To interact with humans more precisely and naturally, social robots need to “perceive” human engagement intention, especially need to recognize the main interaction person in multi-person interaction scenarios. By analyzing the intensity of human engagement intention (IHEI), social robots can distinguish the intention of different persons. Most existing research in this field mainly focus on analyzing whether a person has the intention to interact with the robot while lack of analysis of IHEI. In this regard, this paper proposes an approach for recognizing the engagement intention intensity. Four categories of visual features, including line of sight, head pose, distance and expression of human, are captured, and a CatBoost-based machine learning model is applied to train an optimal classifier for predicting the IHEI on the dataset. The experimental results show that this classifier can effectively predict the IHEI that can be applied into real human–robot interaction scenarios. Moreover, the proposed model is an interpretable machine learning model, where interpretability analysis on the trained classifier has been done to explore the deep associations between input features and engagement intention, thereby providing robust and effective robot social decision-making.

Title: New Method of Path Optimization for Medical Logistics Robots | Keywords: medical logistics robot, multiple steps traveling salesman problem model, improved ant colony optimization (IACO), refilling model | Author: Hui Jin, Qingsong He, Miao He, Fangchao Hu, and Shiqing Lu

The construction and prediction of health indicators (HIs) are crucial for maintaining mechanical equipment containing rolling bearings. Conventional HI construction methodologies often rely on either extensive feature engineering or indirect signal transformation, which may hinder adaptability in diverse operating conditions. To address this, we propose a linear encoding composite degradation-aware CNN-Transformer network (LECDA-Net). It constructs the HI directly from raw multi-axis vibration signals without the need for handcrafted features or complex physical models, and, through linear positional encoding and a composite degradation-aware loss function, ensures that the constructed HI is not only consistent with data-driven patterns but also aligns with physical degradation laws. First, vibration data from the X and Y axes of the bearings undergo low-pass filtering. These filtered signals are then individually subjected to linear positional encoding and fed into LECDA-Net for HI construction. The constructed HI from each axis are subsequently fused through weighted aggregation to produce the overall bearing HI. Comparative experiments show that, compared with convolutional neural network (CNN)-Transformer, CNN-Transformer-DA, deep convolutional autoencoder, convolutional autoencoder, PCA, and DDA methods, LECDA-Net achieves remarkable performance improvements. The constructed HI demonstrates clear advantages in monotonicity, correlation, and overall composite performance. Following the successful construction of a reliable bearing HI, health index prediction based on historical data plays a key role in proactive equipment maintenance. Although existing prediction methods can achieve high accuracy, the evaluation of prediction goodness of fit has received only limited attention. Therefore, we designed an AuTanh-bidirectional long short-term memory (BiLSTM) method, introducing channel-wise adaptive learnable parameters to address the issue that traditional activation functions use scalar parameters for all channels, resulting in poor linear fitting. This method incorporates channel-wise learnable parameters in the AuTanh layer, enhancing the model’s fitting ability through a flexible nonlinear activation mechanism. Compared with BiLSTM, BiLSTM-DA, LSTM, gated dual attention unit, GAU, GAHAU, and temporal convolutional network models, the proposed AuTanh-BiLSTM model demonstrates lower error and superior fitting performance.

Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate the implementation of effective preventive maintenance strategies. This article proposed a novel RUL-centric data augmentation method, designated as DF-MDAGRU, for the purpose of predicting the remaining useful life (RUL) of bearings. This model is based on an encoder–decoder framework that integrates time–frequency domain feature enhancement with multidimensional dynamic attention gated recurrent units for feature extraction. This method enhances time–frequency domain features through the Discrete Wavelet Downsampling module (DWD) and Convolutional Fourier Residual Block (CFRB). This method employs a Multiscale Channel Attention Module (MS-CAM) and a Multiscale Convolutional Spatial Attention Mechanism (MSSAM) to extract channel and spatial feature information. Finally, the output predictions are processed through linear regression to achieve the final RUL estimation. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches on the FEMETO-ST and XJTU datasets.