ARTIFICIAL INTELLIGENCE-BASED CONTROL OF ROBOTIC MANIPULATORS: ADVANCES, APPLICATIONS, AND FUTURE PERSPECTIVES
Keywords:
robotic manipulators, artificial intelligence, deep learning, reinforcement learning, industrial automation, neural networks, computer vision, trajectory planningAbstract
The integration of artificial intelligence (AI) into robotic manipulator control systems has fundamentally transformed industrial automation, healthcare robotics, and autonomous systems research. This paper presents a comprehensive review of AI-based methodologies applied to robotic manipulator control, encompassing deep learning, reinforcement learning, fuzzy logic systems, genetic algorithms, and transformer-based architectures. Drawing on data from over 35 peer-reviewed publications, industry reports, and benchmark studies, we analyze the performance metrics, application domains, and limitations of current approaches. Statistical analysis reveals that AI-enhanced manipulators achieve task success rates of 91–98%, compared to 72–83% for conventional control methods. The global AI robotics market has grown from $3.2 billion in 2018 to an estimated $16.3 billion in 2024, with projections exceeding $41.5 billion by 2030. Key challenges including real-time processing constraints, sim-to-real gaps, and interpretability are examined alongside emerging solutions. This review provides educational insights into the technological foundations relevant to modern pedagogical curricula in engineering and computer science.
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