Due to their high flexibility, large workspace, and high repeatability, industrial robots are widely used in roughing and semifinishing fields. However, their low machining accuracy and low stability limit further development of industrial robots in the machining field, with low stiffness being the most significant factor. The stiffness of industrial robots is affected by the joint deformation, transmission mechanism, friction, environment, and coupling of these factors. Moreover, the stiffness of a robot has a nonlinear distribution throughout the workspace, and external forces during processing cause irregular deviations of the robot, thereby affecting the machining accuracy and surface quality of the workpiece. Many scholars have researched identifying the stiffness of industrial robots and have proposed methods for improving the performance of industrial robots, mainly by optimizing the body structure of the robot and compensating for deformation errors with stiffness models. This paper reviews recent research on the stiffness modelling of industrial robots, which can be broadly classified as finite element analysis (FEA), matrix structure analysis (MSA), and virtual joint modelling (VJM) methods. Each method is studied from three aspects: algorithms, implementation, and limitations. In addition, common measurement techniques have been introduced for measuring deformation. Further research directions are also discussed.

In autonomous driving systems, cameras and Light Detection and Ranging (LIDAR) are two common sensors for object detection. But both sensors can be severely affected by adverse weather. With the development of radar technology, the emergence of the 4D radar gives a more robust solution for sensor fusion strategies in 3D object detection tasks. This study proposes a two-level 4D radar and camera fusion model called TL-4DRCF, which performs a two-level fusion of 4D radar and camera information at the data and feature levels. In the data-level fusion stage, the radar point cloud is projected onto the image and fed as additional information to the image into the EarlyFusion-Net (EF-Net), which is the network designed for simultaneous extraction of point cloud and image features. In the feature-level fusion stage, the Radar-Camera Alignment (RCA) module is proposed to accurately correlate point cloud voxels and pixel-level image features while consuming less inference time. The correlated features are used to predict the class and location of the object through a standard 3D detection framework. The proposed TL-4DRCF was validated on the View-of-Delft (VoD) dataset and the VoD-Fog dataset performed by artificial fog processing. The experimental results show that the proposed model outperforms the baseline method PointPillars on the VoD dataset by 3.8% mAP and the LIDAR-camera-based method MVX-Net in the driving corridor area of the VoD-Fog dataset by 0.39% mAP.

The existing fault diagnosis of harmonic drives is difficult to deploy vibration sensors, and the diagnosis accuracy is insufficient only using current signal. Therefore, we propose an intelligent fault diagnosis method using a multimodal collaborative meta network (MCMN) with severely missing modality. First, multimodal data of the harmonic drive were collected to analyze. Second, a feature reconstruction network is used to achieve a unified model to handle missing modalities in testing. Besides, the uncertainty assessment is used as a feature regularization network to overcome data bias. Third, the inference of the meta-learning is used to obtain the generated weights, and carried out the joint optimization. Finally, MCMN is optimized by using the approximate lower bound of Monte Carlo. Experimental results show that the accuracy of MCMN is 93.75% in the case of full-modalities training data and the severely missing modalities testing data, which is better than the existing method.

Industrial robots are regularly being employed for machining tasks. As a key machining parameter, dynamic stiffness has a significant influence on robotic machining performance. This paper examines two experimental approaches for obtaining the dynamic stiffness of an industrial robot. One approach is based on experimental modal analysis. The other approach is a direct calculation with the acquisition of excitation forces and vibration displacement. Different excitation frequencies are planned to stimulate an industrial robot. The dynamic stiffnesses obtained by the two approaches are thoroughly evaluated. Large deviation appears between the dynamic stiffness measured by two approaches. The factors that result in variations in the measurements are discussed.

The influence of matching between robots’ social cues on users’ social perceptions should be investigated systematically to better fit robots to their occupational roles. In this study, an experiment with 69 older and middle-aged participants was conducted to explore the effects of the voice and lighting color of a home healthcare robot on users’ social perception, which was measured by the Robotic Social Attributes Scale (RoSAS). The results indicated that voice and lighting color significantly affected social perceptions of the healthcare robot. Specifically, the adopted robot received high warmth ratings when it had an adult female voice or a child voice, whereas it received high competence ratings when it had an adult male voice. The robot received a high warmth rating and a high competence rating when warm and cool lighting were used, respectively, as visual feedback. Furthermore, a mismatch in the robot’s voice and lighting color was discovered to evoke feelings of discomfort. The findings of this study can be used as a reference to design robots with acceptable social perception and to expand the roles of social robots in the future.