Automation

This research presents a study on enhancing the localization and orientation accuracy of indoor Autonomous Guided Vehicles (AGVs) operating under a centralized, camera-based control system. We investigate and compare the performance of two Extended Kalman Filter (EKF) configurations: a standard EKF and a novel Blended EKF. The research methodology comprises four primary stages: (1) Sensor bias correction for the camera (CAM), Dead Reckoning, and Inertial Measurement Unit (IMU) to improve raw data quality; (2) Calculation of sensor weights using the Inverse-Variance Weighting principle, which assigns higher confidence to sensors with lower variance; (3) Multi-sensor data fusion to generate a stable state estimation that closely approximates the ground truth (GT); and (4) A comparative performance evaluation between the standard EKF, which processes sensor updates independently, and the Blended EKF, which fuses CAM and DR (Dead Reckoning) measurements prior to the filter’s update step. Experimental results demonstrate that the implementation of bias correction and inverse-variance weighting significantly reduces the Root Mean Square Error (RMSE) across all sensors. Furthermore, the Blended EKF not only achieved a lower RMSE in certain scenarios but also produced smooth trajectories similar to or less than the standard EKF in some weightings. These findings indicate the significant potential of the proposed approach in developing more accurate and robust navigation systems for AGVs in complex indoor environments.

Foggy weather critically undermines the autonomous perception capabilities of unmanned aerial vehicles (UAVs) by degrading image contrast, obscuring object structures, and impairing small target recognition, which often leads to significant performance deterioration in conventional detection models. To address these challenges in automated UAV operations, this study introduces Hazy Aware-YOLO (HA-YOLO), an enhanced detection framework based on YOLO11, specifically engineered for reliable object detection under low-visibility conditions. The proposed model incorporates wavelet convolution to suppress haze-induced noise and enhance multi-scale feature fusion. Furthermore, a novel Context-Enhanced Hybrid Self-Attention (CEHSA) module is developed, which sequentially combines channel attention aggregation (CAA) with multi-head self-attention (MHSA) to capture local contextual cues while mitigating global noise interference. Extensive evaluations demonstrate that HA-YOLO and its variants achieve superior detection precision and robustness compared to the baseline YOLO11, while maintaining model efficacy. In particular, when benchmarked against state-of-the-art detectors, HA-YOLO exhibits a better balance between detection accuracy and complexity, offering a practical and efficient solution for real-world autonomous UAV perception tasks in adverse weather.

Quantitative Feedback Theory (QFT) enables the control system to guarantee stability and performance in the presence of plant uncertainty, thus offering a quantitative and less conservative framework for designing robust yet practical controllers. The presented work investigates a single-stage constraint optimization-based approach for synthesizing controllers for the ship roll stabilization. The typical QFT loop shaping is a manual two-stage procedure that demands a proficient understanding of loop-shaping principles on Nichols charts. The proposed procedure simplifies the QFT synthesis process by introducing a single-stage method that allows for concurrent synthesis of both the QFT controller and pre-filter. The present work considers the synthesis of fractional order controllers (using the FOMCON toolbox). The proposed method also enables the designer to pre-specify the controller architecture at the beginning of the design procedure. A comparative analysis with the controllers obtained using the QFT toolbox, Ziegler–Nichols, ${\mathcal{H}}_{\infty }$, IMC, and MPC have also been presented in the work. The implementation has been carried out for the ship roll stabilization, which is one of the critical problems in marine engineering, as it directly impacts the vessel safety, operational efficiency, and passenger comfort, wherein excessive roll can lead to reduced propulsion efficiency. The obtained results highlight that the proposed controller performs better than the benchmark controllers, and Monte Carlo simulations have also been included to support the results.