Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) are among the key enabling technologies driving intelligent logistics and industrial automation. Despite their widespread adoption and rapid technological evolution, the literature often addresses AGV and AMR systems in a fragmented manner, lacking a structured methodological perspective that highlights their architectural foundations, levels of autonomy, and technological maturity. This paper presents a methodological survey of AGV and AMR technologies, focusing on system-level architectures and core functional components rather than isolated algorithms. The survey systematically analyzes key technological dimensions, including sensing and perception, localization and positioning strategies, navigation and path-planning approaches, communication infrastructures, and multi-robot coordination mechanisms. A clear distinction is drawn between classical AGV systems, which rely on fixed infrastructure and predefined routes, and AMR systems, which exhibit adaptive, perception-driven, and self-configuring behaviors enabled by artificial intelligence techniques. Rather than proposing new algorithms, this paper organizes existing approaches into a coherent framework that highlights technological transitions from infrastructure-dependent guidance to autonomous, data-driven navigation. Recent trends such as cloud–edge integration, learning-based navigation, scalable fleet management architectures, and cooperative multi-robot systems are reviewed and discussed from a methodological standpoint, emphasizing their role in increasing flexibility, robustness, and operational efficiency in industrial and logistics environments. The survey also addresses cross-cutting challenges, including system transparency, safety and certification, interoperability, and sustainability. Finally, this paper outlines research directions aligned with the principles of Industry 5.0, highlighting the need for human-centered, resilient, and scalable AMR and AGV systems capable of safe and explainable operation in complex industrial contexts.
Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) are central technologies for intelligent logistics, smart manufacturing, warehousing, healthcare logistics, and intralogistics automation [
1,
2]. Their industrial role is no longer limited to transporting loads between fixed points. Modern mobile-robot systems influence material-flow flexibility, line feeding, warehouse throughput, inventory traceability, production scheduling, human–robot interaction, and the ability of a facility to reconfigure itself when product mix, demand, or layout changes [
3]. In the Industry 4.0 context, AGVs and AMRs support cyber–physical production systems, digital monitoring, industrial data exchange, and connected automation [
4,
5,
6]. Here, Industry 5.0 is used as an evaluation lens: AGV/AMR assessment extends Industry 4.0 goals to human-centered operation, resilience, sustainability, and trustworthy autonomy [
7,
8,
9].
An automated guided vehicle/autonomous mobile robot (AGV/AMR) deployment is a system-of-systems [
2,
3]. It combines physical platforms, payload interfaces, sensors, actuators, localization, mapping, path planning, obstacle avoidance, controllers, communication networks, fleet scheduling, traffic management, charging infrastructure, enterprise integration, safety verification, and operating-zone design [
1]. This means that a robot cannot be evaluated only by its navigation algorithm or sensor package. Its industrial value depends on how the robot fleet receives tasks from a Warehouse Management System (WMS), synchronizes with a Manufacturing Execution System (MES), exchanges data with Enterprise Resource Planning (ERP) tools, interacts with Programmable Logic Controllers (PLCs), respects safety zones, manages charging, and maintains traceable event logs [
10,
11,
12,
13].
The literature contains strong contributions on individual technology families, including obstacle avoidance and path planning [
14], localization and Simultaneous Localization and Mapping (SLAM) [
15,
16], multi-robot task allocation [
17], and reinforcement learning for multi-robot systems [
18].
Existing review studies provide important but differently scoped perspectives on AGV/AMR development. Oyekanlu et al. [
2] review advances in AGV and AMR technologies with a strong emphasis on industrial communication, integration challenges, fleet connectivity, and research needs for 5G-based smart manufacturing. Lackner et al. [
3] focus specifically on AMRs in intralogistics and organize the remaining limitations and research gaps around environmental complexity, safety, resource scheduling and power consumption, artificial intelligence, and interoperability. The present review complements these studies by adopting an explicit system-of-systems and deployment-oriented perspective. It connects enterprise logistics and WMS/MES/ERP integration with fleet, traffic, and charging coordination; robot hardware and autonomy functions; communication and middleware; energy management; safety; and certification. The literature is then compared using common deployment criteria, including system function, infrastructure dependence, autonomy contribution, industrial maturity/readiness, integration and verification requirements, limitations, and industrial relevance.
Table 1 summarizes these distinctions.
Table 1. Positioning of the present review relative to representative AGV/AMR review studies.
Accordingly, the novelty of this review lies not in repeating individual AGV/AMR technologies, but in relating them through one industrial deployment architecture and evaluating how robot-level technologies interact with fleet coordination, enterprise integration, infrastructure, safety, energy, and verification requirements.
This paper addresses that need through a system-driven survey. The technologies are organized according to the industrial functions they support: logistics demand generation, fleet coordination, traffic and charging management, robot platform execution, sensing and perception, localization and mapping, navigation and planning, control, communication, safety, energy management, and certification. This organization keeps each technology attached to its system role, input data, output data, integration interface, and deployment limitation.
The main contributions of this survey are summarized as follows:
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A top-down AGV/AMR system architecture that connects enterprise logistics, fleet management, robot platforms, autonomy functions, communication, safety, and certification;
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A comparative analysis of AGV and AMR paradigms based on infrastructure dependency, autonomy level, flexibility, validation profile, and industrial risk;
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A technology-oriented synthesis of sensing, localization, mapping, planning, control, middleware, communication, and safety methods used in industrial mobile robots;
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Compact comparison tables with source citations that summarize technology families using consistent criteria including function, advantages, limitations, industrial maturity/readiness, deployment and integration requirements, and industrial relevance;
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Identification of research gaps and future directions for robust human-shared navigation, certifiable artificial intelligence (AI), multi-brand interoperability, cloud–edge resilience, cybersecurity, and sustainable fleet management.
This review evaluates industrial AGV/AMR systems through four deployment-critical issues:
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System architecture and AGV/AMR differentiation: How should industrial AGV and AMR systems be differentiated and organized within a complete system-of-systems architecture, including infrastructure dependence, autonomy, and standards?
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Technology suitability, readiness, and verification: Which sensing, localization, planning, control, and communication approaches are most suitable under different industrial operating conditions, and what are their maturity/readiness and verification limitations?
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Fleet integration and deployment constraints: How do fleet coordination, enterprise integration, charging, safety, and application context constrain deployment, interoperability, and industrial performance?
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Cross-layer gaps and testable future directions: Which cross-layer gaps most limit scalable, safe, resilient, sustainable, and trustworthy AGV/AMR deployment, and which research directions provide testable progress?
These four deployment-critical issues structure the remainder of this review.
Section 2 defines the evidence-selection and synthesis procedure supporting all four objectives.
Section 3 and
Section 4 address
system architecture and AGV/AMR differentiation through the system architecture, autonomy framework, standards, and technological evolution.
Section 5 addresses
technology suitability, readiness, and verification through deployment-oriented comparison of robot-level technologies.
Section 6 addresses
fleet integration and deployment constraints through communication, enterprise integration, and fleet coordination, while
Section 7 tests these technology and integration implications across application domains.
Section 8 and
Section 9 address
cross-layer gaps and testable future directions by converting limitations into operational research targets and evaluation criteria.
Section 11 synthesizes the findings.
