The successful deployment of AI solutions in manufacturing
environments hinges on their security, safety and reliability which
becomes more challenging in settings where multiple AI systems
(e.g., industrial robots, robotic cells, Deep Neural Networks
(DNNs)) interact as atomic systems and with humans. To guarantee
the safe and reliable operation of AI systems in the shopfloor,
there is a need to address many challenges in the scope of complex,
heterogeneous, dynamic and unpredictable environments.
Specifically, data reliability, human machine interaction,
security, transparency and explainability challenges need to be
addressed at the same time. Recent advances in AI research (e.g.,
in deep neural networks security and explainable AI (XAI) systems),
coupled with novel research outcomes in the formal specification
and verification of AI systems provide a sound basis for safe and
reliable AI deployments in production lines. Moreover, the legal
and regulatory dimension of safe and reliable AI solutions in
production lines must be considered as well.To address some of the
above listed challenges, fifteen European Organizations collaborate
in the scope of the STAR project, a research initiative funded by
the European Commission in the scope of its H2020 program (Grant
Agreement Number: 956573). STAR researches, develops, and validates
novel technologies that enable AI systems to acquire knowledge in
order to take timely and safe decisions in dynamic and
unpredictable environments. Moreover, the project researches and
delivers approaches that enable AI systems to confront
sophisticated adversaries and to remain robust against security
attacks.This book is co-authored by the STAR consortium members and
provides a review of technologies, techniques and systems for
trusted, ethical, and secure AI in manufacturing. The different
chapters of the book cover systems and technologies for industrial
data reliability, responsible and transparent artificial
intelligence systems, human centered manufacturing systems such as
human-centred digital twins, cyber-defence in AI systems, simulated
reality systems, human robot collaboration systems, as well as
automated mobile robots for manufacturing environments. A variety
of cutting-edge AI technologies are employed by these systems
including deep neural networks, reinforcement learning systems, and
explainable artificial intelligence systems. Furthermore, relevant
standards and applicable regulations are discussed. Beyond
reviewing state of the art standards and technologies, the book
illustrates how the STAR research goes beyond the state of the art,
towards enabling and showcasing human-centred technologies in
production lines. Emphasis is put on dynamic human in the loop
scenarios, where ethical, transparent, and trusted AI systems
co-exist with human workers. The book is made available as an open
access publication, which could make it broadly and freely
available to the AI and smart manufacturing communities.
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