This book gathers the Proceedings of the 6th International Conference on Robot Intelligence Technology and Applications (RITA 2018). Reflecting the conference's main theme, Robotics and Machine Intelligence: Building Blocks for Industry 4.0, it features relevant and current research investigations into various aspects of these building blocks. The areas covered include: Instrumentation and Control, Automation, Autonomous Systems, Biomechatronics and Rehabilitation Engineering, Intelligent Systems, Machine Learning, Robotics, Sensors and Actuators, and Machine Vision, as well as Signal and Image Processing. A valuable asset, the book offers researchers and practitioners a timely overview of the latest advances in robot intelligence technology and its applications.

Highlights the latest research on robot intelligence technology and its applications Features relevant and current research investigations into various aspects of the building blocks of Industry 4.0 Gathers the Proceedings of the 6th International Conference on Robot Intelligence Technology and Applications

Autorentext

Dr. Anwar P.P. Abdul Majeed graduated with a first class honours B.Eng. in Mechanical Engineering from Universiti Teknologi MARA (UiTM), Malaysia. He obtained an M.Sc. in Nuclear Engineering from Imperial College London, United Kingdom. He then received his Ph.D. in Rehabilitation Robotics under the supervision of Prof. Dr. Zahari Taha from Universiti Malaysia Pahang (UMP). He is currently serving as a senior lecturer at the Faculty of Manufacturing Engineering, UMP. He is an active research member at the Innovative Manufacturing, Mechatronics and Sports Laboratory, UMP. His research interest includes computational mechanics, applied mechanics, sports engineering, renewable and nuclear energy, sports performance analysis as well as machine learning.

Mr. Jessnor Arif Mat Jizat graduated as Diplomingenieur (FH) in Mechatronics Engineering from Hochschule Karlsruhe Technik and Wirtschaft, Germany. He obtained a M.Eng.(Manufacturing) by research in Automated Guided Vehicleusing computer vision from Universiti Malaysia Pahang. He is currently pursuing his Ph.D. studies at the Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing Engineering, Universiti Malaysia Pahang under the supervision of Professor Zahari Taha. His research interest includes robotics vision, sports engineering, autonomous vehicle.

Dr. Mohd Hasnun Arif Hassan earned his first degree in Mechanical Engineering from the Technische Hochschule Bingen in Germany in 2010. He then pursued a Master's degree in Mechanical Engineering at the University of Malaya in Kuala Lumpur, graduating with distinction in 2012. After that, he embarked on his Ph.D. at the Universiti Malaysia Pahang (UMP), where he studied the head injuries sustained by soccer players due to heading. He completed his Ph.D. study in 2016, then continued to serve UMP as a senior lecturer. He is currently the director of the Innovative Manufacturing, Mechatronics and Sports Laboratory (iMAMS), which was founded by Professor Zahari Taha, who was his supervisor for his doctoral study. His research interests include finite element modelling of the interaction between human and sports equipment, instrumentation of sports equipment, and injury prevention particularly with regard to sport. His work aims to apply engineering principles in sports to not only enhance the performance of an athlete, but also to prevent injuries.

Professor Zahari Taha graduated with a B.Sc. in Aeronautical Engineering with Honours from the University of Bath, United Kingdom. He obtained his Ph.D. in Dynamics and Control of Robots from the University of Wales Institute of Science and Technology in 1987. He is currently a Professor of Manufacturing Engineering at Universiti Malaysia Pahang and he is also the founder and advisor of the Innovative Manufacturing, Mechatronics and Sports Laboratory. Prof. Zahari teaches and conducts research in the areas of industrial automation, robotics, ergonomics, sustainable manufacturing, machine learning and sports engineering.

Professor Han-Lim Choi is an Associate Professor of Aerospace Engineering at KAIST (Korea Advanced Institute of Science and Technology), and a Visiting Scholar in the School of Aeronautics and Astronautics at Purdue University. He received his B.S. and M.S. degrees in Aerospace Engineering from KAIST, Daejeon, Korea, in 2000 and 2002, respectively, and his Ph.D. degree in Aeronautics and Astronautics from Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2009. He then worked at MIT as a postdoctoral associate until he joined KAIST in 2010. His current research interests include planning and control of multi-agent systems, planning and control under uncertainty, resource management in radars, and Bayesian inference for large-scale systems. He (together with Dr. Jonathan P. How) is the recipient of Automatica Applications Prize in 2011. Professor Junmo Kim received the B.S. degree from Seoul National University, Seoul, Korea in 1998, and the M.S. and Ph.D. degrees from the Massachusetts Institute of Technology (MIT), Cambridge in 2000 and 2005 respectively. From 2005 to 2009, he was with the Samsung Advanced Institute of Technology (SAIT), Korea, as a Research Staff Member. He joined the faculty of KAIST in 2009, where he is currently an Associate Professor of electrical engineering. His research interests are in image processing, computer vision, statistical signal processing, and information theory.

Inhalt

Longitudinal Velocity Control Design with Error Tolerance Strategy for Autonomous Vehicle.- Vision Based Smart Sorting Machine.- A Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines Developed on MATLAB.- Path Tracking and Optimization for Mecanum-wheeled Robot via Linear Angle-to-Gain (LA-G) Method.- Brain Tumour Detection and Classification using K-means clustering and SVM Classifier.- Redundancy in Parallel Robots: a case study of kinematics of a redundantly actuated parallel chewing robot.- Location-based Cryptographic Techniques: Its Protocols and Parameters.- Efficient Robotic Grasp Learning by Demonstration.