![]() Results have shown to be concise and accurate, with minor model displacement inaccuracies because of differences between the virtual and real models.Īn intuitive drag and drop layout modeling accelerates the design and setup of robotic palletizing cells and automatic offline generation of robot programs. ![]() The AdaptPack Studio software was tested and evaluated in a pure simulation case and in a real-world scenario. ![]() Simulation and real tests performed showed an improvement in the design, development and operation of robotic palletizing systems. This framework, named AdaptPack Studio, offers a custom-built library to assemble virtual models of palletizing cells, quick connect these models by drag and drop, and perform offline programming of robots and factory equipment in short steps. An automatic offline programming tool, for a variety of robot brands, is also introduced. This paper aims to propose an automated framework for agile development and simulation of robotic palletizing cells. The comparable results also show that our method is superior to the original YOLO-V3, SSD, and Faster R-CNN for PB data, and has a detection AP of 86.7%, a recall rate of 97%, and a detection speed of 25.47 FPS, which can achieve high-efficiency and high-precision detection in complex industrial scenarios. The experimental results demonstrated the effectiveness of the proposed improvement mechanisms. Finally, a new bounding box regression loss function was elaborated to accelerate the neural network training. In this way, the low-level semantics and high-level abstract features can be extracted effectively to improve detection performance. According to the feature of the PB data in the palletizing robot, such as the existence of dust and dirt on the surface, the feature extraction network was further enhanced by adding a Densenet-4 module. Then, an improved anchor box mechanism based on the k-means++ algorithm was designed to obtain a more accurate anchor box for the PB data. First, due to the actual detection requirement, we constructed the PB data set by using a series of data enhancement operations such as horizontal flip, ± 30degree rotation, and random luminance enhancement or decrease. To improve the detection accuracy and speed of palletizing robot positioning bolts in complex scenes, we proposed a positioning bolt (PB) detection method based on improved YOLO-V3. Developed in the robot's native language (RAPID), the application has a basic user interface written in XML and can provide different pallet patterns. This application, together with an off-line programming software (RobotStudio), allows for automatic programming of a robot's palletizing functions. In this context, this work aims to contextualise and develop an application for palletizing robots. Subsequently, palletizing applications are amongst the handling operations that have played an important role in the end stages of modern supply chains. Within the several activities for robots on industrial applications, handling operations are regarded as predominant on the European market. Hence, it is of no wonder that the worldwide operational stock of industrial robots has been increasing in a steady pace for the past decades and is expected to progress in such a trend. Considering that a fourth industrial revolution is to be expected in a near future-which is highly based on smart machines, storage systems, and production facilities that cooperate to allow dynamic businesses and engineering processes-robotics presents itself as an increasingly sought-after solution, since it is often associated with such concepts. This software is a package design application used for creating optimal boxes and pallet arrangements.Current market demands require several degrees of flexibility, speed, and repetitiveness of manufacture and logistic processes. The Koona Software Quick Pallet Maker can be used in Mac operating systems and in Microsoft Windows based systems to access and open the data stored in these QPMfiles. The QPM extension is carried by Windows and Mac OSX machines, but not in Mac OS Classic. These text files can be read by any text reading application and can be saved into the hard disk or any network disk. These files are used for calculating shipping pallet arrangements from primary package dimensions. Specifically they contain the main Input Window and the Dividers window information. ![]() These QPM files are generally classified as computer aided design files that contain primary package information and box and pallet settings. The Quick Pallet Maker Input Data is stored in the QPM format and is affixed with the QPM extension, and is used by the Quick Pallet Maker software. What is a qpm file and how do I open a qpm file? ![]()
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