منصة إيفاد العلمية

سارة صبيح بداي

إرسال رسالة

التخصص: ماجستير هندسة حاسبات

الجامعة: البصرة

النقاط:

12.5

معامل الإنتاج البحثي

الخبرات العلمية

خبره واسعه في التدريس، خبرة في النشر العلمي، خبرة في البوربوينت، خبرة في مجال الذكاء الاصطناعي
خبرة في مجال الروبوتات

الأبحاث المنشورة

Issues and Research Fields of Medical Robotics: A Review

المجلة: Iraqi Journal for Electrical and Electronic Engineering

سنة النشر: 2023

تاريخ النشر: 2024-08-24

Abstract The goal for collaborative robots has always driven advancements in robotic technology, especially in the manufacturing sector. However, this is not the case in service sectors, especially in the health sector. Thus, this lack of focus has now opened more room for the design and development of service robots that can be used in the health sector to help patients with ailments, cognitive problems, and disabilities. There is currently a global effort towards the development of new products and the use of robotic medical devices and computer-assisted systems. However, the major problem has been the lack of a thorough and systematic review of robotic research into disease and epidemiology, especially from a technology perspective. Also, medical robots are increasingly being used in healthcare to perform a variety of functions that improve patient care. This scoping review is aimed at discovering the types of robots used in healthcare and where they are deployed. Moreover, the current study is an overview of various forms of robotic technology and its uses the healthcare industry. The considered technologies are the products of a partnership between the healthcare sector and academia. They demonstrate the research and testing that are necessary for the service of robot development before they can be employed in practical applications and service scenarios. The discussion also focused on the upcoming research areas in robotic systems as well as some important technologies necessary for human-robot collaboration, such as wireless sensor networks, big data, and artificial intelligence.

Comparative Analysis of GA-PRM Algorithm Performance in Simulation and Real-World Robotics Applications

المجلة: Misan Journal of Engineering Sciences

سنة النشر: 2023

تاريخ النشر: 2024-08-24

Abstract: This paper presents a comprehensive analysis of the performance of the Genetic Algorithm Probabilistic Roadmap (GA-PRM) algorithm in both simulated and real-world robotic environments. The GA-PRM algorithm is a promising approach for robot path planning, and understanding its behavior in different settings is crucial for its practical applications. In simulations, we explore the advantages of controlled and reproducible test conditions, allowing for extensive parameter tuning and algorithm improvement. Real-world testing is employed to validate the algorithm's performance in actual robotic environments, taking into account the inherent complexities and uncertainties present. In our comparative analysis, we found that the GA-PRM algorithm demonstrates significant improvements in real-world scenarios compared to simulations. Specifically, the algorithm produced shorter paths in realworld robot testing, with an average length of 21.428 cm, as opposed to 25.6235 units in simulations. Moreover, the computational efficiency of the algorithm was notably enhanced in the real-world environment, where it took only 0.375 seconds on average to plan paths, compared to 0.6881 seconds in simulations. The algorithm also exhibited higher path smoothness in the real world, with an average smoothness score of 0.432, compared to 0.3133 in simulations. These results underscore the algorithm's adaptability to real-world conditions and its potential for efficient navigation in practical healthcare and automation applications. Our research bridges the gap between simulation and reality, facilitating the development of more reliable and adaptable robotic systems. The insights gained from this comparative evaluation contribute to a deeper understanding of the GA-PRM algorithm's behavior and its potentials.

Article Efficient Path Planning in Medical Environments: Integrating Genetic Algorithm and Probabilistic Roadmap (GA-PRM) for Autonomous Robotics

المجلة: Iraqi Journal for Electrical and Electronic Engineering

سنة النشر: 2024

تاريخ النشر: 2024-08-24

Abstract Path-planning is a crucial part of robotics, helping robots move through challenging places all by themselves. In this paper, we introduce an innovative approach to robot path-planning, a crucial aspect of robotics. This technique combines the power of Genetic Algorithm (GA) and Probabilistic Roadmap (PRM) to enhance efficiency and reliability. Our method takes into account challenges caused by moving obstacles, making it skilled at navigating complex environments. Through merging GA’s exploration abilities with PRM’s global planning strengths, our GA-PRM algorithm improves computational efficiency and finds optimal paths. To validate our approach, we conducted rigorous evaluations against well-known algorithms including A*, RRT, Genetic Algorithm, and PRM in simulated environments. The results were remarkable, with our GA-PRM algorithm outperforming existing methods, achieving an average path length of 25.6235 units and an average computational time of 0.6881 seconds, demonstrating its speed and effectiveness. Additionally, the paths generated were notably smoother, with an average value of 0.3133. These findings highlight the potential of the GA-PRM algorithm in real-world applications, especially in crucial sectors like healthcare, where efficient path-planning is essential. This research contributes significantly to the field of path-planning and offers valuable insights for the future design of autonomous robotic systems.

Forecasting Robot Movement with Sensor Readings and MultiLayer Perceptron Models

المجلة: Misan Journal of Engineering Sciences

سنة النشر: 2024

تاريخ النشر: 2024-08-24

Abstract: Classification of sensor data is applied in several domains and enables application tasks such as fault detection, event recognition, and predictive maintenance. This paper proposes an application of Multi-Layer Perceptron networks for the classification of noisy and imbalanced sensor data. In contrast to conventional methods, the proposed approach addresses imbalanced classes, noisy signals, and high-dimensional data using state-of-the-art preprocessing techniques and thorough hyperparameter tuning. Using a dataset collected from a SCITOS G5 mobile robot fitted with 24 ultrasound sensors, this research discusses in depth the intricacies of Multi-Layer Perceptron (MLP) neural networks for the optimal performance of their architecture and training process. Experimentation and analysis show that the proposed system yielded a good accuracy of 93.04% on the test dataset, surpassing traditional techniques such as Support Vector Machines and Logistic Regression. In addition, thorough evaluation metrics on accuracy, precision, recall, and F1 score demonstrate the model's MLP efficacy across different classes of movements. In this respect, the study shows not only the effectiveness of MLP networks in sensor data classification but also best practices in handling challenges that come with the handling of sensor data.

Enhancing Robotic Grasping Performance through Data-Driven Analysis

المجلة: Misan Journal of Engineering Sciences

سنة النشر: 2024

تاريخ النشر: 2024-08-24

Abstract: In automation, reliability in robotic grasping in dynamic environment is still a problem encountered. Further, there is the need to consider deep learning methods, as traditional approaches are not easily flexible in dealing with different objects and situations. In this work, we aim to analyze how well deep neural network models perform in predicting grasp strength based on data collected from the Smart Grasping Sandbox simulation trials. Hence, the proposed approach for analyzing the joint positions, velocities and efforts led to the design of a deep neural network for improving robot grasp performance. The question here could is if deep learning could help better predict grasp stability. To implement the method, we underwent rigorous data pre-processing such as outlier detection, and feature normalization and employed a structured neural network model for training. Our model got a training accuracy of nearly 99% and the test accuracy of nearly 96% demonstrating significant promise. These results were also better than CNN and LSTM where their accuracy rate was 94.12% and 91.81%, respectively. High deep learning performance was proven in robotic grasping, which can contribute to the creation of more flexible and sophisticated robotic platforms. This work also provides the foundation for subsequent research in robotics and automation, with emphasis on the role of data-driven methods in robotic grasping tasks