1. Adaptive visual quality inspection based on defect prediction from production parametersZvezdan Lončarević, Simon Reberšek, Samo Šela, Jure Skvarč, Aleš Ude, Andrej Gams, 2024, izvirni znanstveni članek Povzetek: At the end of a production process, the manufactured products must usually be visually inspected to ensure their quality. Often, it is necessary to inspect the final product from several viewpoints. However, the inspection of all possible aspects might take too long and thus create a bottleneck in the production process. In this paper we propose and evaluate a methodology for adaptive, robot-aided visual quality inspection. With the proposed method, the most probable defects are first predicted based on the production process parameters. A suitable classifier for defect prediction is learnt in an unsupervised manner from a database that includes the produced parts and the associated parameters.Arobot then steers the camera only towards viewpoints associated with predicted defects, which implies that the trajectories of robot motion for the inspection might be different for every product. To enable dynamic planning of camera trajectories, we describe a methodology for evaluation and selection of the most appropriate autonomous motion planner. The proposed defect prediction approach was compared to other methods and evaluated on the products from a real-world production line for injection moulding, which was implemented for a producer of parts in the automotive industry.
Ključne besede: robot learning, robotic quality inspection, visual quality inspection, injection moulding, production parameters, robot motion planning
Objavljeno v DiRROS: 15.07.2024; Ogledov: 41; Prenosov: 14
 Celotno besedilo (7,44 MB)
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4. Hierarchical learning of robotic contact policiesMihael Simonič, Aleš Ude, Bojan Nemec, 2023, izvirni znanstveni članek Povzetek: The paper addresses the issue of learning tasks where a robot maintains permanent contact with the environment. We propose a new methodology based on a hierarchical learning scheme coupled with task representation through directed graphs. These graphs are constituted of nodes and branches that correspond to the states and robotic actions, respectively. The upper level of the hierarchy essentially operates as a decision-making algorithm. It leverages reinforcement learning (RL) techniques to facilitate optimal decision-making. The actions are generated by a constraint-space following (CSF) controller that autonomously identifies feasible directions for motion. The controller generates robot motion by adjusting its stiffness in the direction defined by the Frenet–Serret frame, which is aligned with the robot path. The proposed framework was experimentally verified through a series of challenging robotic tasks such as maze learning, door opening, learning to shift the manual car gear, and learning car license plate light assembly by disassembly.
Ključne besede: autonomous robot learning, learning, experience, compliance and impedance contro
Objavljeno v DiRROS: 21.09.2023; Ogledov: 542; Prenosov: 292
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9. Smart hardware integration with advanced robot programming technologies for efficient reconfiguration of robot workcellsTimotej Gašpar, Miha Deniša, Primož Radanovič, Barry Ridge, Thiusius Rajeeth Savarimuthu, Aljaž Kramberger, Marc Priggemeyer, Jürgen Rossmann, Florentin Wörgötter, Tatyana Ivanovska, Shahab Parizi, Žiga Gosar, Igor Kovač, Aleš Ude, 2020, izvirni znanstveni članek Objavljeno v DiRROS: 10.09.2020; Ogledov: 1772; Prenosov: 785
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