“The Review of Control and Navigation Using ML and AI techniques”

Control and navigation systems are pivotal in modern technological advancements, driving innovations in autonomous vehicles, robotics, aerospace, and beyond. AI/ML gives adventure in Control and Navigation systems have witnessed transformative changes, enabling unprecedented levels of accuracy, adaptability, and efficiency. This paper shows the integration of ML and AI techniques for control and navigation systems. It examines key methodologies, including supervised learning, reinforcement learning, and neural networks, and their application in path planning, obstacle avoidance, and system optimization. The review highlights the advantages of ML and AI over traditional approaches, emphasizing their capacity for handling complex, dynamic environments and making real-time decisions. It also explores the challenges faced in implementing these technologies, such as data quality, computational costs, and ethical considerations. Furthermore, the paper elaborates the emerging trends and future directions in domains are, including advancements in quantum computing, IoT integration, and the development of adaptive, self-learning systems. By integrating insights from various studies, this review seeks to highlight the current advancements and future prospects of control and navigation systems. It emphasizes the pivotal role of Machine Learning sand Artificial Intelligence in driving the evolution of intelligent systems and shaping their transformative potential.