This paper presents a lightweight MATLAB-based framework with a graphical interface for modeling, 3D simulation, trajectory generation, and experimental validation of a 6-DOF industrial robot. The platform integrates kinematic modeling using the rigidBodyTree structure, animated visualization, and both predefined and user-defined trajectory planning within a unified environment. A central aspect of the proposed approach is the implementation of a ROS-compatible TCP/IP communication protocol that avoids the need for a full ROS core installation while preserving compatibility with ROS-Industrial standards. This enables bidirectional data exchange between MATLAB and the robot controller within a simplified architecture. Communication performance tests indicate round-trip latency in the tens-of-milliseconds range and consistent StateServer update rates, supporting monitoring, trajectory execution, and digital twin synchronization in non-real-time conditions. Experiments conducted on an ABB IRB120 robot demonstrate a close correspondence between simulated and real motion, with RMSE below 0.0075 rad and MAE below 0.0065 rad across all joints. All data are stored in JSON format to support reproducibility and further analysis. By integrating simulation and real robot execution within a modular architecture, the proposed framework provides a practical tool for education, rapid prototyping, and experimental research in industrial robotics, while offering a basis for future extensions toward advanced control strategies and digital twin applications.

Two-wheeled self-balancing robots (TWSBRs) are difficult to control because they are nonlinear, unstable, and underactuated, particularly when balance, velocity regulation, and line tracking must be achieved simultaneously. This paper proposes a hybrid parallel control architecture for a line-following TWSBR operating on straight segments, 90∘ curves, and a 15∘ slope. Balance stabilization is handled by a classical PD loop, while traslational velocity is regulated by an adaptive fuzzy PI controller, and line following is performed with an adaptive fuzzy PD controller. The fuzzy modules adjust the effective gains based on tracking errors, thereby improving robustness to disturbances, sensor noise, and changes in operating conditions. The complete strategy is implemented on a low-cost PIC18F4550 microcontroller. Experiments show that the fuzzy line-following controller reduces the orientation tracking error compared with a conventional controller. At 0.10ms, RMSE decreases from 0.042rad to 0.038rad, and at 0.175ms, it decreases from 0.083rad to 0.066rad. The fuzzy approach also improves IAE (1.317rads to 1.185rads) and ISE (0.242rad2s to 0.153rad2s) at 0.175ms, while maintaining similar maximum error (0.299rad to 0.261rad). Overall, the proposed hybrid scheme achieves better adaptability without retuning. These results support real-time deployment on resource-limited platforms.

Local path tracking is a critical challenge for autonomous vehicles, requiring precise trajectory following under nonlinear dynamics, strict constraints, and real-time execution. While Nonlinear Model Predictive Control (NMPC) has emerged as a leading approach, many existing methods decouple velocity planning from tracking, lack formal stability guarantees, or do not demonstrate feasibility on embedded platforms. We present a unified NMPC framework that integrates curvature-aware velocity adaptation directly into the cost function. The controller makes use of cubic spline paths, recursive feasibility constraints, and Lyapunov-based terminal costs to ensure stability. The nonlinear optimization problem is implemented in CasADi and solved using IPOPT, with warm-starting and efficient discretization techniques enabling real-time performance. Our approach has been validated in the CARLA simulator across a variety of urban scenarios, including straight roads, intersections, and roundabouts. The controller achieves a mean cross-track error of 0.10 m on straight roads, 0.44 m on roundabouts, and 1.36 m on tight intersections, while maintaining smooth control inputs and bounded actuator effort. A curvature-aware cost term yields a 14.4% reduction in lateral tracking error compared to the curvature-unaware baseline. Benchmarking results indicate that the Raspberry Pi 5 outperforms the NVIDIA Xavier AGX by 1.5–1.6×, achieving mean execution times of 38–45 ms across all scenarios. This demonstrates that advanced NMPC can run in real time on low-cost consumer hardware ($80 vs. $700). Systematic ablation studies reveal the critical role of state weighting (Q) and input regularization (R): removing Q degrades tracking by 10% and destabilizes control effort (+54% acceleration, +477% steering), while omitting R induces oscillatory behavior with +907% acceleration effort. Euler integration provides no computational benefit (+8% solver time) while degrading accuracy by 25%, confirming RK4 as strictly superior. Sensitivity analysis via Latin Hypercube Sampling identifies the prediction horizon (N) and discretization timestep (Δt) as dominant parameters. Per-scenario Pareto analysis yields a balanced operating point (N=15, Δt=0.055 s) used for all primary evaluations, while a global sweep identifies a robust alternative (N=12, Δt=0.086 s) suitable for general deployment tuning. This framework bridges the gap between spline-based local planning and stability-guaranteed NMPC, offering a simulation-validated, real-time solution for embedded autonomous driving research. Future work will focus on hardware-in-the-loop and full-vehicle deployment, integration with high-level decision-making, and learning-enhanced MPC.

People affected by vision impairment experience significant challenges in mobility and daily life activities. In this paper, a smart assistive navigation system is proposed to address mobility challenges and to enhance the independence of visually impaired individuals. Three modules are integrated into the proposed system. The vision module detects obstacles and interactive objects such as doors, chairs, people, fire extinguishers, etc. The depth cam-based distance module provides the distance of detected objects and obstacles. The voice module provides auditory feedback to visually impaired individuals about the detected objects and obstacles that fall under the pre-defined threshold distance. Finally, the proposed system is optimized in terms of performance and user experience. Jetson Nano is used to reduce the cost of the overall system; however, it has compatibility issues with many of the latest object detection models. The YOLOv5n model is used considering compatibility for object detection, but it has low Mean Average Precision (mAP) and frame rate. To improve the performance of the vision module, various hyperparameters of YOLOv5n are fine-tuned along with transfer learning to enhance the mAP@50 from the original 0.457 to 0.845 and mAP@50-95 from 0.28 to 0.593. Tensor-RT optimization is employed to increase the frame rate to deploy the model in a real scenario. The real-time experimentation shows that the proposed system successfully alerts users to key objects, hazards, and obstacles which enables independent and confident navigation.

The rise in thermal runaway events within electric vehicle (EV) battery systems requires anticipatory models to predict critical safety failures during operation. This investigation develops a multi-physics digital twin framework that links electrochemical, thermal, and structural domains to replicate the internal dynamics of lithium-ion packs in both normal and faulted modes. Coupled simulations distributed among MATLAB 2024a, Python 3.12-powered three-dimensional visualizers, and COMSOL 6.3-style multi-domain solvers supply refined spatial resolution of temperature, stress, and ion concentration profiles. While the digital twin architecture is designed to accommodate different battery chemistries and pack configurations, the numerical results reported in this study correspond specifically to a lithium NMC-based 4S3P cylindrical cell module. Quantitative benchmarks show that the digital twin identifies incipient thermal deviation with 97.4% classification accuracy (area under the curve, AUC = 0.98), anticipates failure onset within a temporal margin of ±6 s, and depicts spatial heat propagation through three-dimensional isothermal surface sweeps surpassing 120 °C. Mechanical models predict casing strain concentrations of 142 MPa, approaching polymer yield strength under stress load perturbations. A unified operator dashboard delivers diagnostic and prognostic feedback with feedback intervals under 1 s, state-of-health (SoH) variance quantified by a root-mean-square error of 0.027, and mission-critical alerts transmitting with a mean latency of 276.4 ms. Together, these results position digital twins as both diagnostic archives and predictive safety envelopes in the evolution of next-generation EV architectures.

Infrared small target detection has become a research hotspot in recent years. Due to the small target size and low contrast with the background, it remains a highly challenging task. Existing infrared small target detection algorithms are generally implemented on 8-bit low dynamic range (LDR) images, whereas raw infrared sensing images typically possess a 14–16 bit high dynamic range (HDR). Conventional HDR image enhancement methods do not consider the subsequent detection task. As a result, the enhanced LDR images often suffer from overexposure, increased noise levels with higher contrast, and target distortion or loss. Consequently, discriminative features in HDR images that are beneficial for detection are not effectively exploited, which further increases the difficulty of small target detection. To extract target features under these conditions, existing detection algorithms usually rely on large parameter models, leading to an unsatisfactory trade-off between efficiency and accuracy. To address these issues, this paper proposes a novel infrared small target detection framework based on HDR image enhancement (HDR-IRSTD). Specifically, a multi-branch feature extraction and fusion mapping subnetwork (MFEF-Net) is designed to achieve the mapping from HDR to LDR. This subnetwork effectively enhances small targets and suppresses noise while preserving both detailed features and global information. Furthermore, considering the characteristics of infrared small targets, an asymmetric Vision Mamba U-Net with multi-level inputs (AVM-Unet) is developed, which captures contextual information effectively while maintaining linear computational complexity. During training, a bilevel optimization strategy is adopted to collaboratively optimize the two subnetworks, thereby yielding optimal parameters for both HDR infrared image enhancement and small target detection. Experimental results demonstrate that the proposed method achieves visually favorable enhancement and high-precision detection, with strong generalization ability and robustness. The performance and efficiency of the method exhibit a well-balanced trade-off.

This paper describes a real-time system for recognizing voice commands for resource-constrained embedded devices, specifically a PIC microcontroller. While most existing speech ordering support solutions rely on high-performance processing platforms or cloud computation, the system described here performs fully embedded low-power processing locally on the device. Sound is captured through a low-cost MEMS microphone, segmented into short audio frames, and time domain features are extracted (i.e., Zero-Crossing Rate (ZCR) and Short-Time Energy (STE)). These features were chosen for low power and computational efficiency and the ability to be processed in real time on a microcontroller. For the purposes of this experimental system, a small vocabulary of four command words (i.e., “ON”, “OFF”, “LEFT”, and “RIGHT”) were used to simulate real sound-ordering interfaces. The main contribution is demonstrated in the clever combination of low-complex, lightweight signal-processing techniques with embedded neural network inference, completing a classification cycle in real time (under 50 ms). It was demonstrated that the classification accuracy was over 90% using confusion matrices and timing analysis of the classifier’s performance across vocabularies with varying levels of complexity. This method is very applicable to IoT and portable embedded applications, offering a low-latency classification alternative to more complex and resource intensive classification architectures.

This paper explores cost-effective solutions for automated guided vehicle (AGV) through the design and implementation of a low-cost, hoverboard-based line-following AGV tailored for textile manufacturing environments, specifically within sewing plants. The designed AGV leverages the capability of a commercial hoverboard as its mobility platform, significantly reducing development costs while maintaining effective operational performance. Utilizing affordable sensors such as infrared line detectors and ultrasonic sensors, the AGV autonomously navigates pre-defined pathways marked on the factory floor. Its primary function is transporting materials such as fabric bundles and partially or finished products between workstations, addressing common logistical challenges in dynamic and labor-intensive textile production settings. The system is designed for easy integration with both existing plant layouts and information and communication environment, requiring minimal infrastructural changes. Field testing demonstrated the AGV’s reliability, maneuverability, and responsiveness in real-world sewing plant conditions. The proposed solution underscores the potential of retrofitting existing consumer electronics for industrial automation, offering a scalable and economically viable alternative for small- to medium-sized textile enterprises seeking to enhance productivity and workflow efficiency.

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations.

A networked system consists of a collection of interconnected autonomous agents that communicate and interact through a shared communication infrastructure. These agents collaborate to pursue common objectives or exhibit coordinated behaviors that would be difficult or impossible for a single agent to achieve alone. With widespread applications in domains such as robotics, smart grids, and communication networks, the coordination and control of networked systems have become a vital research focus—driven by the complexity of distributed interactions and decision-making processes. Graph-based reinforcement learning (GRL) has emerged as a powerful paradigm that combines reinforcement learning with graph signal processing and graph neural networks (GNNs) to develop policies that are relationally aware, scalable, and adaptable to diverse network topologies. This survey aims to advance research in this evolving area by providing a comprehensive overview of GRL in the context of networked coordination and control. It covers the fundamental principles of reinforcement learning and graph neural networks, examines state-of-the-art GRL models and algorithms, reviews training methodologies, discusses key challenges, and highlights real-world applications. By synthesizing theoretical foundations, empirical insights, and open research questions, this survey serves as a cohesive and structured resource for the study and advancement of GRL-enabled networked systems.