NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

An Application-centric Perspective on Industrial Artificial Intelligence

Advances in Artificial Intelligence have made its application increasingly relevant to all types of Information Systems. One area where researchers and practitioners see massive potential is the interface between Artificial Intelligence-empowered Information Systems and industrial processes. This explicit area of Industrial Artificial Intelligence and Industry 4.0 has been a popular topic of recent work and has opened up new research streams and applications. However, given the increasing number of publications, it is difficult to discern where the research field is heading. In our work, we conduct a systematic literature review of 296 scientific articles to provide a comprehensive overview of the current state of Industrial Artificial Intelligence in terms of research streams and application areas. We present both a metadata analysis as well as an application-specific analysis. Our results reveal insights into 14 major application areas as well as several findings on applied algorithms and approaches of Industrial Artificial Intelligence

Advancing automation and robotics technology for the space station and the US economy

In response to Public Law 98-371, dated July 18, 1984, the NASA Advanced Technology Advisory Committee has studied automation and rebotics for use in the space station. The Executive Overview, Volume 1 presents the major findings of the study and recommends to NASA principles for advancing automation and robotics technologies for the benefit of the space station and of the U.S. economy in general. As a result of its study, the Advanced Technology Advisory Committee believes that a key element of technology for the space station is extensive use of advanced general-purpose automation and robotics. These systems could provide the United States with important new methods of generating and exploiting space knowledge in commercial enterprises and thereby help preserve U.S. leadership in space

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments