Using Collaborative Robots As A Tool For Easier Programming Of Industrial Robots

Programming industrial robots using traditional jogging via teach pendant is a time-consuming task that requires extensive training. More intuitive and faster task programming is often possible using kinesthetic teaching. Although this feature is available on many commercial collaborative robots, it is rarely available on traditional industrial robots. In this paper we propose a framework for allowing tasks to be instructed using a collaborative robot via kinesthetic teaching, and afterwards deployed to a traditional industrial robot. The frame- work consists of a physical modular concept for robot exchange, and a online programming software tool called Universal Industrial Interface. To assess the framework, a feasibility study is conducted where an industrial relevant task is  rst programmed using a collaborative manipulator, and afterwards deployed on an industrial manipulator

SARSCEST (human factors)

People interact with the processes and products of contemporary technology. Individuals are affected by these in various ways and individuals shape them. Such interactions come under the label 'human factors'. To expand the understanding of those to whom the term is relatively unfamiliar, its domain includes both an applied science and applications of knowledge. It means both research and development, with implications of research both for basic science and for development. It encompasses not only design and testing but also training and personnel requirements, even though some unwisely try to split these apart both by name and institutionally. The territory includes more than performance at work, though concentration on that aspect, epitomized in the derivation of the term ergonomics, has overshadowed human factors interest in interactions between technology and the home, health, safety, consumers, children and later life, the handicapped, sports and recreation education, and travel. Two aspects of technology considered most significant for work performance, systems and automation, and several approaches to these, are discussed

Endüstri mühendisliğinde bilgisayar destekli öğrenme uygulaması

Text in English ; Abstract: English and TurkishIncludes bibliographical references (leaves 63-66)xi, 96 leavesLearning is the most central process in education where it's essential outcome being the assimilation of new skills, abilities, perspectives, attitudes, and knowledge. Learning is a life-long quest that is vital in today's information based economy. Educational institutions, though not exclusively, play a significant role in an individual's learning journey. Over the last two decades there has been significant research and development in methods of learning and teaching mostly spearheaded by institutions of higher education. At the core of these R&D efforts lie the need for improving the effectiveness of traditional methods of learning and teaching. Motivated by this need we designed and report here a computer aided learning application that helps better teach and learn some of the topics in inventory control and production planning in the discipline of industrial engineering. Computer aided learning environment provides time and location flexibility, enables asynchronicity, self-pacing, and experiential self-learning, extensively utilizes multi-media, and supports multiple types of learning styles. We designed our application in order to take advantage of all the benefits of this environment to reinforce the learning goals associated with our application area. Our application is a learning tool named WashMac Game. The game has three levels that are aligned with the progress of the student. Each level aims to simulate the production and sales process and teaching to calculate the product amounts. There are animations for teaching and understanding more easily and accurate.Öğrenme kelimesi, bilgi edinmek, yetenek ve beceri kazanmak anlamlarına gelmektedir. Öğrenme işi sonucunda yeni yetenekler, davranışlar kazanılır ve bilgi öğrenimi gerçekleşmektedir. Eğitim kurumlarının bilgiye ek olarak kişisel öğrenme üzerinde de etkin bir rolü vardır. Son 20 yılda öğrenme ile ilgili önemli araştırmalar ve gelişimler yaşanmıştır. Klasik öğrenme olarak ifade edilen öğrenme çeşidinde araştırma geliştirmeler sonucunda ilerlediğini ve yerini elektronik öğrenmeye bırakmaya başladığını görüyoruz. Bu araştırmalara istinaden endüstri mühendisliği için bilgisayar destekli öğrenme üzerine bir çalışma yaptık. Envanter kontrolü ve üretim planlama konularını kapsayan bir tasarım hazırladık. Bilgisayar destekli eğitim sayesinde, kullanıcılar zaman ve yer konularında esneklik kazanılabilmektedir. Bir diğer faydası ise kullanıcılar başka bir insana, öğretmene ayrıca ihtiyaç duymadan öğrenim görebilmektedir. Tasarladığımız uygulamanın ismi WashMac Game olup toplamda 3 seviyeden oluşmaktadır. Basitten başlayarak üretim ve satış süreçlerinde üretilecek miktarları hesaplamayı, envanteri takip etmeyi öğretmeyi hedefliyoruz. Öğrenme stillerini baz alarak animasyonlar kullandık. Kullanıcının dikkati çekebilen bir oyun olmasına dikkat ederek tasarımı hazırladık.IntroductionMotivationContributionsOutlineLearning TheoryTypes of IntelligenceTypes of LearningBloom’s TaxonomyBloom's Revised TaxonomyLearning StylesTradional LearningE-LearningProject Based LearningComputer Aided LearningLearning by DoingNew LearningOnline Learning CommunitiesLearning Objectives and Learning OutcomesY & Z GenerationIndustrial EngineersLiterature ReviewVisual Matrix Calculator for Undergraduate StudentsGeogebraThe Virtual CompanySupply Chain SimulatorThe Poker Chip GameImplementationsBeer GamePrisoner’s DilemmaTaxonomiesTaxonomy of Inventory Control PoliciesTaxonomy of Production ModesTaxonomy of Facility Layout TypesTaxonomy of Mathematical ModelsWashMac Learning GameWelcome ScreenFirst Level (Easy)TermsThe Second Level (Medium)EOQ Model(r,Q), (s,S) policiesThe Third Level (Hard)QuizzesAssembly LineConclusio

Intuitive Instruction of Industrial Robots : A Knowledge-Based Approach

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they must become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor-controlled robot tasks. The robot motions are expressed using constraints, and multiple of simple constrained motions can be combined into a robot skill. The skill can be stored in a knowledge base together with a semantic description, which enables reuse and reasoning. The main contributions of the thesis are 1) development of ontologies for knowledge about robot devices and skills, 2) a user interface that provides simple programming of dual-arm skills for non-experts and experts, 3) a programming interface for task descriptions in unstructured natural language in a user-specified vocabulary and 4) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user, is simple to use for non-experts and reduces the programming time for experts by 80%. The representation is described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in seven papers, the first describing the knowledge representation and the second the knowledge-based architecture that enables skill sharing between robots. The third paper presents the translation from high-level instructions to low-level code for force-controlled motions. The two following papers evaluate the simplified programming prototype for non-expert and expert users. The last two present how program statements are extracted from unstructured natural language descriptions

ILoSA: Interactive Learning of Stiffness and Attractors

Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demonstrations and corrections with a user-friendly interface. The presented framework, named ILoSA, uses Gaussian Processes for policy learning, identifying regions of uncertainty and allowing interactive corrections, stiffness modulation and active disturbance rejection. The experimental evaluation of the framework is carried out on a Franka-Emika Panda in three separate cases with unique force interaction properties: 1) pulling a plug wherein a sudden force discontinuity occurs upon successful removal of the plug, 2) pushing a box where a sustained force is required to keep the robot in motion, and 3) wiping a whiteboard in which the force is applied perpendicular to the direction of movement

The Effect of Augmented Reality Treatment on Learning, Cognitive Load, and Spatial Visualization Abilities

This study investigated the effects of Augmented Reality (AR) on learning, cognitive load and spatial abilities. More specifically, it measured learning gains, perceived cognitive load, and the role spatial abilities play with students engaged in an astronomy lesson about lunar phases. Research participants were 182 students from a public university in southeastern United States, and were recruited from psychology research pool. Participants were randomly assigned to two groups: (a) Augmented Reality and Text Astronomy Treatment (ARTAT); and (b) Images and Text Astronomy Treatment (ITAT). Upon entering the experimental classroom, participants were given (a) Paper Folding Test to measure their spatial abilities; (b) the Lunar Phases Concept Inventory (LPCI) pre-test; (c) lesson on Lunar Phases; (d) NASA-TLX to measure participants’ cognitive load; and (e) LPCI post-test. Statistical analysis found (a) no statistical difference for learning gains between the ARTAT and ITAT groups; (b) statistically significant difference for cognitive load; and (c) no significant difference for spatial abilities scores