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

In the new generation of industries, including all the advances introduced by Industry 4.0, human robot interaction (HRI), by means of automatic learning and computer vision, become an important element to accomplish. HRI allows to create collaborative environments between people and robots, avoiding the latter generating a risk of occupational safety. In addition to the automatic systems, the interaction by mean of automated learning processes provides necessary information to increase productivity and minimize delivery response times by helping to optimize complex production planning processes. In this paper, it is presented a review of the technologies necessary to be considered as basic elements in all processes of industry 4.0 as a crucial linking element between humans, robots, intelligent and traditional machines.

[1]  Sung Ho Ha,et al.  Recognizing yield patterns through hybrid applications of machine learning techniques , 2009, Inf. Sci..

[2]  Pingyu Jiang,et al.  A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm , 2016, Knowl. Based Syst..

[3]  Lin Zhang,et al.  Modeling of manufacturing service supply-demand matching hypernetwork in service-oriented manufacturing systems , 2017 .

[4]  Chun-Wu Yeh,et al.  A non-parametric learning algorithm for small manufacturing data sets , 2008, Expert Syst. Appl..

[5]  Kurosh Madani,et al.  A Soft-Computing basis for robots’ cognitive autonomous learning , 2015, Soft Comput..

[6]  Susana Ferreiro,et al.  Comparison of machine learning algorithms for optimization and improvement of process quality in conventional metallic materials , 2011, The International Journal of Advanced Manufacturing Technology.

[7]  Maren Bennewitz,et al.  Efficient vision-based navigation , 2010, Auton. Robots.

[8]  Takeo Kanade,et al.  Computer Vision and Image Understanding Computer Vision for Assistive Technologies , 2022 .

[9]  L. Sudha,et al.  Optimization of process parameters in feed manufacturing using artificial neural network , 2016, Comput. Electron. Agric..

[10]  John Folkesson,et al.  Unsupervised robot learning to predict person motion , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Toyoaki Nishida,et al.  Toward combining autonomy and interactivity for social robots , 2009, AI & SOCIETY.

[12]  Leo van Moergestel,et al.  Assessment of reconfiguration schemes for Reconfigurable Manufacturing Systems based on resources and lead time , 2017 .

[13]  Alper Atmaca,et al.  Implementation of an Overall Design of a Flexible Manufacturing System , 2015 .

[14]  Min Liu,et al.  An incremental extreme learning machine for online sequential learning problems , 2014, Neurocomputing.

[15]  Hamid Jahankhani,et al.  Cybernetic approaches to robotics , 2011, Paladyn J. Behav. Robotics.

[16]  Jeffrey C. Trinkle,et al.  Controller design for human-robot interaction , 2008, Auton. Robots.

[17]  Lihui Wang,et al.  Vision-guided active collision avoidance for human-robot collaborations , 2013 .

[18]  Marc Toussaint,et al.  Learning model-free robot control by a Monte Carlo EM algorithm , 2009, Auton. Robots.

[19]  Rainer Drath,et al.  Industrie 4.0: Hit or Hype? [Industry Forum] , 2014, IEEE Industrial Electronics Magazine.

[20]  László Monostori AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing , 2002 .

[21]  Der-Chiang Li,et al.  Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems , 2008, Expert Syst. Appl..

[22]  Dusan Tatic,et al.  The application of augmented reality technologies for the improvement of occupational safety in an industrial environment , 2017, Comput. Ind..

[23]  Changchun Liu,et al.  An empirical study of machine learning techniques for affect recognition in human–robot interaction , 2006, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Patrick Gebhard,et al.  Exploring a Model of Gaze for Grounding in Multimodal HRI , 2014, ICMI.

[25]  Guglielmo Tamburrini,et al.  Learning robots interacting with humans: from epistemic risk to responsibility , 2007, AI & SOCIETY.

[26]  Mats Jackson,et al.  How to improve the use of industrial robots in lean manufacturing systems , 2011 .

[27]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.

[28]  Boris Otto,et al.  Design Principles for Industrie 4.0 Scenarios , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[29]  Åsa Fast-Berglund,et al.  Relations between complexity, quality and cognitive automation in mixed-model assembly , 2013 .

[30]  Charlie C. L. Wang,et al.  The status, challenges, and future of additive manufacturing in engineering , 2015, Comput. Aided Des..

[31]  Olivia Penas,et al.  Multi-scale approach from mechatronic to Cyber-Physical Systems for the design of manufacturing systems , 2017, Comput. Ind..

[32]  Sotiris Makris,et al.  Augmented reality system for operator support in human–robot collaborative assembly , 2016 .

[33]  Jay Lee,et al.  Introduction to cyber manufacturing , 2016 .

[34]  David de la Fuente,et al.  A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems , 2006, Eng. Appl. Artif. Intell..

[35]  Sang Do Noh,et al.  Smart manufacturing: Past research, present findings, and future directions , 2016, International Journal of Precision Engineering and Manufacturing-Green Technology.