establish necessary and sufficient conditions for exact disturbance rejection in the continuous-time output case, deriving control laws that enforce output invariance. For the discrete-time output scenario, we resolve the weak rejection problem by proving that—under stabilizability assumptions and an optimal discretization step—the system can achieve practical disturbance attenuation. Our results unify these two regimes, providing explicit design criteria for robust control in localized linear systems. Full article

Wheel loaders in mines and construction sites repeatedly load soil from a pile to load receivers. Automating this task presents a challenging planning problem since each loading’s performance depends on the pile state, which depends on previous loadings. We investigate an end-to-end optimization approach considering future loading outcomes and transportation costs between the pile and load receivers. To predict the evolution of the pile state and the loading performance, we use world models that leverage deep neural networks trained on numerous simulated loading cycles. A look-ahead tree search optimizes the sequence of loading actions by evaluating the performance of thousands of action candidates, which expand into subsequent action candidates under the predicted pile states recursively. Test results demonstrate that, over a horizon of 15 sequential loadings, the look-ahead tree search is 6% more efficient than a greedy strategy, which always selects the action that maximizes the current single loading performance, and 14% more efficient than using a fixed loading controller optimized for the nominal case. Full article

This paper presents an advanced rescheduling approach that jointly applies two sets of objective functions within a novel multi-objective search algorithm and a production simulation of the manufacturing system. The role of the first set of objective functions is to optimize the performance of production systems, while the second newly proposed set of objective functions aims to minimize the intervention changes from the original schedule, thereby supporting schedule stability and smooth manufacturing processes. The combined use of these two objective sets is ensured by a flexible candidate-qualification method, which allows for priorities to be assigned to each objective function, offering precise control over the rescheduling process. The applicability of this approach is presented through an example of an extended flexible flow shop manufacturing system. A new test problem containing 16 objective functions has been developed. The effectiveness of the proposed new objective functions and rescheduling method is validated by a simulation model. The obtained numerical results are also presented in this paper. The aim of this study is not to compare different search algorithms but rather to demonstrate the beneficial impact of change-minimizing objective functions within a given search framework. Full article

Urban systems are transforming as artificial intelligence (AI) evolves from automation to Agentic Urban AI (AI systems with autonomous goal-setting and decision-making capabilities), which independently define and pursue urban objectives. This shift necessitates reassessing governance, planning, and ethics. Using a conceptual-methodological approach, this study integrates urban studies, AI ethics, and governance theory. Through a literature review and case studies of platforms like Alibaba’s City Brain and CityMind AI Agent, it identifies early agency indicators, such as strategic adaptation and goal re-prioritisation. A typology distinguishing automation, autonomy, and agency clarifies AI-driven urban decision-making. Three trajectories are proposed: fully autonomous Agentic AI, collaborative Hybrid Urban Agency, and constrained Non-Agentic AI to mitigate ethical risks. The findings highlight the need for participatory, transparent governance to ensure democratic accountability and social equity in cognitive urban ecosystems. Full article