A Systematic Review of Intelligent Navigation in Smart Warehouses Using Prisma: Integrating AI, SLAM, and Sensor Fusion for Mobile Robots

This systematic review focuses on intelligent navigation as a core enabler of autonomy in smart warehouses, where mobile robots must dynamically perceive, reason, and act in complex, human-shared environments. By synthesizing advancements in AI-driven decision-making, SLAM, and multi-sensor fusion, the study highlights how intelligent navigation architectures reduce operational uncertainty and enhance task efficiency in logistics automation. Smart warehouses, powered by mobile robots and AGVs and integrated with AI and algorithms, are enabling more efficient storage with less human labour. This systematic review followed PRISMA 2020 guidelines to systematically identify, screen, and synthesize evidence from 106 peer-reviewed scientific articles (including pri-mary studies, technical papers, and reviews) published between 2020–2025, sourced from Web of Science. Thematic synthesis was conducted across 8 domains: AI, SLAM, sensor fusion, safety, network, path planning, implementation, and design. The transition to smart warehouses requires modern technologies to automate tasks and optimize resources. This article examines how intelligent systems can be integrated with mathematical models to improve navigation accuracy, reduce costs and prioritize human safety. Real-time data management with precise information for AMRs and AGVs is crucial for low-risk operation. This article studies AI, the IoT, LiDAR, machine learning (ML), SLAM and other new technologies for the successful implementation of mobile robots in smart warehouses. Modern technologies such as reinforcement learning optimize the routes and tasks of mobile robots. Data and sensor fusion methods integrate information from various sources to provide a more precise understanding of the indoor environment and inventory. Semantic mapping enables mobile robots to navigate and interact with complex warehouse environments with high accuracy in real time. The article also analyses how virtual reality (VR) can improve the spatial orientation of mobile robots by developing sophisticated navigation solutions that reduce time and financial costs.

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Publication Details

Journal
Eng—Advances in Engineering
Published
2025-12-01
DOI
https://doi.org/10.3390/eng6120339
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
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article

A Systematic Review of Intelligent Navigation in Smart Warehouses Using Prisma: Integrating AI, SLAM, and Sensor Fusion for Mobile Robots

Željko Barač, Hrvoje Glavaš, Domagoj Zimmer, Mladen Jurišić et al.
Eng—Advances in Engineering
Advanced Manufacturing and Logistics Optimization
article

A Systematic Review of Intelligent Navigation in Smart Warehouses Using Prisma: Integrating AI, SLAM, and Sensor Fusion for Mobile Robots

Željko Barač, Hrvoje Glavaš, Domagoj Zimmer, Mladen Jurišić, Ivan Plašćak, Dorijan Radočaj, Robert Benković
article en

Abstract

This systematic review focuses on intelligent navigation as a core enabler of autonomy in smart warehouses, where mobile robots must dynamically perceive, reason, and act in complex, human-shared environments. By synthesizing advancements in AI-driven decision-making, SLAM, and multi-sensor fusion, the study highlights how intelligent navigation architectures reduce operational uncertainty and enhance task efficiency in logistics automation. Smart warehouses, powered by mobile robots and AGVs and integrated with AI and algorithms, are enabling more efficient storage with less human labour. This systematic review followed PRISMA 2020 guidelines to systematically identify, screen, and synthesize evidence from 106 peer-reviewed scientific articles (including pri-mary studies, technical papers, and reviews) published between 2020–2025, sourced from Web of Science. Thematic synthesis was conducted across 8 domains: AI, SLAM, sensor fusion, safety, network, path planning, implementation, and design. The transition to smart warehouses requires modern technologies to automate tasks and optimize resources. This article examines how intelligent systems can be integrated with mathematical models to improve navigation accuracy, reduce costs and prioritize human safety. Real-time data management with precise information for AMRs and AGVs is crucial for low-risk operation. This article studies AI, the IoT, LiDAR, machine learning (ML), SLAM and other new technologies for the successful implementation of mobile robots in smart warehouses. Modern technologies such as reinforcement learning optimize the routes and tasks of mobile robots. Data and sensor fusion methods integrate information from various sources to provide a more precise understanding of the indoor environment and inventory. Semantic mapping enables mobile robots to navigate and interact with complex warehouse environments with high accuracy in real time. The article also analyses how virtual reality (VR) can improve the spatial orientation of mobile robots by developing sophisticated navigation solutions that reduce time and financial costs.

Eng—Advances in EngineeringVol. 6(12)
University of Slavonski Brod (HR), University of Osijek (HR)
Openalex Percentile: Top 50%
Advanced Manufacturing and Logistics Optimization
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