1. Enhancing robotic bin-picking through machine vision: investigating the impact of detection speed and bin fill levels : preučevanje vpliva zaznave objektov ob različno napolnjenih zabojihSuhaib Ebrahim Mambayil Ebrahimkutty, 2025, undergraduate thesis Abstract: Robotic bin-picking systems have become important in modern intralogistics as warehouses automate to address labour shortages and rising e-commerce demands. These systems operate through the coordinated use of (a) a robotic manipulator, (b) a 3D machine vision system, and (c) robotic grippers. When integrated with Automated Storage and Retrieval Systems (AS/RS), these systems offer an efficient approach to improving the order-picking performance.
In this study, the performance of a selected 3D machine vision system was evaluated based on two key parameters: detection mode and bin fill levels. A series of structured laboratory experiments was conducted using a UR5e collaborative robot equipped with three types of robotic grippers and a Pickit M-HD2 3D vision system. Objects of varying shapes and orientations were tested under different detection modes and bin fill levels. Faster detection modes reduced the processing time but resulted in more detection failures, especially with complex shapes or densely populated bins. In contrast, slower modes improved accuracy but increased the cycle time. Normal mode offered the best balance. The detection reliability decreased at higher bin fill levels and with irregularly shaped objects, due to occlusion and limited visibility. By analysing the detection time and successful detection, insights were gained into how appropriate detection configurations can improve both reliability and throughput in robotic bin-picking systems integrated with AS/RS.
Keywords: intralogistics, Robotic Bin-Picking, machine vision system, object detection
Published in DKUM: 31.07.2025; Views: 0; Downloads: 12
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2. Robotic bin-picking : benchmarking robotics grippers with modified YCB object and model setTone Lerher, Primož Bencak, Luka Bizjak, Darko Hercog, Boris Jerman, 2023, published scientific conference contribution Abstract: Robotic bin-picking is increasingly important in the order-picking process in intralogistics. However, many aspects of the robotic bin-picking process (object detection, grasping, manipulation) still require the research community's attention. Established methods are used to test robotic grippers, enabling comparability of the research community's results. This study presents a modified YCB Robotic Gripper Assessment Protocol that was used to evaluate the performance of four robotic grippers (twofingered, vacuum, gecko, and soft gripper). During the testing, 45 objects from the modified YCB Object and Model Set from the packaging categories, tools, small objects, spherical objects, and deformable objects were grasped and manipulated. The results of the robotic gripper evaluation show that while some robotic grippers performed substantially well, there is an expressive grasp success variation over diverse objects. The results indicate that selecting the object grasp point next to selecting the most suitable robotic gripper is critical in successful object grasping. Therefore, we propose grasp point determination using mechanical software simulation with a model of a two-fingered gripper in an ADAMS/MATLAB cosimulation. Performing software simulations for this task can save time and give comparable results to real-world experiments.
Keywords: intralogistics, robotic bin-picking, YCB protocol, robotic gripper evaluation, mechanical software simulations, performance analysis
Published in DKUM: 21.03.2024; Views: 298; Downloads: 21
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3. Object detection and graspability analysis for robotic bin-picking application in intralogisticsPrimož Bencak, Darko Hercog, Tone Lerher, 2023, published scientific conference contribution Abstract: Robotics has been gaining attention in intralogistics applications in recent years. Automation of intralogistics processes aims to cope with the rising trends of workforce deficiency, aging, and increasing demands that came with the rise of E-commerce. Many improvements aim at bin-picking applications since order-picking requires most contributions while adding little to the products' value. Robotic bin-pickers are showing promising results; however, they are still subject to many limitations. First, the vision system must correctly determine the object's location and orientation. Second, a correct robotic gripper must be chosen. Lastly, appropriate grasping points that lead to successful picking must be selected. In this paper, we explore the influencing parameters of object detection using a 3D vision system. Second, we analyze an actual bin-picking application to determine the most appropriate selection of the robotic gripper. Based on the experiments, we provide the guidelines for selecting the most appropriate robotic bin-picking configuration.
Keywords: intralogistics, robotic bin-picking, detection analysis, graspability analysis
Published in DKUM: 25.07.2023; Views: 407; Downloads: 39
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4. Evaluating robot bin-picking performance based on Box and Blocks TestPrimož Bencak, Darko Hercog, Tone Lerher, 2022, published scientific conference contribution Abstract: The rise of e-commerce, which demands solutions for small order sizes, large product assortment, short delivery times, and variable order quantity has created a need for more advanced warehousing and order-picking systems. The advancements in collaborative robotics have made it possible to automate intralogistics processes, especially the order-picking systems. If the items to be order-picked are stored in small containers, called bins, the process is known as bin-picking. The processes of bin-picking are still mainly manual due to the adaptability, dexterity, and pace of human hands. However, with the emerging technologies, the gap between human and robot dexterity is getting thinner. The integrators of robotic technologies are therefore faced with the challenge of choosing an appropriate robotic system for bin-picking. There is a lack of standardized procedures, which are designed to process such decisions easier. In our research paper, we propose an improved robotic version of the Box and Blocks Test (BBT), which provides a quick evaluation of a complete robotic bin-picking system. Using our improved robot-adapted BBT protocol, we evaluate a sample robotic bin-picking system, consisting of a collaborative robot, a 3D vision system and three types of robotic grippers, comparing the bin-picking performance of each configuration.
Keywords: robotic bin-picking, intralogistics, Box, Blocks Test, vision system, collaborative robot
Published in DKUM: 03.03.2023; Views: 718; Downloads: 88
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5. Simulation model for robotic pick-point evaluation for 2-F robotic gripperPrimož Bencak, Darko Hercog, Tone Lerher, 2023, original scientific article Abstract: Robotic bin-picking performance has been gaining attention in recent years with the development of increasingly advanced camera and machine vision systems, collaborative and industrial robots, and sophisticated robotic grippers. In the random bin-picking process, the wide variety of objects in terms of shape, weight, and surface require complex solutions for the objects to be reliably picked. The challenging part of robotic bin-picking is to determine object pick-points correctly. This paper presents a simulation model based on ADAMS/MATLAB cosimulation for robotic pick-point evaluation for a 2-F robotic gripper. It consists of a mechanical model constructed in ADAMS/View, MATLAB/Simulink force controller, several support functions, and the graphical user interface developed in MATLAB/App Designer. Its functionality can serve three different applications, such as: (1) determining the optimal pick-points of the object due to object complexity, (2) selecting the most appropriate robotic gripper, and (3) improving the existing configuration of the robotic gripper (finger width, depth, shape, stroke width, etc.). Additionally, based on this analysis, new variants of robotic grippers can be proposed. The simulation model has been verified on a selected object on a sample 2-F parallel robotic gripper, showing promising results, where up to 75% of pick-points were correctly determined in the initial testing phase.
Keywords: intralogistics, robotic bin-picking, simulation model, ADAMS, pick-point determination, MATLAB/Simulink, 2-F robotic gripper, performance analysis
Published in DKUM: 27.02.2023; Views: 764; Downloads: 127
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