In recent years, multi-robot control systems and service robots equipped with graphical computing have been introduced in various application scenarios. However, the long-term operation of VSLAM calculation leads to reduced energy efficiency of the robot, and accidental localization failure still persists in large-scale fields with dynamic crowds and obstacles. This study proposes an EnergyWise multi-robot system based on ROS that actively determines the activation of VSLAM using real-time fused localization poses by an innovative energy-saving selector algorithm. The service robot is equipped with multiple sensors and utilizes the novel 2-level EKF method and incorporates the UWB global localization mechanism to adapt to complex environments. During the COVID-19 pandemic, three disinfection service robots were deployed to disinfect a large, open, and complex experimental site for 10 days. The results demonstrated that the proposed EnergyWise multi-robot control system successfully achieved a 54% reduction in computing energy consumption during long-term operations while maintaining a localization accuracy of 3 cm.

The challenge in developing an AI deep learning model for motor health diagnosis is hampered by the lack of sufficient and representative datasets, leading to considerable time and resource consumption in research. Therefore, this paper focuses on the analysis of the second harmonic component fault characteristic induced by inter-turn short circuits (ITSCs) in phase voltages. First, it establishes a coil inter-turn short-circuit fault (ITSCF) model of the motor to identify the twice-frequency q-axis voltage error characteristics. Subsequently, it develops simulation programs by integrating control and fault models in MATLAB/Simulink/Simscape to observe and analyze the q-axis voltage and circulating current errors caused by the short circuit. Finally, a discrete wavelet transform method is established to analyze the q-axis synchronous reference frame voltage. By applying the energy-based method to extract the twice-frequency voltage error characteristics, the approach successfully detects the error features and confirms ITSCF in the motor. The contributions of this paper include not only the development of an ITSCF characteristic model for the motor but also the successful application of wavelet transform to effectively analyze the time-frequency characteristics of its signals. This approach can serve as a valuable reference for the design of deep learning models in future AI applications.

This study proposes a multi-objective optimization scheduling method for edible oil transportation in smart manufacturing, focusing on centralized control and addressing challenges such as complex pipelines and shared resource constraints. The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. Additionally, the transport time is calculated using the Hagen–Poiseuille law by considering the viscosity coefficients of different oil types. To minimize both cost and time, task execution sequences are optimized based on a Pareto front approach. A 3D digital model of the pipeline system was developed using C#, SolidWorks Professional, and the Helix Toolkit V2.24.0 to simulate a realistic production environment. This model is integrated with a 3D visual human–machine interface(HMI) that displays the status of each task before execution and provides real-time scheduling adjustment and decision-making support. Experimental results show that the proposed method improves scheduling efficiency by over 43% across various scenarios, significantly enhancing overall pipeline transport performance. The proposed method is applicable to pipeline scheduling and transportation management in digital factories, contributing to improved operational efficiency and system integration.

This study proposes a data re-indexing method that integrates the Fast Fourier Transform (FFT) and a Genetic Algorithm (GA) to reconstruct features of back electromotive force (Back-EMF) from ultra-low sampling rate data (ULSD) obtained via a low-speed serial interface (e.g., RS-485) embedded in motor drives. Initially, a demagnetization model is developed to extract Back-EMF features. Subsequently, the ULSD is re-indexed based on varying numbers of sampling points per electrical period, and corresponding waveforms are reconstructed. FFT is applied to analyze rearranged waveforms, and two adjacent sampling configurations with the highest fundamental frequency amplitudes are identified. Finally, the GA optimizes the index assignment of the ULSD using these two candidate configurations, thereby enhancing the reconstruction of the Back-EMF waveform. Experimental results demonstrate that the proposed ULSD re-indexing method reconstructs Back-EMF waveforms. These waveforms are then used as inputs to a one-dimensional convolutional autoencoder (1-D CAE) combined with a multilayer perceptron (MLP) for demagnetization fault (DMF) diagnosis. The proposed approach achieves a high diagnostic accuracy of 96.7%, while reducing noise and sampling errors, and without incurring additional external signal capture interfaces (ESCIs) hardware costs. These findings validate the feasibility and effectiveness of applying rearranged ULSD for low-cost and reliable fault diagnosis in IPMSM systems.

Over the past few years, due to an aging population and a longer life expectancy, traditional high-oil, high-calorie, and high-temperature culinary concepts have been widely replaced by healthier, low-temperature heating methods. However, while the key device of the Sous-Vide heating system ordinarily operates at full power to achieve the target temperature, pump speed control is not currently considered within the water heating and circulating system framework. This study develops a model for a water heating and circulating system and examines the characteristics of the lowest power point and pump speed. Building upon these results, we present the LPPT control method as a means of optimizing input power for heating. The effectiveness of this method is supported by simulations and experiments, which demonstrate a significant reduction in energy consumption. The control concept calculates the real-time input power based on the input voltage and current, and it can achieve the most efficient input power by perturbing the pump speed. It is demonstrated that applying LPPT to daily pot capacity reduces the Sous-Vide Cooker’s input power by up to 17% and achieves efficiency optimization control by removing the need to calculate the foods and other parameters of the water heating and circulating system environment.