Dual Robot Kit Preparation in Batch Preparation of Component Kits for Mixed Model Assembly

Kitting is a materials supply principle that plays a vital role for performance in mixed model assembly systems. The kit preparation process, whereby component kits are created, is central when kitting is applied. Kit preparation is a form of materials handling and is associated with several ergonomic and quality related issues. Robotics holds a great potential for decreasing the need for human labour, but literature on the topic is scarce. The purpose of this paper is to identify the time efficiency potential of a dual robot application for kit preparation. To address the purpose, a mathematical model is developed that allows dual robot kit preparation to be analysed and compared with manual kit preparation. Furthermore, the model supports identification of a suitable batch size given a lead time requirement from the assembly system. A numerical example shows dual robot kit preparation to be slightly more efficient than its manual ditto for preparation of 2, 3 and 4 kit batch sizes. The paper’s makes a theoretical contribution in terms of the time efficiency model of dual robot kit preparation. This model is also useful for practitioners when evaluating the potential of dual robot arm kit preparation in their own processes

Supervised and unsupervised learning in vision-guided robotic bin picking applications for mixed-model assembly

Mixed-model assembly usually involves numerous component variants that require effective materials supply. Here, picking activities are often performed manually, but the prospect of robotics for bin picking has potential to improve quality while reducing man-hour consumption. Robots can make use of vision systems to learn how to perform their tasks. This paper aims to understand the differences in two learning approaches, supervised learning, and unsupervised learning. An experiment containing engineering preparation time (EPT) and recognition quality (RQ) is performed. The findings show an improved RQ but longer EPT with a supervised compared to an unsupervised approach

Design of an Adaptable Tooling System for Part to Part Variation Processing

Today’s automotive manufacturing facilities use different robotic systems with the specifically designed end of arm tooling (EOAT). Regardless of how accurate these robotic systems may be, they are programmed to repeat the same task and move to the same position repeatedly. As convenient as this process may be, it does not allow robots to automatically readjust to different part variations without the human assistance. This situation is especially noticeable in the plastics manufacturing industry, e.g., fuel tank welding. This thesis describes the systematic design methodology of an adaptable tooling system for a part to part variations processing aimed at automotive plastic fuel tank manufacturing. By combining a 3D vision system with a PLC, and a Fanuc R-2000iB/165F 6 axis robot, the system provides the robot with the ability to automatically readjust the processing unit to different part variations. The design approach specifies programming and device correlation by using Siemens S7, Fanuc TP, and SICK AG software. A case study using a fuel tank sample was developed to check the system for functionality and performance. Results of the study indicate that the system is accurate within ±0.25 mm, which is well suited for fuel tank manufacturing. The study signifies a new approach to vision guided robotics (VGR). It utilizes existing equipment for applications where part variation may be present. Three patent applications were published during the course of this research. They each cover plastic fuel tank welding applications

Increasing the accuracy of position and orientation of the objects placed by the manipulator

Předkládaná disertační práce se zabývá zvyšováním přesnosti polohy objektů při jejich umisťování či montáži robotem. Průmyslové roboty se běžně používají v montážních linkách, kde se stále častěji využívá tzv. bin-bickin, což je vytahování neuspořádaných předmětů z palety či krabice. Tato pick-and-place aplikace se řeší pomocí vision systému a robotu, který uchopené objekty ukládá na dopravník, který posouvá objekty na technologické lince pro další zpracování. V práci je představená možnost vynechat dopravník a uchopený objekt robotem použít pro danou montáž či manipulaci bezprostředně po uchopení objektu bin-picking systémem. Práce se věnuje zpřesnění odhadu polohy objektu v chapadle robotu (v 3D prostoru) Iterative Closest Point algoritmem. K zpřesnění odhadu polohy jsou klíčová správná vstupní data do ICP algoritmu, čehož se dosáhne skenováním relevantních prvků, geometrických primitiv daného objektu. Úvodní část této disertační práce se věnuje komerčně dostupným bin-picking systémům a přehledu aktuálního stavu řešené problematiky s možnostmi zpřesnění montáže či manipulace s objektem manipulace. Následně jsou stanoveny cíle práce, které vycházejí z průzkumu současného stavu a v souvislosti s projekty realizovanými katedrou robotiky. Vlastní část práce je rozdělena do dílčích kapitol, podle jednotlivých cílů práce. V práci je popsána metodika pro umístění senzorů vůči skenovanému objektu z různých materiálů pro zabezpečení sběru dat. Na základě získaných charakteristik je vytvořen simulační model pro účely virtuálního skenování a simulací. Následně je vytvořena metodika rozmístění senzorů pro zpřesnění odhadu polohy – hledání optimální polohy senzorů vůči skenovanému objektu s ohledem na získaná data pro vstup do ICP algoritmu. Simulační model, virtuální skenování a odhad polohy je ověřen na reálném systému.The presented dissertation deals with increasing the position accuracy of objects during their placement or assembly by a robot. Industrial robots are commonly used in assembly lines, where bin-picking, which is the removal of disordered objects from a pallet or box, is becoming increasingly used. This pick-and-place application is solved by a vision system and a robot that places the grasped objects on a conveyor that moves the objects on the technology line for further processing. This work presents the possibility to eliminate the conveyor which can be achieved by performing the assembly process directly after grasping the object via bin-picking system. The thesis focuses on refining the object pose estimation in a gripper of the robot (in 3D space) by Iterative Closest Point algorithm. To refine the pose estimation, the correct input data to the ICP algorithm are crucial, which is achieved by scanning the relevant features, the geometric primitives of the object. The introductory part of this dissertation is devoted to commercially available bin-picking systems and an overview of the current state of the art with possibilities for refining the assembly or manipulation process. Then, the objectives of the thesis are stated based on a survey of the current state of the art and in the context of projects carried out by the Department of Robotics. The actual part of the thesis is divided into subchapters according to the different objectives of the thesis. This article describes a methodology for positioning sensors relative to the scanned object (and various materials) to increase the reliability of data collection. Based on the obtained characteristics, a simulation model is developed for virtual scanning and simulation purposes. Subsequently, a sensor placement methodology is developed to refine the pose estimation – finding the optimal pose of the sensors relative to the scanned object with respect to the collected data for input to the ICP algorithm. The simulation model and pose estimation are verified on a real system.354 - Katedra robotikyvyhově

Comparative analysis of 3D- depth cameras in industrial bin picking solution

Machine vision is a crucial component of a successful bin picking solution. During the past few years, there has been large advancements in depth sensing technologies. This has led to them receiving a lot of attention, especially in bin picking applications. With reduced costs and greater accessibility, the use of machine vision has rapidly increased. Automated bin picking poses a technical challenge, which is present in numerous industrial processes. Robots need perception from their surroundings, and machine vision attempt to solve this by providing eyes to the machine. The motivation behind solving this challenge is the increased productivity, enabled by automated bin picking. The main goal of this thesis is to address the challenges of bin picking by comparing the performance of different 3D- depth cameras with illustrative case studies and experimental research. The depth cameras are exposed to different ambient conditions and object properties, where the performance of different 3D- imaging technologies is evaluated and compared between each other. The performance of a commercial bin picking solution is also researched through illustrative case studies to evaluate the accuracy, reliability, and flexibility of the solution. Feasibility study is also conducted, and the capabilities of the bin picking solution is demonstrated in two industrial applications. This research work focuses on three different depth sensing technologies. Comparison is done between structured light, stereo vision, and time-of-flight technologies. The main categories for evaluation are ambient light tolerance, reflective surfaces, and how well the depth cameras can detect simple and complex geometric features. The comparison between the depth cameras is limited to opaque objects, ranging from shiny metal blanks to matte connector components and porous surface textures. The performance of each depth camera is evaluated, and the advantages and disadvantages of each technology are discussed. Results of this thesis showed that while all of the technologies are capable of performing in a bin picking solution, structured light performed the best in the evaluation criteria of this thesis. The results from bin picking solution accuracy evaluation also illustrated some of the many challenges of bin picking, and how the true accuracy of the bin picking solution is not dictated purely by the resolution of the vision sensor. Finally, to conclude this thesis the results and future suggestions are discussed.Konenäkö on keskeinen osa automatisoitua kasasta poimintasovellusta. Syvyyskamerateknologiat ovat kehittyneet paljon kuluneiden vuosien aikana, joka on herättänyt paljon keskustelua niiden käyttömahdollisuuksista. Kustannusten alenemisen, sekä paremman saatavuuden myötä konenäön käyttö, erityisesti kasasta poimintasovelluksissa onkin lisääntynyt nopeasti. Automatisoitu kasasta poiminta kuitenkin omaa teknisiä haasteita, jotka ovat läsnä lukuisissa teollisissa prosesseissa. Motivaatio automatisoidun kasasta poiminnan taustalla on tuotettavuuden kasvu, jonka konenäkö mahdollistaa tarjoamalla dataa robotin ympäristöstä. Tämän diplomityön tavoitteina on vastata kasasta poiminnan haasteisiin vertailemalla erilaisten 3D-syvyyskameroiden suorituskykyä tapaustutkimusten sekä kokeellisen tutkimuksen avulla. Syvyyskameroiden toimintaa arvioidaan erilaisissa ympäristöissä sekä erilaisilla kappaleilla, jonka seurauksena 3D-kuvaustekniikoiden suorituskykyä vertaillaan keskenään. Työn aikana arvioidaan myös kaupallisen kasasta poimintasovelluksen suorituskykyä, jossa tutkitaan tapaustutkimusten avulla sovelluksen tarkkuutta, luotettavuutta sekä joustavuutta. Tämän lisäksi sovelluksen toimintaa pilotoidaan, ja ratkaisun ominaisuuksia demonstroidaan kahdessa teollisessa sovelluksessa. Tämä diplomityö keskittyy kolmeen eri syvyyskameratekniikkaan. Vertailu tehdään strukturoidun valon, stereonäön sekä Time-of-Flight tekniikoiden välillä. Arvioinnin pääkategoriat ovat ympäristön valoisuus, geometristen muotojen havainnointikyky, sekä heijastavat pinnat. Syvyyskameroiden välinen vertailu rajoittuu läpinäkymättömiin kappaleisiin, jotka vaihtelevat kiiltävistä metalliaihioista mattapintaisiin liitinkomponentteihin ja huokoisiin pintarakenteisiin. Tutkimuksen tulokset osoittivat, että vaikka kaikki tekniikat kykenevät automatisoituun kasasta poimintaan, strukturoitu valo suoriutui tutkituista teknologioista parhaiten. Kasasta poimintasovelluksen tarkkuuden arviointi havainnollisti myös sen monia haasteita, sekä kuinka sovelluksen todellinen tarkkuus ei riipu ainoastaan syvyyskameran resoluutiosta. Loppupäätelmien lisäksi työ päätetään ehdotuksilla tutkimuksen jatkamiseksi

Materials Handling in Production Systems: Design and Performance of Kit Preparation

This thesis focuses on processes for kit preparation, which are applied with the materials supply principle of kitting in production systems for mixed-model assembly. With kitting, assembly processes are supplied with portions of pre-sorted components, and each portion makes up a kit that holds the components needed for one assembly object at one or several assembly processes. When kitting is applied, picking activities, which are otherwise performed at assembly processes, are instead carried out in a process for kit preparation. Kit preparation involves collecting components designated for a particular assembly object into a single unit load that is delivered to assembly.Kitting is widely seen as beneficial for quality and flexibility in assembly processes when there are a large variety of components. Performance effects in assembly processes normally associated with kitting largely depend on the performance of kit preparation. Previous research indicates that a picking system’s design greatly impacts its performance. While research that has dealt with kit preparation points out several design aspects that can affect its performance, the available knowledge is far from exhaustive. The purpose of this thesis is to contribute to the knowledge of how kit preparation design aspects govern kit preparation performance.Case research, experiments, and modelling have been used to study how flexibility, kit quality and man-hour efficiency are affected by kit preparation design aspects related to work organisation, layout, policies, packaging, equipment, picking information, automation and control. Two case research studies respectively address kit preparation flexibility and kit quality, identifying how kit preparation design aspects can be configured to support these two performance areas. Two experiments focus on how picking information systems and confirmation methods affect kit preparation man-hour efficiency. One modelling study focuses on how collaborative robots can support man-hour efficient kit preparation. Through involvement in three research projects and an extensive review of the literature, this research has been guided by the needs of industry and by previously established knowledge.This thesis contributes to theory and to practice in the form of knowledge about relationships between kit preparation design aspects and the performance areas flexibility, kit quality and manhour efficiency. The theoretical contribution consists of building upon and underpinning the limitedknowledge about the topic that has been previously available, while also adding new knowledge. This includes, for example, glasses with integrated computer displays, RFID-scanning wristbands, and collaborative robots, and how they are linked to kit preparation performance. The practical contribution consists of concise yet holistic descriptions of relationships between kit preparation design and performance, which industry can readily adopt with some consideration to the situation’s characteristics