1,488 research outputs found

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

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    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

    Automated freeform assembly of threaded fasteners

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    Over the past two decades, a major part of the manufacturing and assembly market has been driven by its customer requirements. Increasing customer demand for personalised products create the demand for smaller batch sizes, shorter production times, lower costs, and the flexibility to produce families of products - or different parts - with the same sets of equipment. Consequently, manufacturing companies have deployed various automation systems and production strategies to improve their resource efficiency and move towards right-first-time production. However, many of these automated systems, which are involved with robot-based, repeatable assembly automation, require component- specific fixtures for accurate positioning and extensive robot programming, to achieve flexibility in their production. Threaded fastening operations are widely used in assembly. In high-volume production, the fastening processes are commonly automated using jigs, fixtures, and semi-automated tools. This form of automation delivers reliable assembly results at the expense of flexibility and requires component variability to be adequately controlled. On the other hand, in low- volume, high- value manufacturing, fastening processes are typically carried out manually by skilled workers. This research is aimed at addressing the aforementioned issues by developing a freeform automated threaded fastener assembly system that uses 3D visual guidance. The proof-of-concept system developed focuses on picking up fasteners from clutter, identifying a hole feature in an imprecisely positioned target component and carry out torque-controlled fastening. This approach has achieved flexibility and adaptability without the use of dedicated fixtures and robot programming. This research also investigates and evaluates different 3D imaging technology to identify the suitable technology required for fastener assembly in a non-structured industrial environment. The proposed solution utilises the commercially available technologies to enhance the precision and speed of identification of components for assembly processes, thereby improving and validating the possibility of reliably implementing this solution for industrial applications. As a part of this research, a number of novel algorithms are developed to robustly identify assembly components located in a random environment by enhancing the existing methods and technologies within the domain of the fastening processes. A bolt identification algorithm was developed to identify bolts located in a random clutter by enhancing the existing surface-based matching algorithm. A novel hole feature identification algorithm was developed to detect threaded holes and identify its size and location in 3D. The developed bolt and feature identification algorithms are robust and has sub-millimetre accuracy required to perform successful fastener assembly in industrial conditions. In addition, the processing time required for these identification algorithms - to identify and localise bolts and hole features - is less than a second, thereby increasing the speed of fastener assembly

    Robotic assembly of threaded fasteners in a non-structured environment

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    Over the past two decades, a major part of the manufacturing and assembly market has been driven by the increasing demand for customised products. This has created the need for smaller batch sizes, shorter production times, lower costs, and the flexibility to produce families of products—or to assemble different parts—with the same sets of equipment. Consequently, manufacturing companies have deployed various automation systems and production strategies to improve their resource efficiency and move towards right-first-time production. Threaded fastening operations are widely used in assembly and are typically time-consuming and costly. In high-volume production, fastening operations are commonly automated using jigs, fixtures, and semi-automated tools. However, in low-volume, high-value manufacturing, fastening operations are carried out manually by skilled workers. The existing approaches are found to be less flexible and robust for performing assembly in a less structured industrial environment. This motivated the development of a flexible solution, which does not require fixtures and is adaptable to variation in part locations and lighting conditions. As a part of this research, a novel 3D threaded hole detection and a fast bolt detection algorithms are proposed and reported in this article, which offer substantial enhancement to the accuracy, repeatability, and the speed of the processes in comparison with the existing methods. Hence, the proposed method is more suitable for industrial applications. The development of an automated bolt fastening demonstrator is also described in this article to test and validate the proposed identification algorithms on complex components located in 3D space

    Bin-Picking Solution for Randomly Placed Automotive Connectors Based on Machine Learning Techniques

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    This paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. Seals are inserted manually or, more recently, through robotic stations. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Consequently, faulty connectors are dumped into boxes, piling up different types of references. These connectors are not trash and need to be reused. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms.info:eu-repo/semantics/publishedVersio

    Vision Experts: “Capturing the Holy Grail” Business Plan

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    This project investigates the potential viability of commercializing robotics software developed by a UBC engineer. The aim of this project is to provide the inventor with a business plan that will act as a tool to help in obtaining funding for the commercialization of this software. Through research and work, it has been concluded that the possibility does exist to use this software as the basis for a successful company. To that end, a business plan is presented with the goal of helping the developer achieve her goals

    Robot Vision in Industrial Assembly and Quality Control Processes

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    Advances in flexible manipulation through the application of AI-based techniques

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    282 p.Objektuak hartu eta uztea oinarrizko bi eragiketa dira ia edozein aplikazio robotikotan. Gaur egun, "pick and place" aplikazioetarako erabiltzen diren robot industrialek zeregin sinpleak eta errepikakorrak egiteko duten eraginkortasuna dute ezaugarri. Hala ere, sistema horiek oso zurrunak dira, erabat kontrolatutako inguruneetan lan egiten dute, eta oso kostu handia dakarte beste zeregin batzuk egiteko birprogramatzeak. Gaur egun, industria-ingurune desberdinetako zereginak daude (adibidez, logistika-ingurune batean eskaerak prestatzea), zeinak objektuak malgutasunez manipulatzea eskatzen duten, eta oraindik ezin izan dira automatizatu beren izaera dela-eta. Automatizazioa zailtzen duten botila-lepo nagusiak manipulatu beharreko objektuen aniztasuna, roboten trebetasun falta eta kontrolatu gabeko ingurune dinamikoen ziurgabetasuna dira.Adimen artifizialak (AA) gero eta paper garrantzitsuagoa betetzen du robotikaren barruan, robotei zeregin konplexuak betetzeko beharrezko adimena ematen baitie. Gainera, AAk benetako esperientzia erabiliz portaera konplexuak ikasteko aukera ematen du, programazioaren kostua nabarmen murriztuz. Objektuak manipulatzeko egungo sistema robotikoen mugak ikusita, lan honen helburu nagusia manipulazio-sistemen malgutasuna handitzea da AAn oinarritutako algoritmoak erabiliz, birprogramatu beharrik gabe ingurune dinamikoetara egokitzeko beharrezko gaitasunak emanez
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