This study presents an integrated approach to the parametric modeling and structural optimization of mechanical components, emphasizing assembly compatibility and failure risk minimization. The parametric modeling is conducted in CAD software, leveraging its robust CAD capabilities to create flexible, parameter-driven component designs. Finite element analysis (FEA) and multi-objective optimization are performed in MATLAB, utilizing its computational power to evaluate and enhance the mechanical performance of the components. A seamless communication framework between CAD software and MATLAB is established to enable an iterative optimization cycle. The optimization process aims to minimize natural frequency deviations, stress, strain and deformation, while ensuring that the components adhere to strict assembly constraints and maintain structural integrity. This methodology demonstrates an efficient workflow for designing mechanically optimized components that meet both functional and assembly requirements. Unlike previous studies that address structural performance and assembly constraints separately, this work explicitly integrates both within a unified optimization loop, providing a practical and scalable solution for real-world shaft design problems. The proposed framework is validated through case studies, showcasing its effectiveness in improving performance and reducing failure risks in mechanical systems.

In this article, we investigate the application of Lagrange polynomials for compressing data vectors within the context of constrained model predictive control (MPC). Lagrange polynomials, traditionally used for interpolating among n data points, are leveraged in a novel manner to facilitate efficient representation and computation of control signals in MPC systems. The study demonstrates that Lagrange polynomials of degree n, designed to pass through n specified data points, can be utilized to compress input signal vectors in SISO/MIMO state-space models employed in MPC. By selectively choosing a subset of the original data vector sampled at a constant rate and constructing a Lagrange polynomial to interpolate through this subset, the remaining data points can be accurately estimated through interpolation. The application of Lagrange polynomials in this context enables significant reduction in computational complexity, crucial for real-time implementation of MPC algorithms particularly with longer prediction horizons. Moreover, by representing control signals within the prediction horizon using a fixed-size subset and exploiting Lagrange interpolation for the rest, the computational demands of solving the constrained quadratic optimization problem inherent in MPC become independent of the prediction horizon length. This innovation holds promise for enhancing the feasibility and efficiency of MPC controllers in controlling fast dynamical systems, where rapid decision-making is paramount.

This study investigates the use of satellite imagery for robotics applications, specifically focusing on evaluating a hybrid satellite image segmentation method against conventional soft-computing techniques. The research is conducted in Ankara, where a satellite image is processed and segmented into road, building, vegetation/forest, and ground categories using several methods. Supervised methods are trained on sample images from the city district satellite image and compared to manual segmentation conducted in Adobe Photoshop. Results from the Feed-forward Neural Network (FNN) and Probabilistic Neural Network (PNN) are contrasted with the Hybrid method, which demonstrates robust performance in identifying features such as buildings, roads, ground, and forest. The hybrid method achieves a higher Kappa coefficient (0.4060) compared to FNN (0.3908) and PNN (0.3757), indicating superior segmentation accuracy. Challenges persist in distinguishing similar color spectrums, particularly between ground and other classes. Future research directions involve refining color-based feature extraction methods and exploring advanced machine learning techniques to enhance segmentation accuracy, especially in complex urban environments. The application of satellite imagery holds great potential for robotic mapping and navigation, with opportunities to integrate multi-modal data sources and deploy deep learning architectures for more reliable performance. As researchers continue to innovate in satellite image analysis, the prospects for transformative advancements in robotics applications remain compelling.

Localization is one of the major research fields in mobile robotics. With the utilization of satellite images and Monte Carlo localization technique, the global localization of an outdoor mobile robot is studied in this paper. The proposed method employs satellite images downloaded from the Internet to localize the robot iteratively. To accomplish this, the proposed method matches the local laser scanner data with the segmented satellite images. Initial test results conducted on the METU campus are found to be quite promising. Further improvement of this approach has the potential of cutting down not only the operational costs but also the preparation period of the mobile robot enabling researchers to operate their robots in diverse outdoor settings.

Localization of mobile robots has been studied rigorously in the last decade. A number of successful approaches such as Extended Kalman Filter, Markov Localization, and Monte Carlo Localization assume that the map of the environment is originally presented to the robot. However, an important information package like the map of the environment could not be taken for granted in most real- world problems. In this study, a novel technique composed of a combination of Fuzzy C-Means and Fuzzy Neural Network methods is proposed to segment and convert a satellite image into a digital map for outdoor mobile robot applications.