Autonomous Intelligent Systems and Robotics: A Review of Architectures, Learning Approaches and Applications

Abstract

Autonomous intelligent systems and robotics are becoming one of the most important technological domains of modern era. These systems combine artificial intelligence, sensing, control theory and mechanical design to perform tasks with minimal human intervention. From industrial automation to healthcare and autonomous vehicles, robotics has changed how machines interact with physical world. This paper presents a comprehensive review of autonomous intelligent systems focusing on architecture models, perception and decision-making approaches, learning paradigms, and real-world applications. The role of machine learning, deep learning and reinforcement learning in enabling autonomy is discussed in detail. Furthermore, challenges such as safety, ethical considerations, human-robot interaction and reliability are analyzed. Comparative tables summarizing system components and learning methods are also provided. The review concludes that autonomous robotics will continue expanding in industry, defense, service and daily life environments, but improvements in explainability, robustness and trustworthiness are still required.

KEYWORDS: Autonomous systems, intelligent robotics, machine learning, perception, control systems, human-robot interaction, reinforcement learning