5^th International Conference on Automation and Robotics April 16-17, 2018 Las Vegas, Nevada, USA

Randika K W Vithanage has received his BSc degree in Mechanical Engineering from University of Moratuwa, Sri Lanka in 2010. He is currently a PhD Scholar at Glasgow Caledonian University, where he is awarded a Competitive Scholarship to fund his PhD, he has worked as a Senior Manufacturing Engineer at Toshiba TEC, Singapore where he was recognized as employee of the year and named Inventor on a patent. His research interests include industrial robots, robotic sensing, and manipulation of robots in unstructured environments.

Due largely to the growing emphasis in academic research on industrial robots and their applications, it is oft en required by researchers to understand and examine the kinematic aspect of such robots. Obtaining both forward and inverse kinematic models of a given industrial robot could be a tedious and intricate task. Th erefore, this paper presents a detailed kinematic analysis of a 6-axis industrial robot that commonly found in present-day industry and research laboratories. Th e proposed kinematic solutions have been validated against the simulation soft ware provided by robot’s supplier and an error analysis has been done to ensure the accuracy. Th ere are particular models of robots which are well discussed in the fi eld of robotic research and also within the domain of kinematic analysis. Th e Puma 560 by Unimation, also known as “white rat” of robotics is one of such robots which catalyzed robotic research for decades, and well examined in textbooks and research articles. However, the eminence of such robots is gradually being replaced by modern 6-axis industrial robots. Perhaps due to its size, prize, availability or consideration of health and safety aspects, the Fanuc LR Mate 200iD is becoming increasingly popular in the industry as well as in the research laboratories. Alternatively, there are a limited number of research articles which examine the kinematics of present day industrial robots. Further, the majority of those articles barely discusses the results and lacks the scientifi c validation of proposed solutions. Th erefore, the main focus of this article was to generate and validate the both forward and inverse kinematic models of a popular and modern industrial robot – the Fanuc LR Mate 200iD.

Mi-Ching Tsai has completed his PhD in Engineering Science from the University of Oxford UK in 1990. He is currently a Chair Professor at the Department of Mechanical Engineering, National Cheng Kung University, Taiwan. He has authored or coauthored more than 117 journal papers and holds more than 100 patents. His research interests include robust control, servo control, motor design, and applications of advanced control technologies using DSPs. He is a Fellow of the Institution of Engineering and Technology, UK and previously served as an Associate Editor of the IEEE/ASME Transactions on Mechatronics from 2003 to 2007 and the Deputy Minister of the Ministry of Science and Technology, Taiwan from 2016 to 2017.

Piezoelectric self-sensing actuators (SSAs) have been extensively used in vibration control of fl exible structures over the last three decades. Compared to separated sensor/actuator systems, the SSA is simple, robust, and cost-eff ective. According to the literature, the specially designed electric circuit, referred to as a bridge circuit, is required to realize the concept. A method of achieving self-sensing capability without a bridge circuit is proposed by utilizing a velocity observer, and then the vibration velocity of a SSA can be estimated by the measured voltage and current signals. Th us, the SSA active vibration control can be implemented without using a physical velocity sensor to achieve the required vibration suppression based on feedback control design. Th e SSA vibration suppression performance is highly dependent on the equivalent mechanical admittance, which consists of equivalent mass, stiff ness, and damping. Furthermore, the equivalent stiff ness and damping will be directly influenced by the controller parameters. Th us, the SSA vibration suppression performance can be adjusted by the control design. The experimental results show that the proposed method can eff ectively reduce the structural resonance phenomenon when the controller parameters of the SSA are properly designed with a required mechanical admittance.

A mobile robot deep reinforcement learning system is created that converges on common robotic tasks using four times less feedback than pre-existing solutions. Th e system achieves this leap in effi ciency through context-aware action selection and aggressive online hyper-parameter optimization while still maintaining performance on embedded hardware. A core algorithm of deep wire fi tted q-learning is supplemented with active measurement of robot uncertainty, defi ned as the derivative of error between expected and received reward. Th is uncertainty value directly scales the temperature of Boltzmann probabilistic exploration policy in addition to the learning rate of stochastic gradient descent. Furthermore, to provide generality across robots and tasks, neural network topology is effi ciently evolved throughout training and evaluation. Finally, experience replay is extended to changing environments and is integrated with our uncertainty value. Human operators successfully trained the system on multiple robots in a matter of minutes to perform tasks such as driving to a point with a diff erential drive system, following a line using holonomic Swedish wheels or playing ping pong with a robot arm. All are without any manual hyperparameter adjustment in both simulation and hardware.

A mobile robot deep reinforcement learning system is created that converges on common robotic tasks using four times less feedback than pre-existing solutions. Th e system achieves this leap in effi ciency through context-aware action selection and aggressive online hyper-parameter optimization while still maintaining performance on embedded hardware. A core algorithm of deep wire fi tted q-learning is supplemented with active measurement of robot uncertainty, defi ned as the derivative of error between expected and received reward. Th is uncertainty value directly scales the temperature of Boltzmann probabilistic exploration policy in addition to the learning rate of stochastic gradient descent. Furthermore, to provide generality across robots and tasks, neural network topology is effi ciently evolved throughout training and evaluation. Finally, experience replay is extended to changing environments and is integrated with our uncertainty value. Human operators successfully trained the system on multiple robots in a matter of minutes to perform tasks such as driving to a point with a diff erential drive system, following a line using holonomic Swedish wheels or playing ping pong with a robot arm. All are without any manual hyperparameter adjustment in both simulation and hardware.

Sergey Mikhailovich Afonin is an Associate Professor of Department of Intellectual Technical Systems of National Research University of Electronic Technology (Moscow Institute of Electronic Technology MIET). He is a graduate of the National Research University of Electronic Technology MIET, Engineer in Electronic Technology 1976. He has completed his PhD in Electronic Technology Engineering and Control Systems from MIET 1982. He has received academic title of Senior Researcher from MIET 1991. He has received different positions such as: Aspirant MIET 1976–79, Junior Researcher MIET 1979–82, Senior Researcher MIET 1983–93, Associate Professor at MIET since 1993 to present time. He has more than 200 scientifi c papers to professional publication and 16 inventions. He is the Recipient of silver medal and two bronze in VDNKh Russia.

The piezo actuator is using in the automatic control system in the scanning tunneling microscopes, the scanning force microscopes and the atomic force microscopes for the nanotechnology. Th e piezo actuator is the piezo mechanical device intended for the actuation of the mechanisms, the systems or the management based on the piezo eff ect, converts the electrical signals into the mechanical movement and the force. Th e correcting devices are chosen the high quality of the automatic control systems of the deformation the piezo actuator. Th e analytical expressions for the suffi cient absolute stability conditions of the system with the hysteresis nonlinearity of the electro-magneto-elastic actuators are written using the Yakubovich absolute stability criterion with the use of the derivative for the characteristic deformation of the piezo actuator. Th e Yakubovich criterion is the development of the Popov absolute stability criterion. For the stable control system on Lyapunov, the Yalubovich absolute stability criterion for the systems with the single hysteresis nonlinearity provides the simplest and pictorial representation of results of the investigation of the stability and the possibility of the synthesis of the correcting devices of the system ensuring the stability of the strain control systems with the piezo actuator. In the condition of the absolute stability of the control system for the deformation the piezo actuator of the nanomanipulator is used the value of the tangent of the angle of the tangent line to the hysteresis nonlinearity for the piezo actuator. Th e stationary set of the automatic control systems of the deformation the piezo actuator is the segment of the straight line. Th e conditions of the absolute stability of the automatic control systems with the piezo actuator in the case of longitudinal, transverse and shift piezo eff ect for the hysteresis characteristic of deformation of the piezo actuator are obtained. Th e obtained absolute stability condition with the use of the derivative for the characteristic deformation of the piezo actuator for the automatic control system with the piezo actuator allow one to estimate and calculate the characteristics of the correcting devices of the control system of the deformation the piezo actuator.

Jagadeesh Shanmugam Hariharan Natarajan is currently pursuing Master of Science in Industrial Engineering at North Carolina State University and has completed his Bachelor’s in Mechatronics Engineering from Anna University in India. He has spent his last semester of his under-graduate degree working as a Research Assistant at Nanyang Technological University in Singapore, during which he realized how insights from data can make important decision at various circumstances. Hence, he is currently focusing his career on Data Analytics. Also, he has actively participated in the Summer Research Fellowship at Indian Institute of Technology, Madras.

Color is a powerful form of communication among human beings. Sociable robots that live and coexist with humans must also learn colors from the society it lives. A lot of research has been performed to enable computer, as the brain of a robot, to learn colors. Most of them rely on modeling of human color perception and mathematical complexities. Diff erently, this work targets on developing the capability of the computer to use machine learning approaches to learn the colors through human interaction. Th e diff erent colors which is being detected in the camera is processed by using image processing tool OpenCV and the most dominant color of the picture is identifi ed and displayed in the system. Th e user can now teach the computer the difference between the appropriate colors using the RGB values. Th erefore, although at the beginning, the computer does not know any colors, eventually through interaction, it learns numerous colors which will indicate the shared color learning with humans in the society. Aft er teaching the computer a number of times, it is able to classify the colors by matching with RGB values for that particular color from the database. If the color does not exist, the computer identifi es the closest possible color using the unsupervised machine learning technique k-means clustering. Aft er learning colors from the society, the developed algorithm is implemented in the NTU Singaboat, which is an Unmanned Surface Vehicle (USV) built for competing in the Maritime RobotX Challenge.

Bharat Bhargava is a Professor of Computer Science at Purdue University. He is conducting research in security and privacy issues in Service Oriented Architecture (SoA) and Cloud Computing. He has won six best paper awards in addition to the technical achievement award and golden core award from IEEE, and is a fellow of IEEE. He received outstanding Instructor Awards from the Purdue chapter of the ACM in 1996 and 1998. In 2003, he was inducted in the Purdue's Book of Great Teachers. He is Editor-In-Chief of four journals and serves on over ten editorial boards of international journals. He is the Founder of the IEEE Symposium on reliable and distributed systems, IEEE conference on Digital Library, and the ACM Conference on information and knowledge management. He has worked extensively at research laboratories of Air Force and Navy. He has successfully completed several Darpa and Navy STTR and AFRL projects. His recent work on controlled data dissemination in untrusted environments under attacks received the fi rst place in Purdue’s CERIAS Security Center Symposium held in March 2015.

Intelligent Autonomous Systems (IAS) are highly cognitive, refl ective, multitask-able, and eff ective in knowledge discovery. Examples of IAS include soft ware that is capable of automatic reconfi guration, autonomous vehicles, network of sensors with reconfi gurable sensory platforms, and an unmanned aerial vehicle respecting privacy by deciding to turn off its camera when pointing inside a private residence. Research is needed to build systems that can monitor their environment and interactions, learn their capability, and adapt to meet the mission objectives with limited or no human intervention. Th e systems should be fail-safe and should allow for graceful degradations while continuing to meet the mission objectives. Th is presentation will advance the science of autonomy in smart systems through enhancement in real-time control, auto-confi gurability, monitoring, adaptability, trust. I will present research ideas in smart autonomy, Multi-intelligence (MINT) Enterprise Analytics, and Rapid Autonomy prototype among others. Th e main objective is to realize a vision based on the following approaches: Employ machine learning techniques on sensor and provenance data to learn and understand the underlying patterns of interaction, conduct forensics to detect anomalies, and provide assistance in decision making by on-the-fl y semantic and probabilistic reasoning; Apply advanced data analytics techniques to incomplete and hidden raw system data (provenance data, error logs, etc.,) to discover new knowledge that contributes to the success of the IAS mission; Enhance the autonomous system’s self-awareness, self-protection, self-healing, and self-optimization by learning from the knowledge discovered through dataanalytics and Utilizing blockchain technology for storing provenance data for providing monitoring, trust, and verifi cation, using the WaxedPrune system developed for Northrup Grumman.

Fionn Murtagh is Professor of Data Science and was Professor of Computer Science, including Department Head, in many universities. Fionn was Editor-in-Chief of the Computer Journal (British Computer Society) for more than 10 years, and is an Editorial Board member of many journals. With over 300 refereed articles and 30 books authored or edited, his fellowships and scholarly academies include: Fellow of: British Computer Society (FBCS), Institute of Mathematics and Its Applications (FIMA), International Association for Pattern Recognition (FIAPR), Royal Statistical Society (FRSS), Royal Society of Arts (FRSA). He has been an Elected Member - Royal Irish Academy (MRIA), Academia - Europaea (MAE), Senior Member - IEEE.

The book with the title, “Th e Sciences of the Artifi cial”, is by Nobel Prize winner in 1978, Simon Herbert. At issue is cognitive processes and analytics from inherent hierarchical system complexity. We may be determining the extent of hierarchical nature and properties, perhaps including or determining evolution along the lines of geneology. First we address how inherently hierarchical various sources of data can be. Considered are time series that are fi nancial, environmental, biomedical, and texts that are from literature, from accident reports, and psychogically related dream reports. Th e use and benefi t of taking hierarchical structure fully into account includes the following: how high dimensional or sparse data become hierarchical, and application can be for proximity and related searching, leading to nearest neighbour regression. But far more than that is ultrametric regression, taking into account the ultrametric topology associated with hierarchical structure. For our cognitive processes and analytics, ultrametric regression is how cognitive and analytical processing determines such system properties for regression purposes. By using contiguity-constrained, i.e. here, chronological, hierarchical clustering, then through multivariate time series, and changepoint analysis, hierarchy expresses changes at varying scales. At issue, quite generally, are multivariate time series. Furthermore, our partitioning of the chronologically constrained hierarchical clustering, so as to segment the multivariate time series, and determine changepoints, this is carried out using a wavelet transform in the ultrametric topological space. Th e case study here, of ultrametric wavelet regression of multivariate time series, is through application to Colombian confl ict analysis.

Andrzej Wróbel is a Lecturer in the Institute of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology. He is a specialist in the design, analysis of mechatronic systems and industrial automation. He is the Head of studies in the fi eld of Automation and Robotics Engineering Processes. He is a Member of the Professional Association in Modern Manufacturing Technologies ModTech Iasi-Romania and International Union of Machine Builders (Donetsk, Ukraine). He is the Manager of Laboratory of Visualization of Mechatronic Systems in the Center of the New Technology of the Silesian Technical University. He is the Editor in Chief of Journal “Selected Engineering Problems”. He is an author or a co-author of more than fi ve monographs and chapters in books and more than 70 articles.

The industry has played an important role for the development of the polish economy for centuries. Th e location of a given industry in a given part of the country depends on such factors as natural resources area, location of sale or a qualifi ed staff . Th e Upper Silesian Industrial District is the largest industrial district of Poland includes industrial companies in the centraleastern part of the Silesian Voivodship. In this area, the automotive industry and companies closely cooperating with this industry are a very strong branch of industry. An example of such cooperation is Nexteer-the leader in the innovative motion control delivery of electric and hydraulic steering systems, steering columns and driveline systems. Th e paper presented in this article attempts to replace standard materials of steering columns such as aluminum with new composite materials. Th e prototype of such a steering column has been done as a part of the research project PBS3/B6/37/2015 (PST-41/RMT2/2015)in cooperation of Nexteer and Institute of Engineering. Processes Automation and Integrated Manufacturing Systems. Faculty of Mechanical Engineering, Silesian University of Technology. Th e main objective of the research was to compare the noise generated during the work of previously manufactured gears and the new innovative gear housing made of composite. Aft er analyzing the fi rst two prototypes of the transmission, we managed to obtain results comparable to the results of the production version. Subsequent research that will be carried out will be related to thermographic studies of transmission subassemblies and assemblies as well as examination of moments and forces generated during transmission operation.

Igor Gorlach has completed his PhD from North-West University in South Africa. He is the Chairperson at General Motors South Africa (GMSA) and Professor of Mechatronics at Nelson Mandela University.

Modern production systems utilize robotic and automation systems including automatic guided vehicles (AGVs) for a variety of material handling tasks. A low-cost AGV was initially designed for an assembly line at General Motors South Africa (GMSA). Th is paper presents the latest development and modifi cations of the AGV design. Th e main research focuses were to improve the AGV performance, simplify the operation and reduce the cost. Th e AGV is used as a tugger, which tows trolley between assembly stations in a pre-designed loop. However, it could also be employed to deliver unique or unusual parts between production lines in a more complex production environment. Th e improved AGV controller is based on a BeagleBone Black, which uses an ARM cortex-A8 processor for navigation, obstacle detection and logic processing. Th e navigation is achieved with a magnetic sensor that follows magnetic tape. Th e ultrasonic sensors are used to develop a safety zone ahead of the AGV to avoid obstacles. Th e proposed AGV design meets the criteria for an effi cient and low-cost autonomous material handling system. Th e developed AGV is capable of transporting tasks in various industrial environments and it can be easily reprogrammed to cater for very specifi c scenarios.

Rosemonica Bezerra De Jesus is currently in a Master’s degree program for Industrial Engineering and Chemical Engineer at the Federal University of Bahia (UFBA) Brazil. She has experience with projects, research and innovation. She has ability to use process simulators (ASPEN, HYSYS and UNISIM) and other engineering softwares such as MATLAB, AutoCAD and MsProject. In addition, she has knowledge in modeling, simulation, control and optimization of chemical and petrochemical processes.

This article refers to a phenomenological model of a batch reactor. Using an energy balance model it was possible to predict the heating and cooling behavior inside the reactor and how the temperature varies with the applied voltage. Th is work will be used for later emphasis on polymer syntheses, especially the hydrolytic synthesis of caprolactam, in order to obtain a product with a higher value-added market, nylon 6. Given the diffi culty of temperature control when operating with nylon 6, silicone oil (nylon-like characteristics) was used for system testing and data collection. In this way, the system boils down to a heating tank, with heating via electrical resistance. Th us, the focus of the present study will be on modeling the reactor using silicone oil. Th e study will be done obtaining dynamic measurements of temperature in order to be able to present a phenomenological model of the reactor. For the validation of the model, we used data collected in the plant from a steptype test. In this way, this text aims to develop the phenomenological model of the system in order to better understand the dynamics of the heating and thus enable future control studies. Th e model represented a satisfactory behavior of the reactor in question, presenting an average relative error of 4.3%.