Improving NAO Robot Grasps with Deep Learning-Based Rectangle Detection

Robotic grasping precision is crucial for effective manipulation of objects in various applications, especially in manufacturing and robotics. This paper presents a novel approach to improve grasping performance for NAO robots. We propose a deep learning-based method that utilizes rectangle detection to enhance the accuracy and reliability of grasping tasks. The approach leverages Convolutional Neural Networks (CNNs) to identify optimal grasping rectangles within images of pieces. This enables the NAO robot to precisely grasp pieces based on their rectangular features, leading to a significant improvement in grasping success rates compared to traditional methods. The paper details the design of the CNN architecture, the training process, and the evaluation of the proposed method on real-world grasping tasks with the NAO robot. The results demonstrate the effectiveness of our approach in achieving reliable and precise grasping, paving the way for more robust manipulation capabilities for NAO robots.