CNN-Based Intelligent Control Synthesis for Multi-Robot Coordination and Path Planning

Abstract

The increasing demand for intelligent classification algorithms in numerous areas, such as autonomous robots, medical diagnosis, and industrial control, has stimulated the demand for highly accurate, computationally efficient, and flexible deep learning algorithms. Traditional machine learning algorithms like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest do not support feature extraction and handling high dimensional data and are, therefore, very inefficient in processing complex classification tasks. Similarly, existing Convolutional Neural Network (CNN) architectures, such as AlexNet and ResNet-50, although efficient, are computationally intensive and highly time-consuming to train, thereby ill-fitting real-time applications and lowresource platforms. This work fills this gap by introducing an optimized CNN-based smart classification system with the objectives of enhancing classification accuracy, computational efficiency, and real-time response in dynamic and challenging situations. The primary objective of this study is to architect and deploy a CNN-based classification model that tackles challenges of feature extraction, overfitting, and computational complexity while ensuring effective generalization across datasets. The resulting framework features deep convolutional feature extraction layers, batch normalization, and an adaptive learning rate scheme, which in combination promote improved classification accuracy and training effectiveness. The Fashion-MNIST dataset was chosen for model testing, as it offers a diverse set of grayscale images from ten different fashion classes, which is a good benchmark for deep learning-based classification models. The dataset was thoroughly preprocessed, involving image normalization, resizing, and augmentation methods like rotation, flipping, and brightness changes, to ensure strong model training and better generalization. Evidence from experiments on Fashion-MNIST shows that the introduced CNN performs better than conventional techniques and even better than AlexNet and ResNet-50 (on the same 70-30 train-test split), with accuracy of 98.5%. The model also reduces computational cost by 20%, enhancing efficiency in real-time classification across various applications. Comparative analysis with state-of-the-art models indicates that the proposed approach demonstrates improved performance in accuracy, data robustness, and computational efficiency compared to current models. Furthermore, the integration of dropout layers and adaptive learning rates ensures stable training without overfitting, leading to a more stable classification system. The outcome of this research proves that the proposed CNN-based intelligent classification system is an extremely efficient and computationally cheap solution for real-world classification tasks and therefore is relevant to autonomous robotics, industrial automation, and intelligent surveillance systems. Future work will focus on enhancing model generalization with transfer learning techniques, the use of attention-based models such as Vision Transformers (ViTs), and model optimization for deployment on edge device and low-power hardware. The results confirm that deep learning-based classification models, when optimized properly, can significantly enhance decision-making processes across most artificial intelligence (AI)-based applications.