In computer vision and mobile robotics, autonomous navigation is crucial. It enables the robot to navigate its environment, which consists primarily of obstacles and moving objects. Robot navigation employing impediment detections, such as walls and pillars, is not only essential but also challenging due to real-world complications. This study provides a real-time solution to the problem of obtaining hallway scenes from an exclusive image. The authors predict a dense scene using a multi-scale fully convolutional network (FCN). The output is an image with pixel-by-pixel predictions that can be used for various navigation strategies. In addition, a method for comparing the computational cost and precision of various FCN architectures using VGG-16 is introduced. The binary semantic segmentation and optimal obstacle avoidance navigation of autonomous mobile robots are two areas in which our method outperforms the methods of competing works. The authors successfully apply perspective correction to the segmented image in order to construct the frontal view of the general area, which identifies the available moving area. The optimal obstacle avoidance strategy is comprised primarily of collision-free path planning, reasonable processing time, and smooth steering with low steering angle changes.

This research paper proposes a real-time obstacle avoidance strategy for mobile robots with a monocular camera. The approach uses a binary semantic segmentation FCN-VGG-16 to extract features from images captured by the monocular camera and estimate the position and distance of obstacles in the robot’s environment. Segmented images are used to create the frontal view of a mobile robot. Then, the optimized path planning based on the enhanced A* algorithm with a set of weighted factors, such as collision, path, and smooth cost improves the performance of a mobile robot’s path. In addition, a collision-free and smooth obstacle avoidance strategy will be devised by optimizing the cost functions. Lastly, the results of our evaluation show that the approach successfully detects and avoids static and dynamic obstacles in real time with high accuracy, efficiency, and smooth steering with low angle changes. Our approach offers a potential solution for obstacle avoidance in both global and local path planning, addressing the challenges of complex environments while minimizing the need for expensive and complicated sensor systems.

This paper reports the synthesis of a new printable ABS–MWCNT composite filament, for use in fused deposition modeling (FDM), using an extrusion technique. Acrylonitrile butadiene styrene (ABS) and multi-walled carbon nanotubes (MWCNTs) were the initial materials used for fabricating the filaments. The MWCNTs were dispersed in ABS resin, then extruded through a single-shaft extruder in filament form, with MWCNT contents of 0.5%, 1%, 1.5%, 2%, 3% or 4% by weight. After extrusion, the diameter of the filaments was about 1.75 mm, making them appropriate for FDM. The as-synthesized filaments were then used in FDM to print out samples, on which tensile tests and other analyses were carried out. The results demonstrate that the sample with 2% MWCNTs had the highest strength value, 44.57 MPa, comprising a 42% increase over that of the pure ABS sample. The morphology and dispersion of MWCNTs in the composite were observed by field emission scanning electron microscopy (FESEM), demonstrating the uniform distribution of MWCNTs in the ABS matrix. The thermal behavior results indicated no significant change in the ABS structure; however, the melt flow index of the filaments decreased with an increase in the MWCNT content.

Enhancing the operational efficacy of electrical discharge machining (EDM) is crucial for achieving optimal results in various engineering materials. This study introduces an innovative solution—the use of coated electrodes—representing a significant advancement over current limitations. The choice of coating material is critical for micro-EDM performance, necessitating a thorough investigation of its impact. This research explores the application of different coating materials (AlCrN, TiN, and Carbon) on WC electrodes in micro-EDM processes specifically designed for Ti-6Al-4V. A comprehensive assessment was conducted, focusing on key quality indicators such as depth of cut (Z), tool wear rate (TWR), overcut (OVC), and post-machining surface quality. Through rigorous experimental methods, the study demonstrates substantial improvements in these quality parameters with coated electrodes. The results show significant enhancements, including increased Z, reduced TWR and OVC, and improved surface quality. This evidence underscores the effectiveness of coated electrodes in enhancing micro-EDM performance, marking a notable advancement in the precision and quality of Ti–6Al–4V machining processes. Among the evaluated coatings, AlCrN-coated electrodes exhibited the greatest increase in Z, the most significant reduction in TWR, and the best OVC performance compared to other coatings and the uncoated counterpart.

In this paper, design and optimization method of a three-phase dual-active-bridge DC/DC converter is discussed. Three single phase transformers connected in star-star configuration were designed with large leakage inductance aiming to eliminate the need for external inductors. Switching frequency, peak flux density, number of turns, number of layers, etc., were optimized using non-linear programming technique for minimizing the overall converter loss. Experimental results on a 10 kW prototype show that the optimized converter can operate efficiently an efficacy of up to 98.65% and a low-temperature rise of less than 70 degrees Celsius on both transformers and semiconductor devices.

Precise control of overhead crane systems, especially when there are disturbances, is crucial for ensuring safety and efficiency in operations. This paper addresses tracking control for overhead cranes that have variable cable lengths. It employs the Takagi-Sugeno (T-S) descriptor fuzzy method combined with Input Shaping (IS) techniques to significantly reduce payload oscillation. By representing the nonlinear system as local linear sub-models over different operating regions, the T-S descriptor fuzzy method effectively controls the crane system’s high nonlinearity. The Parallel Distributed Compensation (PDC) Integral controller is integrated into the design framework to enhance tracking control performance. Using Lyapunov theory, linear matrix inequalities (LMIs) are constructed, and the controller parameters are determined by solving these inequalities. By incorporating H-infinity or L-infinity performance criterion into the fuzzy model, robust controllers are developed to effectively reject disturbances and ensure tracking performance. Finally, simulations are conducted to evaluate the performance and effectiveness of the proposed controllers.