Automatic Learning Improves Human-Robot Interaction in Productive Environments: A Review

Automatic Learning Improves Human-Robot Interaction in Productive Environments: A Review

Mauricio Andres Zamora Hernandez (University of Costa Rica, San Pedro de Montes de Oca, Costa Rica), Eldon Caldwell Marin (University of Costa Rica, San Pedro de Montes de Oca, Costa Rica), Jose Garcia-Rodriguez (University of Alicante, Alicante, Spain), Jorge Azorin-Lopez (Department of Computer Technology, University of Alicante, Alicante, Spain) and Miguel Cazorla (University of Alicante, Alicante, Spain)
Copyright: © 2017 |Pages: 11
DOI: 10.4018/IJCVIP.2017070106
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In the creation of new industries, products and services -- all of which are advances of the Fourth Industrial Revolution -- the human-robot interaction that includes automatic learning and computer vision are elements to consider since they promote collaborative environments between people and robots. The use of machine learning and computer vision provides the tools needed to increase productivity and minimizes delivery reaction times by assisting in the optimization of complex production planning processes. This review of the state of the art presents the main trends that seek to improve human-robot interaction in productive environments, and identifies challenges in research as well as in industrial - technological development in this topic. In addition, this review offers a proposal on the needs of use of artificial intelligence in all processes of industry 4.0 as a crucial linking element among humans, robots, intelligent and traditional machines; as well as a mechanism for quality control and occupational safety.
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Technology Applications

Specifically on the topics related to the Fourth Industrial Revolution, an emphasis is being placed on cybermanufacturing systems, also known as “cybermanufacturing”, being the manufacturing interconnection of the various human elements with complex automated systems, involving computer systems for control and the exchange of information from manufacturing operations and robotic systems; in order to create work models supported with artificial intelligence to improve decision making and anticipation of problematic situations in the production flow (Siddique, Mitchell, O’Grady, & Jahankhani, 2011). In this type of environment, the most natural interconnection between humans and robots is sought, since as mentioned by (Meisner, Isler, & Trinkle, 2008) this can generate environments that minimize stress on operators when using complex robotic systems (Meisner et al., 2008).

Such environments -as mentioned by (Hedelind & Jackson, 2011; Hermann, Pentek, & Otto, 2016; J. Lee, Bagheri, & Jin, 2016) – are strongly related to the concept of automation and data exchange as a core in manufacturing technologies, where technologies such as robotics, systems, cyber physicists, Big Data, and Things Internet are the foundation in building a collaborative environment with people (Hedelind & Jackson, 2011; Hermann et al., 2016; J. Lee, Bagheri, & Kao, 2014).

As the complexity of systems increase, the main element to be considered for the construction of these new integrated production environments are humans, who can make use of technologies of interaction with robots and machines, as it is the case of augmented reality (AR). For example, (Tatic & Tešic, 2017) talk about a thermal energy plant in Bosnia and Herzegovina. The aim is to prevent workers from making mistakes and protect their physical integrity through the use of mobile devices that integrate systems AR, which makes it easier for them to use real-time checklists (Meisner et al., 2008). Cases like these can frequently be found in other investigations.

Continuing with the topics of the previous section, computer vision is an important element to consider in this new manufacture era, since its applications in human-robot interaction can be applied to manufacture for quality control, detection of collisions (Wang, Schmidt, & Nee, 2013), navigation (Hornung, Bennewitz, & Strasdat, 2010) and augmented reality (Makris, Karagiannis, Koukas, & Matthaiakis, 2016). However, implementing these applications requires the application of automatic learning as a whole. As mentioned by (Lee et al., 2016), they require the intervention of operators in order to be able to train artificial intelligence using robots. This requires established mechanisms for control and exchange of information to guarantee high quality mechanisms.

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