Reliable anomaly detection and isolation are essential in safety-critical monitoring. Conventional principal component analysis (PCA) methods treat data from different sensors uniformly, which limits their ability to capture localized anomalies. This study proposes an Attention-PCA (A-PCA) framework that integrates spatial and temporal attention with PCA, enabling adaptive reweighting of sensors before projection. The PCA residuals are monitored using a multivariate exponentially weighted moving average (MEWMA) chart, with control limits estimated via kernel density estimation (KDE) to improve robustness under non-Gaussian conditions. Furthermore, anomaly isolation is enhanced through local interpretable model-agnostic explanations (LIME) applied in the PCA residual subspace, which provide clear sensor-level attribution and highlight variable-specific contributions to abnormal conditions. The approach is validated on a wheelchair posture dataset with three seat pressure sensors and one backrest ultrasonic sensor, showing effectiveness in detecting and isolating unbalanced seating conditions. Results demonstrate that A-PCA-MEWMA outperforms conventional PCA- and A-PCA-based monitoring schemes that rely on Hotelling’s T2 statistic and the squared prediction error (SPE). With both parametric and KDE thresholds, the proposed method achieves higher accuracy, reduced false alarms, and reliable isolation of right, left-, and forward-leaning abnormal postures. These findings confirm the effectiveness of combining attention mechanisms with explainable AI for practical and interpretable anomaly detection in human-centered monitoring systems.
This work presents a learning-based motion control framework for automating high-frequency (HF) brazing of copper tube joints, a repetitive yet unstructured task still dominated by manual operation on refrigerator manufacturing lines. We develop a digital-twin training stack using PyBullet and an RB5850 manipulator model, and formulate approach-braze-retreat behaviors as a continuous Cartesian control problem. A PPO actor-critic with convolutional visual encoders consumes RGB inputs (3x240x480) and outputs fine-grained end-effector increments (0.01 m resolution). To stabilize updates, we employ clipping-based surrogate objectives and separated learning rates for actor and critic, together with a replay buffer and mini-batch training. Simulation accelerates data collection and improves sample efficiency, yielding stable policy improvement within a few hundred epochs in simulation with monotonically increasing episodic rewards. We describe system integration toward real-world deployment, including induction-heating end-effector design and multimodal sensing (thermal/RGB/distance) for reward shaping and quality assurance. Ongoing work targets transfer from sim-to-real, closed-loop temperature/position control during brazing, and quantitative weld-quality evaluation under fixed-posture testbeds. The results indicate that PPO with a task-aware digital twin is a promising path to robust, generalizable HF brazing automation across product variants while reducing dependence on expert operators
This paper presents a motion planning approach designed to achieve energy efficiency in redundant robotic systems. The strategy proposed in this work is based on the optimization of the position of one of the redundant joints of the robot in each of the way points that define the task, while maintaining a fixed execution time. The configuration of the robot is then obtained through inverse kinematics, and the final trajectory is computed by interpolating the optimal joint positions of the robot in consecutive way points. Numerical and experimental results validate the proposed approach using a manipulator with seven degrees of freedom, and demonstrating the feasibility of the strategy in enhancing energy efficiency in redundant robots.
Accurate power measurement is essential in precision mechatronic devices, especially in embedded platforms where energy efficiency and reliability are critical. However, sensors like the INA219 are susceptible to inductive electromagnetic interference (EMI), often producing noisy power readings near motors or switching converters. This work presents a lightweight RMS-based filtering technique that estimates effective power via numerical integration over fixed sampling windows. Designed for real-time microcontroller implementation, the method avoids hardware filters and minimizes computational load. Validation is conducted on a solar-powered environmental monitoring robotic platform (The RaccoonBot) operating under strong EMI. Results show improved signal stability and fidelity compared to unfiltered and moving-average methods. The proposed approach enables reliable power sensing through embedded computation alone, offering a scalable solution for EMI-exposed, energy-constrained environments.
This work aims to present a new mapping methodology for AGV (Automated Guided Vehicle) systems. The goal is to achieve vehicle positioning through a PID (Proportional, Integral, and Derivative) controller, without requiring physical interaction with the path to be followed. The article provides a broad overview of data processing and applying techniques related to achieving accurate positioning without external feedback. This article presents satisfactory results, even though some sensor limitations must be considered.
