SCIENTIFIC ARTICLE INTELLIGENT DECISION-MAKING SYSTEMS IN ROBOTICS
Keywords:
Artificial Intelligence, Robotics, Decision-Making, Machine Learning, Reinforcement Learning, Autonomous SystemsAbstract
This paper presents a comprehensive analysis of intelligent decision-making systems in modern robotics. As autonomous robots increasingly operate in complex, dynamic, and unpredictable environments, the integration of artificial intelligence (AI) into robotic decision-making has become a critical research priority. This study examines the architectural components of AI-based decision systems, evaluates machine learning and reinforcement learning methodologies applied to robotic autonomy, and analyses real-world deployment statistics across key industrial sectors. Comparative performance metrics, market growth data, and application case studies are presented to illustrate the current state and future potential of intelligent robotic systems. The article further identifies principal challenges — including real-time processing constraints, safety assurance, and ethical considerations — and proposes research directions to address them. Findings indicate that AI-powered decision-making systems significantly outperform traditional rule-based approaches in adaptability, accuracy, and efficiency, with global adoption accelerating at a compound annual growth rate (CAGR) of 22.7% [15].References
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