Periodic Reporting for period 1 - CISC (Collaborative Intelligence for Safety Critical systems)
Período documentado: 2021-01-01 hasta 2022-12-31
The main objectives of this ITN are to:
1) train researchers with interdisciplinary skills and intersectoral experience in the field of AI driven automation for Industry 4.0; merging the fields of Human Factors and AI in Automation integrated with system safety engineering and ethical and legal aspects of Collaborative intelligence
2) promote academic-industry collaborations; and
3) foster European excellent science. CISC explicitly aims to address the United Nation Sustainable Development Goals (UN SDGs) 4.B (eliminate gender disparities in education and ensure equal access, including people with disabilities), 5.B
(Promote Empowerment of Women through Technology), 8.2 (Diversify, Innovate and Upgrade for Economic Productivity) and 9.5 (Enhance Research and Upgrade Industrial Technologies).
Within the CISC project, different levels of interaction are studied through the use of Live labs. The Live labs are a means to validate research in near-real environments and ensure that the ESRs are exposed to real-world problems. There are multiple Live labs within the project which can be divided into three main classes. First, researchers will focus on direct human robot interaction. In this scenario, the human will oversee or teach a robot to complete a task. CISC will study the programming methods and aim to optimize this teaching process to improve task efficiency and human ergonomics. Secondly, researchers will focus on exploiting the data from a human executed manufacturing process. To do this efficiently, human performance will be modelled considering task complexity. The data generated during the manufacturing process can then be used to improve human performance and signal potential failures and maintenance events. Finally, researchers will study control room tasks, where humans must oversee complex and critical operations. CISC will show how machine learning and data analysis can alleviate stress, predict overloading scenarios and thus aid allow human operators to make judicious decisions under stressful conditions.
In all three scenarios, the human operator remains a central component responsible for the high-level cognitive actions. The CISC project will aim to amplify an operator’s capabilities while maintaining a level of safety and ergonomics. This deliverable will outline the three LIVELABs central to the CISC project. The components, location and activity are described, and research implementation and experiment design are outlined. Each ESR has the opportunity to validate their algorithms/ approach across all LIVELABS however their methods/ approaches are typically tailored to a single main experiment.
Live Lab 1- Human robot collaboration: Live Lab 1 is divided into three different scenarios located in IMR’s pilot factory in Mullingar and in the Faculty of Engineering, University of Kragujevac. This Live Lab will focus on how operators can better interact with robots at various different control levels. The efficiency and safety of these operations will be studied and data concerning human factors, such as attention and comfort etc., collected.
Live Lab 2- Augmenting Human performance: Live Lab 2 is focused on manufacturing operations in a large-scale automotive plant. The project will collect data during manufacturing and model the human operator’s performance versus task complexity. The objective is to exploit this data to enable optimization of human performance while simultaneously predicting anomalies and scheduling maintenance events.
Live Lab 3- Assisting Human decision-making: Live Lab 3 is divided into two different scenarios, firstly utilizing data obtained from a real control room in Yokogawa’s facility servicing the Oil and Gas industry and secondly data collection and exploitation from a controllable simulated environment developed by POLITO (Politecnico di Torino). The objective is to explore alarm flooding events, i.e. when multiple alarms occur simultaneously and how to predict such events and how to ensure the human operator is not overwhelmed.