News

September, 20th: Joaquim Blesa at SEFI'22 Conference.

Joaquim Blesa ,from the SAC Advanced Control Systems research group, presenting the paper Introducing autonomous vehicles into an undergraduate engineering course, at SEFI 2022 (Towards a new future in engineering education, Barcelona). 

AUTHORS: J. Blesa, P. Ponsa, A. Calomarde, J. García and V. Repecho.  

ABSTRACT: Autonomous vehicles (AVs) are of great interest for the automotive industry and are expected to revolutionize mobility and public transportation. The University can contribute to the design and development of autonomous vehicles both in the field of teaching and in research and technology transfer. In this paper, it is described how this topic is introduced in an undergraduate engineering course, “Implementation of Automatic Control Systems (IACS)”. The IACS course is based on project based learning (PBL) and learning by doing methodologies. Several practical examples that correspond to real automatic systems are discussed throughout the course and one of them, a low-cost AV to which a Raspberry pi has been adapted, forms the basis for a final project of the course. The control algorithms are developed on MATLAB/SIMULINK and are sent to the Raspberry through a wireless communication network. The control objective of the system is the automatic guidance of the vehicle through a single lane indoor closed circuit, the detection and identification of different traffic signals and the automatic response to these signals. Students check the behavior of the vehicle and proceed to make improvements. Based on the assessment of the students and the robustness of the autonomous vehicles, it is time to consolidate this type of project within the course. Students that want to get deeper into the matter have the opportunity to do a final degree project related with the AV.

July, 8th: Alejandro Chacón. PhD Thesis Defense.

Alejandro Chacón ,from the ESPE University, presenting the PhD thesis A socio-technical approach for assistants in human-robot collaboration in industry 4.0, at ETSEIB School. In front, the jury, from left to right: Toni Granollers (Universitat de LLeida, UdL, Spain), Francisco Jesús Rodríguez (Universidad de León, Spain) and Anaís Garrell (Universitat Politècnica Catalunya Barcelona TechUPC). PhD Thesis Directors: Cecilio Angulo and Pere Ponsa. 

ABSTRACT: The introduction of technologies disruptive of Industry 4.0 in the workplace integrated through human cyber-physical systems causes operators to face new challenges. These are reflected in the increased demands presented in the operator's capabilities physical, sensory, and cognitive demands. In this research, cognitive demands are the most interesting. In this perspective, assistants are presented as a possible solution, not as a tool but as a set of functions that amplify human capabilities, such as exoskeletons, collaborative robots for physical capabilities, virtual and augmented reality for sensory capabilities. Perhaps chatbots and softbots for cognitive capabilities, then the need arises to ask ourselves: How can operator assistance systems 4.0 be developed in the context of industrial manufacturing? In which capacities does the operator need more assistance?

From the current paradigm of systematization, different approaches are used within the context of the workspace in industry 4.0. Thus, the functional resonance analysis method (FRAM) is used to model the workspace from the sociotechnical system approach, where the relationships between the components are the most important among the functions to be developed by the human-robot team. With the use of simulators for both robots and robotic systems, the behavior of the variability of the human-robot team is analyzed. Furthermore, from the perspective of cognitive systems engineering, the workspace can be studied as a joint cognitive system, where cognition is understood as distributed, in a symbiotic relationship between the human and technological agents. 

The implementation of a case study as a human-robot collaborative workspace allows evaluating the performance of the human-robot team, the impact on the operator's cognitive abilities, and the level of collaboration achieved in the human-robot team through a set of metrics and proven methods in other areas, such as cognitive systems engineering, human-machine interaction, and ergonomics. We conclude by discussing the findings and outlook regarding future research questions and possible developments.