American International Journal of Advanced Research and Development

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Autonomous Robotic Models for Efficient Decision-Making Systems

Author(s) Prof. Lyu Chen
Country Singapore
Abstract Autonomous robotic models are increasingly becoming important for efficient decision-making in industrial, logistics, manufacturing, and intelligent automation environments. Unlike conventional robotic systems that primarily execute predefined commands, autonomous robotic models integrate perception, artificial intelligence, machine learning, planning, navigation, control, and sensor-based reasoning to make decisions according to changing operational conditions. This paper examines autonomous robotic models for efficient decision-making systems through a conceptual and literature-based approach. The study focuses on autonomous mobile robots, intelligent industrial robots, machine learning-based decision systems, robotic perception, path planning, task allocation, multi-robot coordination, and human-robot interaction.
Recent research indicates that autonomous mobile robots can decentralize planning and control decisions and respond dynamically to changing environments, while AI-based robotic systems increasingly integrate perception, planning, control, and decision-making into unified autonomy pipelines. The analysis identifies decision-making efficiency, adaptability, operational flexibility, real-time responsiveness, and autonomous coordination as major contributions of intelligent robotic models. However, implementation remains constrained by data limitations, safety requirements, computational complexity, interoperability, cybersecurity, model reliability, and workforce requirements. The study concludes that effective autonomous robotic decision-making requires integrated architectures combining reliable perception, intelligent reasoning, adaptive planning, robust control, safety monitoring, and human oversight.
Keywords Autonomous Robotics, Robotic Decision-Making, Artificial Intelligence, Machine Learning, Autonomous Mobile Robots, Intelligent Control, Robotic Perception, Task Planning, Industrial Automation
Field Engineering
Published In Volume 4, Issue 3, May-June 2022
Published On 2022-06-02
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