Cognitive robotics software development aspects based on experiments of future software engineers

Machine learning algorithms are widely used in the domain of robotics. In particular, applications using machine learning and artificial intelligence algorithms have led to promising results in industrial applications, cognitive robotics and thus gained attention in recent years. In this context, the purpose of this article is to present the technologies and architectures used in the design and development of cognitive robots by students. This study highlights the difficulties encountered by future engineers in developing research projects in robotics.

[1]  Emília Pietriková,et al.  Potential of Low Cost Motion Sensors Compared to Programming Environments , 2018 .

[2]  Tamás Haidegger,et al.  Handover Process of Autonomous Vehicles – Technology and Application Challenges , 2019, Acta Polytechnica Hungarica.

[3]  I. Poggi,et al.  Uncertain Words, Uncertain Texts. Perception and Effects of Uncertainty in Biomedical Communication , 2019, Acta Polytechnica Hungarica.

[4]  Jie Ma,et al.  A Cross-Platform Tactile Capabilities Interface for Humanoid Robots , 2016, Front. Robot. AI.

[5]  Peter Baranyi,et al.  Cognitive infocommunications: CogInfoCom , 2010, 2010 11th International Symposium on Computational Intelligence and Informatics (CINTI).

[6]  Jozef Juhar,et al.  Speech Technologies for Advanced Applications in Service Robotics , 2013 .

[7]  Miroslav Macik,et al.  Cognitive Aspects of Spatial Orientation  , 2018, Acta Polytechnica Hungarica.

[8]  Angelo Cangelosi,et al.  An open-source simulator for cognitive robotics research: the prototype of the iCub humanoid robot simulator , 2008, PerMIS.

[9]  Attila Magyar,et al.  Voice Controlled Humanoid Robot based Movement Rehabilitation Framework , 2018, 2018 9th IEEE International Conference on Cognitive Infocommunications (CogInfoCom).

[10]  Batu Akan,et al.  Towards industrial robots with human-like moral responsibilities , 2010, 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[11]  Pierre Blazevic,et al.  The NAO humanoid: a combination of performance and affordability , 2008, ArXiv.

[12]  György Molnár,et al.  Smart devices, smart environments, smart students - A review on educational opportunities in virtual and augmented reality learning environments , 2019, 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom).

[13]  Ravi Kumar Mandava,et al.  Dynamic Motion Planning Algorithm for a Biped Robot Using Fast Marching Method Hybridized with Regression Search , 2019 .

[14]  András Benedek,et al.  SUPPORTING THE M-LEARNING BASED KNOWLEDGE TRANSFER IN UNIVERSITY EDUCATION AND CORPORATE SECTOR , 2014 .

[15]  Robert A. Meyers,et al.  Encyclopedia of Complexity and Systems Science , 2009 .

[16]  Giulio Sandini,et al.  The iCub Cognitive Humanoid Robot: An Open-System Research Platform for Enactive Cognition , 2006, 50 Years of Artificial Intelligence.

[17]  Balázs Mikó,et al.  The „ Phantom ” Delta Robot A New Device for Parallel Robot Investigations , 2018 .

[18]  Jozef Juhár,et al.  Multimodal dialogue system with NAO and VoiceXML dialogue manager , 2017, 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom).

[19]  Jozef Juhár,et al.  Towards robot-assisted children speech audiometry , 2019, 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom).

[20]  Mária Gósy,et al.  Evaluation of Cognitive Processes using Synthesized Words: Screening of Hearing and Global Speech Perception  , 2018, Acta Polytechnica Hungarica.