With Industry 5.0, operators’ physical and cognitive behavior is crucial in any field, particularly manufacturing and production lines. To this end, the need to monitor humans performing specific tasks when working alongside robotic systems has grown further. Guaranteeing the well-being of operators sharing a workspace with industrial robots can drastically reduce risky and harmful situations for the operators while leading the robot to adapt to humans fully. For this purpose, monitoring systems can be very helpful in studying the most suitable deep learning methodologies to obtain information from the movements of the individual operator performing a specific task. Therefore, action segmentation can be fundamental to establishing a new and safe way of communication among operators and robots. In this work, a system for segmenting actions performed by operators assembling an industrial object is developed. The public HA4M dataset has been used to train and test temporal action segmentation models by using MS-TCN++ architecture. Highly discriminant features have been extracted from the dataset, and different training approaches based on multimodal data, including RGB and skeletal joints in Depth and RGB resolutions, have been considered. Results show the effectiveness of the proposed system, laying the foundation for further studies for detecting the operators’ actions in the challenging context of Human-Robot Interaction and Collaboration.

This paper introduces a dataset on Human-cobot collaboration for Action Recognition in Manufacturing Assembly (HARMA3). It is a collection of RGB frames, Depth maps, RGB-to-depth-Aligned (RGB-A) frames and Skeleton data relative to actions performed by different subjects in collaboration with a cobot for building an Epicyclic Gear Train (EGT). In particular, 27 subjects executed several trials of the assembly task, which consisted of 7 actions. Data were collected in a laboratory scenario using two Microsoft®Azure Kinect cameras positioned in frontal and lateral positions. The dataset represents a good foundation for developing and testing advanced action recognition as well as action segmentation systems with far-reaching implications beyond human-cobot collaboration. Further potential applications include Computer Vision, Machine Learning, and Smart Manufacturing. Preliminary experiments for action segmentation by applying a state-of-the-art method on features extracted from RGB and skeletal data are presented in this paper, showing high-performance rates.

N owadays, non-destructive techniques (NDT) playa fundamental role in the production industry since early defects detection (EDD) can reduce possible costs and avoid catastrophic failures. Under these aspects, all methods for fast and reliable inspection deserve special attention. This paper proposes a method to detect manufacturing defects or other damage mechanisms without compromising the original condition of the material using active IR thermography and automatic semantic segmentation. The segmentation of defects in composite materials is achieved by using a deep learning algorithm on a high-variance dataset obtained performing lock-in thermography under five different heat source configurations. Experimental results on specimens with known defects have demonstrated that the proposed methodology provides satisfying performances in automatic defect detection.

This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegenerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined.

This paper introduces the control architecture of a platform aimed at promoting good mental health for workers interacting with collaborative robots (cobots). The platform aim is to render industrial production cells capable of automatically adapting their behavior in order to improve the operator’s quality of experience and level of engagement and to minimize his/her psychological strain. In order to achieve such a goal, an extremely rich and complex framework is required. Starting from the identification of the parameters that could influence the collaboration experience, the envisioned human- driven control structure is presented together with a detailed description of the components required to implement such an automated system. Future works will include proper tuning of control parameters with dedicated experimental sessions, together with the definition of organizational and technical guidelines for the design of a mental-health-friendly cobot-based manufacturing workplace.

The widespread availability of network resources and the development of new generation devices allowed the industry to pass into the so-called Industry 4.0 era. In this fourth industrial revolution, Internet of Things (IoT) and robotic systems closely cooperate, reshaping their relations. This way of integration of robotic agents and IoT leads to the concept of Internet of Robotic Things (IoRT). Such disruptive technology opens new possibilities also in research fields other than manufacturing, such as agriculture, health, surveillance, and education. This paper reviews the key technologies of Industry 4.0 and the way they are implemented in IoRT architectures. In addition, it sheds light on the impact of the IoRT on other research fields, focusing on the main open challenges of the integration of robotic technologies into smart spaces.

In this paper, we propose an embedded vision system based on laser profilometry able to get the pose of a vehicle and its relative displacements with reference to the constitutive media of a structured environment. Fundamental equations for laser triangulation are developed and encoded for their actual implementation on an embedded system. It is made of a laser source that projects a line-shaped beam onto the environment and an on-chip camera able to frame the laser light. Images are then sent to the inexpensive Raspberry Pi onboard computer, which is responsible for processing tasks. For the first time, laser profilometry is coupled with the correlation of laser signatures on a low-cost and low-resource processing board for vehicle localization purposes. Several validation tests of the proposed sensor have proven the effectiveness of the system with respect to commercially available sensors such as inductive sensors and standard odometers, which fail when the vehicle crosses path interceptions or its wheels undergo unavoidable slippages. Moreover, further comparisons with other vision-based techniques have also proven the good performances of this embedded system for real-time localization of vehicles.