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Blog Post number 4
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publications
Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms
Published in International journal of advanced robotic systems, 2020
Lowering joint torques of a robotic manipulator enables lowering the energy it uses as well as increase in the longevity of the robotic manipulator. This article proposes the use of evolutionary computation algorithms for optimizing the paths of the robotic manipulator with the goal of lowering the joint torques. The robotic manipulator used for optimization is modelled after a realistic six-degree-of-freedom robotic manipulator. Two cases are observed and these are a single robotic manipulator carrying a weight in a point-to-point trajectory and two robotic manipulators cooperating and moving the same weight along a calculated point-to-point trajectory. The article describes the process used for determining the kinematic properties using Denavit–Hartenberg method and the dynamic equations of the robotic manipulator using Lagrange–Euler and Newton–Euler algorithms.
Recommended citation: Baressi Šegota, S., Anđelić, N., Lorencin, I., Saga, M., & Car, Z. (2020). Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms. International journal of advanced robotic systems, 17(2), 1729881420908076. https://journals.sagepub.com/doi/full/10.1177/1729881420908076
Determining the influence and correlation for parameters of flexible forming using the random forest method
Published in Applied soft computing, 2023
Single-point incremental forming (SPIF) enables the forming of fix-clamped sheet metal by moving a relatively small geometrically simple tool along the trajectory, producing the desired shape of the final product. Excessive thinning of the sheet results in fracture, determining the limit of formability. This characteristic of the forming process can be improved by upgrading the basic SPIF process to two-step forming, whereby a more even distribution of the sheet thickness can be achieved by pre-bulging with a hemispherical punch. This study focused on analysing the SPIF process and a hybrid two-step forming consisting of sequential bulging and SPIF. The analysis focused on the output parameters of sheet metal thinning and maximum forming force components and was conducted with Abaqus simulation software. An innovative new approach for influence analysis of technological, material and geometrical input …
Recommended citation: Sevšek, L., Baressi Šegota, S., Car, Z., & Pepelnjak, T. (2023). Determining the influence and correlation for parameters of flexible forming using the random forest method. Applied soft computing, 144, 110497. https://www.sciencedirect.com/science/article/pii/S156849462300515X
Regression Model for the Prediction of Total Motor Power Used by an Industrial Robot Manipulator during Operation
Published in Machines, 2024
Motor power models are a key tool in robotics for modeling and simulations related to control and optimization. The authors collect the dataset of motor power using the ABB IRB 120 industrial robot. This paper applies a multilayer perceptron (MLP) model to the collected dataset. Before the training of MLP models, each of the variables in the dataset is evaluated using the random forest (RF) model, observing two metrics-mean decrease in impurity (MDI) and feature permutation score difference (FP). Pearson’s correlation coefficient was also applied Based on the scores of these values, a total of 15 variables, mainly static variables connected with the position and orientation of the robot, are eliminated from the dataset. The scores demonstrate that while both MLPs achieve good scores, the model trained on the pruned dataset performs better. With the model trained on the pruned dataset achieving R2=0.99924,σ=0.00007 and MAPE=0.33589,σ=0.00955, the model trained on the original, non-pruned, data achieves R2=0.98796,σ=0.00081 and MAPE=0.46895,σ=0.05636. These scores show that by eliminating the variables with a low influence from the dataset, a higher scoring model is achieved, and the created model achieves a better generalization performance across five folds used for evaluation.
Recommended citation: Baressi Šegota, S., Anđelić, N., Štifanić, J., & Car, Z. (2024). Regression Model for the Prediction of Total Motor Power Used by an Industrial Robot Manipulator during Operation. Machines, 12(4), 225. https://www.mdpi.com/2075-1702/12/4/225
Modeling of Actuation Force, Pressure and Contraction of Fluidic Muscles Based on Machine Learning
Published in Technologies, 2024
In this paper, the dataset is collected from the fluidic muscle datasheet. This dataset is then used to train models predicting the pressure, force, and contraction length of the fluidic muscle, as three separate outputs. This modeling is performed with four algorithms—extreme gradient boosted trees (XGB), ElasticNet (ENet), support vector regressor (SVR), and multilayer perceptron (MLP) artificial neural network. Each of the four models of fluidic muscles (5-100N, 10-100N, 20-200N, 40-400N) is modeled separately: First, for a later comparison. Then, the combined dataset consisting of data from all the listed datasets is used for training. The results show that it is possible to achieve quality regression performance with the listed algorithms, especially with the general model, which performs better than individual models. Still, room for improvement exists, due to the high variance of the results across validation sets, possibly caused by non-normal data distributions.
Recommended citation: Baressi Šegota, Sandi, et al. "Modeling of Actuation Force, Pressure and Contraction of Fluidic Muscles Based on Machine Learning." Technologies 12.9 (2024): 161. http://academicpages.github.io/files/paper1.pdf
talks
Preparation of Simplified Molecular Input Line Entry System Notation Datasets for use in Convolutional Neural Networks
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Johann Palisa 1848-1925
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teaching
Electronics
Laboratory Exercises, Faculty of Engineering - University of Rijeka, 2020
Programming
Laboratory Exercises, Faculty of Engineering - University of Rijeka, 2020
Basics of robotics
Laboratory and Auditory Exercises, Faculty of Engineering - University of Rijeka, 2020
Robotics in industry
Laboratory and Auditory Exercises, Faculty of Engineering - University of Rijeka, 2020
Evolutionary robotics
Laboratory and Auditory Exercises, Faculty of Engineering - University of Rijeka, 2020
Application of Artificial Intelligence
Laboratory and Auditory Exercises, Faculty of Engineering - University of Rijeka, 2020