Annis Fathima Aklak

The Capsule Shape All-Terrain Robot (CS-ATR) is designed to address the challenges of navigating rough and unpredictable environments, such as disaster zones, where stability, maneuverability, and adaptability are critical. This study presents the design and development of the CS-ATR, focusing on its perpendicular dual-track system, real-time PID-controlled tilt correction mechanism specifically for capsule-shaped robots, and autoencoder-based anomaly detection system. The perpendicular dual-track system enables the robot to perform precise lateral movements, allowing navigation through confined spaces, while the PID controller ensures continuous real-time adjustments to maintain balance across uneven terrains. The variational autoencoder enhances the robot’s ability to detect and correct abnormal movements, improving overall system reliability. Extensive simulation experiments using Simscape™ were conducted to evaluate the robot’s ability to maintain balance and adapt to disturbances, such as rocks and pits. Additionally, the performance of the physical prototype was tested on various terrains, including river sand, rocky paths, and simulated pits, to validate its operational effectiveness in real-world conditions. The results demonstrate that CS-ATR effectively overcomes obstacles, maintains stability, adapts to changes in traction, and corrects anomalies, making it a promising solution for applications such as search and rescue missions.

The prominence of image processing in today’s cutting-edge technology is undeniable. Integrating software with hardware leverages both strengths, resulting in a real-time processing system that is efficient and streamlined. Raw images are usually affected by noise, which hinders the acquisition of good-quality and detailed images; hence, denoising becomes necessary. This paper proposes a modified min-max median (MMM) filter to remove impulse noise and a Tritonic sorter to localize corrupted pixels. The proposed denoising method focuses on localizing noisy pixels, unlike traditional denoising approaches, which focus only on noise detection and filtering. A min-max sheet provides the location of the corrupted pixels, and filtering is performed on them. The Tritonic Sorter, consisting of a max locator and a min locator, compares three input values and finds the minimum, maximum and median values among them. Compared to other state-of-the-art methods, the proposed method minimizes the number of comparators needed to carry out the sorting process. The proposed method was synthesized in the ZedBoard Zynq kit using the Vivado tool. The results show that the area improved by 27%, and the power improved by 16.23% compared with those of the existing method.

In real‐time, the images captured are not always appreciable for visual perception. The quality of the image relies on the illumination condition. Contrast enhancement is required to improve the visual quality of the image. In this article, the image enhancement techniques that are adaptable for real‐time images are studied. Further, an investigation is done on implementing the image processing algorithms in very large‐scale integration (VLSI) hardware. As the image data are comparatively larger, the computational complexity and the memory requirement are more. From the study, the need for efficient computation of such image processing algorithms is realized. The efficient computation of image processing algorithms on hardware platforms, in particular on field programmable gate array (FPGA), is reviewed. As an experiment, the gamma‐transformation technique used for image enhancement is realized. Initially, the prototype is modeled using MATLAB, and then it is implemented using an FPGA.