Publications
Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations
Feb 4, 2025Journal Kufa Journal of Engineerin
Publisher Kufa Journal of Engineerin
DOI 10.30572/2018/KJE/160109
Issue Vol. 16 No. 1 (2025): January - 2025
Volume 16
The importance of deep learning has heralded transforming changes across different technological domains, not least in the enhancement of robotic arm functionalities of object detection’s and grasping. This paper is aimed to review recent and past studies to give a comprehensive insight to focus in exploring cutting-edge deep learning methodologies to surmount the persistent challenges of object detection and precise manipulation by robotic arms. By integrating the iterations of the You Only Look Once (YOLO) algorithm with deep learning models, our study not only advances the innovations in robotic perception but also significantly improves the accuracy of robotic grasping in dynamic environments. Through a comprehensive exploration of various deep learning techniques, we introduce many approaches that enable robotic arms to identify and grasp objects with unprecedented precision, thereby bridging a critical gap in robotic automation. Our findings demonstrate a marked enhancement in the robotic arm’s ability to adapt to and interact with its surroundings, opening new avenues for automation in industrial, medical, and domestic applications. The impact of this research extends lays the groundwork for future developments in robotic autonomy, offering insights into the integration of deep learning algorithms with robotic systems. This also serves as a beacon for future research aimed at fully unleashing the potential of robots as autonomous agents in complex, real-world settings.
Design and implementation of image based object recognition
Feb 1, 2020Journal Periodicals of Engineering and Natural Science
Publisher Periodicals of Engineering and Natural Science
DOI DOI: 10.21533/pen.v8i1.1114.g490
Issue ol. 8, No. 1, February 2020, pp.79-
Volume Vol. 8
The aim of this paper is to design and implement image based object recognition. This represents more of a challenge when speaking of advance object recognition systems. A practical example of this issue is the recognition of objects in images. This is a task that humans can perform very well, but convolutional neural network systems don't struggle to perform. AlexNet pre-trained model was used for the training the dataset because of it trouble-free architecture on very large scale dataset "Cifar-10" using R2019a Matlab. The dataset was split with the ratio of 70% for training and 30% for the testing part. This has prompted convolutional neural network to start experimenting with networks architectures as well as new algorithms to train them. This research paper presents an approach to train networks such as to improve their robustness to the recognition of object images on R2019a Matlab. This training strategy is then evaluated for designed AlexNet network architecture. The result of the study was that the training algorithm could improve robustness to different image recognition at the expense of an increase in performance for the performance of images of objects (i.e. Dog, Frog, Deer, Automobile, Airplane etc.) with high accuracy of recognition. When the advantages of different types of architectures were evaluated, it was found that accuracy of all object recognition were around 98% based on the image. It followed the findings from classical object recognition that feed-forward neural networks could perform as well their high accuracy of recognition.