ENHANCING MOBILE ROBOT DELIVERY SYSTEMS USING DEEP LEARNING TECHNIQUES

Abstract

This study explores the optimization of mobile robot delivery systems through the application of deep learning techniques. With the growing demand for efficient and autonomous delivery solutions, mobile robots have become a pivotal component in logistics and service industries. Traditional methods of route planning and task allocation often face limitations in dynamic environments. By leveraging deep learning, this research aims to enhance real-time decision-making, obstacle avoidance, and path optimization. The proposed system integrates advanced neural networks to analyze environmental data, predict optimal routes, and adapt to changing conditions. Experimental results demonstrate significant improvements in delivery efficiency, accuracy, and system robustness. This work highlights the potential of deep learning to revolutionize autonomous delivery systems, paving the way for smarter, more reliable logistics solutions.