ARTIFICIAL INTELLIGENCE ALGORITHMS FOR AUTONOMOUS ROBOTS
Keywords:
autonomous robots, artificial intelligence, deep reinforcement learning, SLAM, path planning, convolutional neural networks, sim-to-real transfer, robot foundation models, multi-agent systemsAbstract
Autonomous robots represent one of the most consequential intersections of artificial intelligence and mechanical engineering in the twenty-first century. This paper provides a systematic scientific review of the AI algorithms that underpin modern autonomous robotic systems — encompassing machine learning, deep reinforcement learning, probabilistic inference, planning algorithms, and emerging foundation model approaches. Through comparative analysis of thirteen algorithm categories, eight reinforcement learning frameworks, seven SLAM methodologies, and performance data from eight leading deployed robotic systems, the study quantifies the current state-of-the-art and identifies critical capability gaps. Statistical data from the global AI robotics market (2019–2030) demonstrates a compound annual growth rate (CAGR) of approximately 26%, with the total market projected to reach USD 118 billion by 2030. The paper further presents a five-stage AI decision pipeline architecture, a reinforcement learning training loop schema, and adoption-rate analysis across eight AI technique categories. Key challenges including sample inefficiency, sim-to-real transfer, explainability, and adversarial robustness are analyzed alongside current solutions. Future directions — including large robotic foundation models, neuromorphic computing, and embodied AI — are discussed within the context of safe and beneficial autonomous systems.
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