A digital twin-based optimization procedure is presented for an ultraprecision motion system with a flexible shaft connecting the motor to the (elastic) load, which is subject to both backlash and friction. The main contributions of the study are the design of the digital twin and its implementation, assuming a two-mass drive system. The procedure includes the virtual representation of mechanical and electrical components, non-linearities (backlash and friction), and the corresponding control system. A procedure for digital twin-based optimization is also presented, in which the maximum absolute position error is minimized while maintaining accuracy with no significant increase in the control effort. The optimal settings for the controller parameters and for the backlash peak amplitude, the backlash peak time, and the hysteresis amplitude are then determined, in order to guarantee an appropriate dynamic response in the presence of backlash and friction. The surface quality of certain manufactured components, such as hip and knee implants, depends on the smoothness and the accuracy of the real trajectory produced in the cutting process that is strongly influenced by the maximum position error. The simulations and experimental studies are presented using a real platform and two references for trajectory control, and a comparison of four digital twin-based optimization methods. The simulation study and the real-time experiments demonstrate the suitability of the digital twin-based optimization procedure and lay the foundations for the implementation of the proposed method at an industrial level.
The characteristics of pneumatic artificial muscles -or McKibben muscles -make them very interesting for the development of robotic applications that feature low impedance in terms of their interaction with the environment, such as can be the case with orthoses or certain wearable robots. In order to research the applicability of these actuators in industrial applications, an experimental one-degree-of-freedom set-up based on pneumatic muscles manufactured by Festo has been built at Ikerlan. This paper presents the modelling of a pneumatic muscle in Modelica as a new component. After that, the paper describes the set-up constructed and shows the complete model in Dymola/Modelica, in addition to validation of the model with some experimental data.
This paper presents the implementation of a DAG-CNN which aims to classify and verify the authenticity of the offline signatures of 3 users, using the writer-independent method. In order to develop this work, 2 databases (training / validation and testing) were built manually, i.e. the manual collection of the signatures of the 3 users as well as forged signatures made by people not belonging to the base and altered by the same users were done, and signatures of another 115 people were used to create the category of non-members. Once the network is trained, its validation and subsequent testing is performed, obtaining overall accuracies of 99.4% and 99.3%, respectively, showing the features learned by the network and verifying the ability of this configuration of neural network to be used in applications for identification and verification of offline signatures.
This paper presents the implementation and evaluation of different convolutional neural network architectures focused on food segmentation. To perform this task, it is proposed the recognition of 6 categories, among which are the main food groups (protein, grains, fruit, and vegetables) and two additional groups, rice and drink or juice. In addition, to make the recognition more complex, it is decided to test the networks with food dishes already started, i.e. during different moments, from its serving to its finishing, in order to verify the capability to see when there is no more food on the plate. Finally, a comparison is made between the two best resulting networks, a SegNet with architecture VGG-16 and a network proposed in this work, called Residual Segmentation Convolutional Neural Network or ResSeg, with which accuracies greater than 90% and interception-over-union greater than 75% were obtained. This demonstrates the ability, not only of SegNet architectures for food segmentation, but the use of residual layers to improve the contour of the segmentation and segmentation of complex distribution or initiated of food dishes, opening the field of application of this type of networks to be implemented in feeding assistants or in automated restaurants, including also for dietary control for the amount of food consumed.
This article presents a work oriented to assistive robotics, where a scenario is established for a robot to reach a tool in the hand of a user, when they have verbally requested it by his name. For this, three convolutional neural networks are trained, one for recognition of a group of tools, which obtained an accuracy of 98% identifying the tools established for the application, that are scalpel, screwdriver and scissors; one for speech recognition, trained with the names of the tools in Spanish language, where its validation accuracy reach a 97.5% in the recognition of the words; and another for recognition of the user's hand, taking in consideration the classification of 2 gestures: Open and Closed hand, where a 96.25% accuracy was achieved. With those networks, tests in real time are performed, presenting results in the delivery of each tool with a 100% of accuracy, i.e. the robot was able to identify correctly what the user requested, recognize correctly each tool and deliver the one need when the user opened their hand, taking an average time of 45 seconds in the execution of the application.
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