Special Section: Symbiotic Human–Artificial Intelligence Partnership for Next-Generation Factories

摘要

As envisioned by Industry 4.0, the next generation of smart factories and warehouses will highly depend on the collaboration between human and artificial intelligence (AI). This symbiotic partnership can augment human capabilities by providing suggestions, assistance, and explanations as needed—or can utilize direct or indirect human feedbacks in a human-in-the-loop learning framework to enhance AI learning capabilities. This Special Section aims to harvest the latest efforts in fundamental methodologies as well as their applications in human–AI partnership with specific applications for next-generation factories encompassing the design process to manufacturing, production, and inspection.“Seeking Human Help to Manage Plan Failure Risks in Semi-Autonomous Mobile Manipulation” by Al-Hussaini et al. presents a framework to identify the risk associated with a task failure in a shared-autonomy mobile manipulation task and if needed communicate the potential risk (e.g., collision) with a remote operator for seeking guidance. To this end, first, a probabilistic metric temporal logic approach is proposed to encode human-provided alert-trigging conditions. Second, a sensor-agnostic way of generating an uncertainty model of the environment is proposed to estimate uncertainty in 3D workspace models, estimate the probability of collision, and estimate task failure for generating alerts. Third, to enhance the situation awareness and decision making of the human operators, a wide range of visualization tools are integrated in the human–robot interface. Finally, the framework is demonstrated with a use case in which a mobile manipulator performs machine tending and material handling tasks.The paper by Li et al. entitled “The Effect of Different Occupational Background Noises on Voice Recognition Accuracy” demonstrates the importance of developing voice recognition algorithms that are designed for specific occupational settings. A customized auto speech recognition (ASR) model with a denoising module is proposed to investigate the effect of unique background noise and the type of communication associated with different settings. The performance of this system is compared to a regular convolutional neural network (CNN) based voice recognition algorithm under several background noise conditions. The ASR model customized for specific occupational setting outperformed the CNN-based model with an overall performance increase between 14% and 35% across all background noises.“User-Requirements Analysis on Augmented Reality-Based Maintenance in Manufacturing” by Runji et al. is a systematic literature review on augmented reality-based maintenance in manufacturing entities. Reviewing the relevant literature from 2017 to 2021, the specific user needs are categorized as ergonomics, communication, situational awareness, intelligence sources, feedback, safety, motivation, and performance assessment. These predominant user needs are cross-tabulated with the contributing factors (e.g., geographical location), and their results are presented using trend analysis to identify gaps and provide possible future direction.The paper by He et al., entitled “A Convolutional Neural Network-Based Recognition Method of Gear Performance Degradation Mode,” presents a CNN-based stacking incremental deformable residual block network (SIDRBnet) model to identify the gear performance degradation mode. This method converts the vibration signals measured via four different accelerometers to gray scale images. Comparing to a regular CNN, the average pooling layer replaces the fully connected layer, and the large-size convolution kernel is replaced with a small-size convolution kernel. The paper experimentally evaluates the performance of both single channel and multichannel SIDRBnet and demonstrates the superiority of multichannel recognition model.Manjunatha et al. focused on physiological data analysis in physical human–robot interaction for optimal role allocation and load s