The rapid advancement of intelligent systems and devices has been largely driven by the integration of cutting-edge physics principles, enabling unprecedented capabilities in fields such as robotics, artificial intelligence (AI), and autonomous systems. This chapter explores the fundamental physics concepts that underpin these innovations, focusing on key areas such as electromagnetism, thermodynamics, quantum mechanics, and optics. The application of electroactive materials, quantum entanglement, and photonic integration is discussed in depth, highlighting their transformative impact on real-time decision-making and system efficiency. The chapter further examines the role of energy management, with an emphasis on power consumption optimization in autonomous vehicles and robotics, and explores how energy harvesting technologies contribute to the sustainability of intelligent devices. The integration of smart materials and advanced control systems, such as quantum sensors and electroactive polymers, is explored to demonstrate their potential for enhancing the adaptability, efficiency, and functionality of intelligent systems. The continuous evolution of these technologies holds the promise of enabling more responsive, autonomous, and energy-efficient devices, paving the way for the next generation of intelligent applications in healthcare, transportation, and beyond. This chapter provides a comprehensive framework for understanding the critical role of applied physics in shaping the future of intelligent systems and their applications.
The development of intelligent systems has witnessed a paradigm shift with the increasing integration of applied physics principles into their design and operation [1]. The fusion of classical physics, quantum mechanics, and materials science has given rise to innovations that redefine the capabilities of modern devices [2], from robots to AI-powered autonomous systems. Physics not only provides a theoretical foundation for these devices but also shapes their functionality [3], efficiency, and adaptability [4]. Core principles such as electromagnetism, thermodynamics, optics, and quantum mechanics govern how intelligent systems process information, make decisions, and interact with their environment. These principles are essential for overcoming the technical challenges faced by engineers in fields like robotics, healthcare, energy management, and artificial intelligence [5].
In particular, the role of quantum mechanics has become increasingly prominent in the evolution of intelligent systems [6]. The advent of quantum computing and quantum sensors has opened new possibilities for enhancing the speed and accuracy of decision-making processes in AI models and autonomous systems [7]. Quantum entanglement, for example, has the potential to drastically increase computational efficiency by enabling systems to process vast amounts of data in parallel, a capability far beyond the reach of traditional classical systems [8]. These breakthroughs promise to accelerate real-time decision-making in complex environments such as healthcare diagnostics, autonomous driving, and large-scale industrial automation [9]. The application of quantum mechanics, in conjunction with classical principles, will undoubtedly pave the way for more powerful and intelligent systems [10].
Photonic integration is another key area where applied physics has had a transformative impact on intelligent devices [11]. Photonics, which involves the manipulation and transmission of light, enables devices to achieve higher processing speeds, greater bandwidth, and improved energy efficiency compared to traditional electronic systems [12]. In applications like autonomous vehicles, robotics, and healthcare monitoring, photonic-based systems are used to process large amounts of data quickly and accurately [13]. Light-based sensors, such as LiDAR and optical cameras, are increasingly used in autonomous systems to gather real-time environmental data, providing precise information that is crucial for safe navigation and interaction with the surroundings [14]. As these technologies continue to evolve, photonic integration will play an even more central role in enabling the next generation of intelligent systems [15].
