scholarly journals A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human–Robot Interaction

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7210
Author(s):  
Christoph Scholl ◽  
Andreas Tobola ◽  
Klaus Ludwig ◽  
Dario Zanca ◽  
Bjoern M. Eskofier

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human–robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human–robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5mm by monitoring the data quality, adding an additional layer of safety for human–robot interaction.

Author(s):  
Mauricio Andres Zamora Hernandez ◽  
Eldon Caldwell Marin ◽  
Jose Garcia-Rodriguez ◽  
Jorge Azorin-Lopez ◽  
Miguel Cazorla

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.


2018 ◽  
pp. 2014-2024
Author(s):  
Mauricio Andres Zamora Hernandez ◽  
Eldon Caldwell Marin ◽  
Jose Garcia-Rodriguez ◽  
Jorge Azorin-Lopez ◽  
Miguel Cazorla

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.


2020 ◽  
Vol 1 (4) ◽  
pp. 179-186
Author(s):  
Matthew Studley ◽  
Alan Winfield

Abstract Purpose of Review Industry is changing; converging technologies allow a fourth Industrial Revolution, where it is envisaged that robots will work alongside humans. We investigate how the research community is responding to the ethical, legal, and social aspects of industrial robots, with a primary focus on manufacturing industry. Recent Findings The literature shows considerable interest in the impact of robotics and automation on industry. This interest spans many disciplines, which is to be expected given that the ELS impacts of industrial robotics may be profound in their depth and far-reaching in their scope. Summary We suggest that the increasing importance of human-robot interaction (HRI) reduces the differentiation between industrial robotics and other robotic domains and that the main challenges to successful adoption for the benefit of human life are above all political and economic. Emerging standards and legal frameworks may scaffold this success, but it is apparent that getting it wrong might have repercussions that last for generations.


2019 ◽  
Vol 374 (1771) ◽  
pp. 20180024 ◽  
Author(s):  
Emily S. Cross ◽  
Ruud Hortensius ◽  
Agnieszka Wykowska

Amidst the fourth industrial revolution, social robots are resolutely moving from fiction to reality. With sophisticated artificial agents becoming ever more ubiquitous in daily life, researchers across different fields are grappling with the questions concerning how humans perceive and interact with these agents and the extent to which the human brain incorporates intelligent machines into our social milieu. This theme issue surveys and discusses the latest findings, current challenges and future directions in neuroscience- and psychology-inspired human–robot interaction (HRI). Critical questions are explored from a transdisciplinary perspective centred around four core topics in HRI: technical solutions for HRI, development and learning for HRI, robots as a tool to study social cognition, and moral and ethical implications of HRI. Integrating findings from diverse but complementary research fields, including social and cognitive neurosciences, psychology, artificial intelligence and robotics, the contributions showcase ways in which research from disciplines spanning biological sciences, social sciences and technology deepen our understanding of the potential and limits of robotic agents in human social life. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.


Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5976
Author(s):  
Inês Soares ◽  
Marcelo Petry ◽  
António Paulo Moreira

The world is living the fourth industrial revolution, marked by the increasing intelligence and automation of manufacturing systems. Nevertheless, there are types of tasks that are too complex or too expensive to be fully automated, it would be more efficient if the machines were able to work with the human, not only by sharing the same workspace but also as useful collaborators. A possible solution to that problem is on human–robot interaction systems, understanding the applications where they can be helpful to implement and what are the challenges they face. This work proposes the development of an industrial prototype of a human–machine interaction system through Augmented Reality, in which the objective is to enable an industrial operator without any programming experience to program a robot. The system itself is divided into two different parts: the tracking system, which records the operator’s hand movement, and the translator system, which writes the program to be sent to the robot that will execute the task. To demonstrate the concept, the user drew geometric figures, and the robot was able to replicate the operator’s path recorded.


2009 ◽  
Author(s):  
Matthew S. Prewett ◽  
Kristin N. Saboe ◽  
Ryan C. Johnson ◽  
Michael D. Coovert ◽  
Linda R. Elliott

2010 ◽  
Author(s):  
Eleanore Edson ◽  
Judith Lytle ◽  
Thomas McKenna

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