Sotiris Aivaliotis

The automotive industry is one of the most automatized industries, employing more than one million robots worldwide. Although several steps in car production are completely automated, many steps are still carried out by operators, especially in tasks requiring high dexterity. Additionally, customization and deployability are still pending issues in this industry, where a real collaboration between robots and operators would increase the reconfigurability of the assembly lines. This paper presents an innovative robotic cell focused on the motor and gearbox assembly, including collaborative industrial robots and autonomous mobile manipulators along the different assembly stations. The design also incorporates a human-centered approach, with an enhanced human interface to facilitate the interaction with operators with the complete robotic cell. The proposed approach has been deployed and validated on a real automotive industrial scenario, obtaining promising metrics and results.

Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts by modern industries towards embracing the latest digitalization trends. Current inspection activities are often based on the manual expertise of operators, which has been proven to be time-consuming. This paper presents a voice-enabled ROS2 framework towards enhancing the collaboration of robots and operators under quality inspection activities. A robust ROS2-based architecture is adopted towards supporting the orchestration of the process execution flow. Furthermore, a speech recognition application and a quality inspection solution are deployed and integrated to the overall system, showcasing its effectiveness under a case study deriving from the automotive industry. The benefits of this voice-enabled ROS2 framework are discussed and proposed as an alternative way of inspecting parts under human–robot collaborative environments. To measure the added value of the framework, a multi-round testing process took place with different parameters for the framework’s modules, showcasing reduced cycle time for quality inspection processes, robust HRI using voice-based techniques and accurate inspection.

This paper presents an Augmented Reality software suite aiming at supporting the operator’s interaction with flexible mobile robot workers. The developed AR suite, integrated with the shopfloor’s Digital Twin, provides the operators’ a) virtual interfaces to easily program the mobile robots b) information on the process and the production status, c) safety awareness by superimposing the active safety zones around the robots and d) instructions for recovering the system from unexpected events. The application’s purpose is to simplify and accelerate the industrial manufacturing process by introducing an easy and intuitive way of interaction with the hardware, without requiring special programming skills or long training from the human operators. The proposed software is developed for the Microsoft’s HoloLens Mixed Reality Headset 2, integrated with Robotic Operation System (ROS), and it has been tested in a case study inspired from the automotive industry.

Manufacturing systems are making a shift towards intelligent and automated detection systems in order to save cost and time in different phases of the product lifecycle as well as keep the production’s quality levels high. Currently, defects detection is based on either the active involvement of the human operator or the devotement of a significant amount of time in order to train models that utilize machine learning techniques. In this context, this study presents an automated optical system, which is able to detect and classify aesthetical defects in different sizes, shapes and positions right after the casting process. A lightweight and robust architecture is adopted towards supporting different products. Also, an advanced user interface is implemented on top of a web application. The aim is to support the operator and provide awareness of the process status by visualizing information related to production and quality aspects. A case study deriving from the automotive industry has been used in order to test the accuracy of the proposed optical system in metal parts. The benefits of the proposed approach are discussed and proposed as a more efficient way of inspecting the quality of finished products under the scope of Industry 4.0.

This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.

Robotic flexibility in industry is becoming more and more relevant nowadays, especially with the rise of the Industry 4.0 concept. This paper presents a smart execution control framework for enabling the autonomous operation of flexible mobile robot workers. These robot resources are able to autonomously navigate the shopfloor, undertaking multiple operations while acting as assistants to human operators. To enable this autonomous behavior, the proposed framework integrates robot perception functions for the real-time shopfloor and process understanding while orchestrating the process execution. A Digital World Model is deployed synthesizing the sensor data coming from multiple 2D and 3D sensors from the shopfloor. This model is consumed for the perception functions enabling the real-time shopfloor and process perception by the robot workers. This smart control system has been applied and validated in a case study from the automotive sector.