The Integration of Neural Networks in Smart Robotics and Autonomous Systems

Abstract

This chapter explores the integration of neural networks in smart robotics and autonomous systems, emphasizing their transformative impact on various industries. It begins by discussing the fundamentals of neural networks, including types such as feedforward, convolutional, and recurrent networks, and their applications in robotics. The chapter highlights how these networks enable robots to learn, adapt, and make autonomous decisions, particularly in areas such as autonomous vehicles, medical robots, industrial automation, and service robots. The role of neural networks in enhancing robotic perception through sensors, actuators, and control systems is also explored. Additionally, the chapter covers the challenges, opportunities, and ethical implications of incorporating neural networks into autonomous systems, such as privacy concerns and impacts on employment. With practical case studies, the chapter demonstrates how neural networks are revolutionizing smart robotics, fostering greater efficiency, precision, and autonomy in diverse applications.