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This review paper presents a comprehensive analysis of AI-powered autonomous warehouse robots, examining the current state-of-the-art technologies, methodologies, and future perspectives in warehouse automation. The integration of Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, and Real-Time Sensors in autonomous mobile robots (AMRs) has revolutionized warehouse operations by automating labor-intensive tasks such as picking, sorting, loading, and inventory handling. This study synthesizes findings from recent literature spanning 2020-2025, analyzing the evolution of warehouse robotics from basic automated guided vehicles (AGVs) to sophisticated AI-driven autonomous systems. The research identifies key technological components including multi-sensor fusion, SLAM-based navigation, intelligent decision-making modules, and human-robot collaboration frameworks. Through systematic analysis of 20+ research papers, this review reveals significant operational improvements including 15-45% reduction in cycle times, 20-35% gains in space utilization, and order accuracy exceeding 98%. The paper also addresses critical challenges such as high initial investment costs, complex system integration, dynamic environment handling, sensor limitations, and computational requirements. Future research directions emphasize the need for explainable AI, predictive maintenance strategies, and seamless integration with warehouse management systems (WMS) to support Industry 4.0 initiatives.
Keywords:
Autonomous Mobile Robots, Warehouse Automation, Artificial Intelligence, Computer Vision, SLAM Navigation, Industry 4.0, Robotics, Supply Chain Management
Cite Article:
"AI Powered Autonomous Warehouse Robot", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a975-a987, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604130.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator