로봇 사물인터넷(IoRT) 시장 : 컴포넌트, 용도, 최종사용자별 - 세계 예측(2025-2032년)

The Internet of Robotic Things Market is projected to grow by USD 324.61 billion at a CAGR of 31.40% by 2032.

A strategic primer explaining why convergent sensing, networking, and intelligent control are transforming robotic systems into coordinated, data-driven operational assets

The Internet of Robotic Things is reshaping operational models across industries by combining physical robotics with pervasive connectivity and data-driven decisioning. This introduction synthesizes the foundational concepts and frames why today's convergence of sensing, computation, and network capability elevates robotic systems from isolated automation tools to intelligent, collaborative entities within distributed operational environments.

Emerging robotic deployments now extend beyond traditional factory floors into complex, dynamic settings where robots must perceive, reason, and act in concert with humans and other systems. As a result, commercial leaders must reconcile legacy automation practices with new imperatives: integrating real-time analytics, securing edge-to-cloud data flows, and enabling resilient interoperability across multi-vendor ecosystems. Moreover, technological advances in edge computing, machine learning, and low-latency wireless communications make previously theoretical capabilities practically achievable.

Consequently, executive attention should focus on structural questions: how to align product roadmaps with evolving regulatory expectations, how to balance capital investment with as-a-service models, and how to cultivate talent capable of operating hybrid human-robot workstreams. In short, understanding the Internet of Robotic Things requires both a systems perspective and a pragmatic roadmap for staged adoption that balances innovation with operational continuity.

How recent advances in perception, edge intelligence, and connectivity are shifting robotic systems from isolated machines to collaborative, adaptive operational platforms

The landscape for robotic systems is undergoing transformative shifts driven by advances in algorithmic intelligence, connectivity, and modular hardware design. These shifts are enabling robots to function as adaptive agents rather than deterministic machines, expanding their utility across environments that were once considered too uncertain or unstructured for automation.

First, improvements in perception and learning allow robots to interpret complex sensory inputs and adapt behavior on the fly, which reduces the need for rigid programming and increases resilience to environmental variance. Second, pervasive low-latency communications and edge processing enable distributed decisioning, allowing robotic agents to collaborate in real time and share learned models without centralized bottlenecks. Together, these capabilities accelerate deployment in domains such as logistics, healthcare, and manufacturing where responsiveness and safety are paramount.

At the same time, commercial and operational models are shifting from product-centric to services-oriented approaches, emphasizing orchestration platforms, subscription-based maintenance, and outcome-based contracts. Regulatory attention and public expectations around safety, privacy, and workforce impact are also rising, creating both constraints and incentives for responsible design. As a result, stakeholders that can combine technical excellence with robust governance and scalable business models will lead the next wave of adoption.

How recent trade policy shifts have catalyzed supply chain re-engineering, modular design thinking, and nearshoring strategies across robotic hardware and systems

Recent tariff actions and trade policy shifts have introduced a layer of complexity into global supply chains that underpin robotic systems, affecting component sourcing, manufacturing strategies, and procurement planning. Tariffs imposed on inputs and finished goods influence decisions at multiple points in the value chain, from where suppliers locate production to how companies price and structure service agreements.

One immediate effect is pressure on component sourcing for high-value items such as sensors, actuators, and control electronics, which often cross multiple borders during assembly. In response, some manufacturers are evaluating nearshoring and dual-sourcing strategies to reduce exposure to tariff volatility and lead-time risk. This reconfiguration can increase unit costs in the short term, yet it also creates opportunities to build more resilient supplier networks that support faster iteration and localized customization.

Furthermore, trade measures accelerate conversations around design for supply chain flexibility. Product architects are prioritizing modularity and standardized interfaces so components can be substituted more easily when cost or availability shifts. Meanwhile, companies that provide integration, maintenance, and software-driven optimization are positioned to offset hardware cost increases by delivering demonstrable operational value. In sum, policy-driven trade changes are catalyzing deeper supply chain re-engineering and strategic investment in adaptability.

A practical segmentation lens that links components, applications, and end-user needs to reveal where integration, services, and software create the most strategic value

A clear segmentation framework helps clarify where value is created and who captures it within Internet of Robotic Things deployments. Component segmentation differentiates hardware, services, and software, where hardware spans actuators, control systems, power sources, and sensors; services encompass consulting, deployment and integration, and ongoing support and maintenance; and software includes data management, real-time streaming analytics, and security solutions. This component view underscores that durable competitive advantage will be derived from seamless integration across mechanical, electrical, and software layers as well as from lifecycle services that sustain performance.

Application segmentation highlights how cognitive robots, collaborative robots, and mobile robots address distinct operational problems. Cognitive robots bring advanced perception and decisioning to complex tasks, collaborative robots focus on safe human-robot interaction in shared workspaces, and mobile robots deliver autonomy in navigation and logistics. Each application class demands specific integration approaches, from safety-certified control architectures for collaboration to high-bandwidth sensor fusion and localization for mobile autonomy.

End-user segmentation clarifies demand drivers across aerospace and defense, consumer electronics, healthcare, logistics and supply chain, manufacturing, and retail. Aerospace and defense emphasize reliability and certification, consumer electronics demand scalability and cost control, healthcare prioritizes safety and regulatory compliance, logistics stresses throughput and route optimization, manufacturing values precision and uptime, and retail seeks flexibility and customer-facing experiences. Understanding these segment intersections is essential for tailoring product architectures, commercialization strategies, and service offerings that address the varied performance, compliance, and total-cost imperatives of each sector.

Regional adoption patterns and ecosystem dynamics that determine where robotics integration, regulatory alignment, and local supplier networks accelerate commercial scaling

Regional dynamics shape adoption rhythms, investment priorities, and competitive strategies for robotic systems. In the Americas, market actors are driven by scale-driven automation in logistics, advanced manufacturing clusters, and strong venture and corporate investment ecosystems that favor rapid commercialization and service innovation. Regulatory frameworks in certain jurisdictions encourage testing and pilot deployments, while demand concentrates on end-to-end automation solutions that reduce labor intensity and accelerate throughput.

In Europe, the Middle East, and Africa, emphasis often falls on safety standards, industrial partnerships, and sustainability mandates that influence procurement cycles and technical specifications. European markets exhibit a preference for interoperable platforms and robust data governance, while certain Middle Eastern and African initiatives prioritize infrastructure modernization and workforce upskilling to support broader digital transformation objectives.

In Asia-Pacific, high-density logistics, consumer electronics manufacturing, and aggressive industrial automation policies drive accelerated adoption of both mobile and collaborative robotic systems. There is notable investment in domestic component ecosystems and rapid iteration cycles that favor adaptable designs and strong local supplier networks. Across regions, interoperability, regulatory alignment, and talent availability remain critical determinants of how quickly organizations can move from pilot to scale.

Competitive motions and strategic priorities among solution providers that prioritize integration, services monetization, and ecosystem partnerships to drive adoption

Companies active in this space are pursuing differentiated strategies to capture value across hardware, software, and services layers. Some firms focus on vertically integrated platforms that tightly couple proprietary hardware with specialized control and analytics software in order to deliver turnkey solutions for specific use cases. Others adopt an open-architecture stance, emphasizing interoperability, developer ecosystems, and cloud-native orchestration to scale across heterogeneous environments.

Strategic moves include targeted partnerships with systems integrators, investments in domain-specific AI capabilities, and expansion of field services to guarantee uptime and continuous improvement. Additionally, firms are increasingly monetizing data-driven services, offering predictive maintenance, fleet optimization, and usage-based pricing that tie commercial outcomes to operational performance. Mergers and acquisitions remain a method to accelerate capability buildup, especially for acquiring niche sensor technologies, specialized control IP, or proven service delivery teams.

The competitive frontier therefore rests on three capabilities: the ability to integrate across multidisciplinary stacks, the capacity to deliver measurable operational outcomes through services and software, and the agility to adapt to shifting trade and regulatory conditions. Organizations that align product development, commercial models, and partner ecosystems around these levers will be well positioned to lead.

Actionable strategic imperatives for leaders to build resilient supply chains, monetize services, embed security, and upskill workforces for scalable robotic deployments

Industry leaders should adopt a dual-track approach that balances near-term operational resilience with longer-term capability building. Begin by hardening supply chains through multi-sourcing, modular design, and inventory strategies that reduce exposure to trade policy volatility and component shortages. Simultaneously, invest in platform interoperability and standardized interfaces so that hardware substitutions and software overlays can be executed with minimal disruption.

Next, shift commercial models toward outcome-oriented services to capture recurring value and to provide customers with predictable performance guarantees. This requires strengthening field operations, developing robust telemetry and analytics capabilities, and packaging services that clearly demonstrate return on operational metrics. In parallel, prioritize cybersecurity and data governance, embedding security-by-design into both hardware and software lifecycles and advancing privacy-preserving approaches for shared environments.

Finally, accelerate workforce transformation by creating cross-disciplinary teams that combine robotics engineering, data science, and systems integration expertise. Foster partnerships with academic and training institutions to build a pipeline of talent, and engage regulators proactively to shape standards that enable safe scale. By combining these moves, leaders can convert disruption into durable advantage and unlock broader enterprise transformation.

A transparent mixed-method research approach that combines expert interviews, technical analysis, and supply chain mapping to produce evidence-based strategic guidance

The research approach combines structured qualitative inquiry with rigorous data triangulation to ensure findings are robust and actionable. Primary research included interviews with system architects, procurement leads, and integration specialists who have practical experience deploying robotic systems across industrial and service contexts. These conversations provided granular insight into integration challenges, service delivery models, and procurement priorities.

Secondary analysis synthesized open technical literature, standards documentation, and patent filings to trace capability trajectories and identify emerging architectural patterns. Supply chain mapping and component-level analysis informed assessments of sourcing vulnerabilities and modularity trends. In addition, case studies of deployments across sectors-manufacturing, logistics, healthcare, and aerospace-were used to surface operational lessons and to validate cross-cutting recommendations.

Throughout the study, methodological rigor was maintained by cross-validating qualitative insights with multiple independent sources and by stress-testing scenario assumptions against observable deployment outcomes. This mixed-method approach yields a practical evidence base that supports strategic decision-making while acknowledging the limitations and uncertainties inherent in rapidly evolving technological domains.

A synthesized conclusion that identifies integration, governance, and service-driven strategies as the essential foundations for scalable robotic system adoption

Bringing together the technical, commercial, and policy threads reveals a consistent conclusion: the Internet of Robotic Things represents a transition from discrete automation to interconnected, outcome-oriented systems. Organizations that treat robotic deployments as integrated socio-technical systems-attending to hardware modularity, software orchestration, service economics, workforce adaptation, and governance-will capture disproportionate value as adoption scales.

Trade and regulatory dynamics add urgency to strategic decision-making, encouraging diversification of supplier networks and design choices that enable rapid substitution and localization. Meanwhile, investments in analytics, edge intelligence, and secure data architectures will determine which players can deliver continuous performance improvement and monetize ongoing services. In the face of these dynamics, pragmatic pilots that prioritize measurable operational metrics and iterative scaling pathways offer the most reliable route to sustainable adoption.

In closing, the path forward demands a balanced portfolio of defensive moves to protect operational continuity and offensive investments to expand capability. Executives who align organizational structures, partner ecosystems, and investment priorities around integration and service delivery will be best positioned to lead in this emerging landscape.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Rapid emergence of AI-powered robotic systems leveraging 5G connectivity for low-latency operations
• 5.2. Deployment of autonomous mobile robots with advanced vision systems in warehouse logistics
• 5.3. Implementation of predictive maintenance platforms integrating IoT sensors and robotic diagnostics
• 5.4. Development of secure edge computing frameworks for real-time robotic swarm coordination in industrial settings
• 5.5. Expansion of remote robotic teleoperation solutions using augmented reality interfaces for hazardous environments
• 5.6. Integration of cloud-based digital twins to optimize multi-robot workflows in smart manufacturing facilities
• 5.7. Growing use of machine learning algorithms for autonomous UAV inspection and maintenance in infrastructure

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. Internet of Robotic Things Market, by Component

• 8.1. Hardware
  • 8.1.1. Actuators
  • 8.1.2. Control systems
  • 8.1.3. Power source
  • 8.1.4. Sensors
• 8.2. Service
  • 8.2.1. Consulting Services
  • 8.2.2. Deployment & Integration
  • 8.2.3. Support & Maintenance
• 8.3. Software
  • 8.3.1. Data Management
  • 8.3.2. Real-Time Streaming Analytics
  • 8.3.3. Security Solution

9. Internet of Robotic Things Market, by Application

• 9.1. Cognitive Robots
• 9.2. Collaborative Robots
• 9.3. Mobile Robots

10. Internet of Robotic Things Market, by End-User

• 10.1. Aerospace & Defense
• 10.2. Consumer Electronics
• 10.3. Healthcare
• 10.4. Logistics & Supply Chain
• 10.5. Manufacturing
• 10.6. Retail

11. Internet of Robotic Things Market, by Region

• 11.1. Americas
  • 11.1.1. North America
  • 11.1.2. Latin America
• 11.2. Europe, Middle East & Africa
  • 11.2.1. Europe
  • 11.2.2. Middle East
  • 11.2.3. Africa
• 11.3. Asia-Pacific

12. Internet of Robotic Things Market, by Group

• 12.1. ASEAN
• 12.2. GCC
• 12.3. European Union
• 12.4. BRICS
• 12.5. G7
• 12.6. NATO

13. Internet of Robotic Things Market, by Country

• 13.1. United States
• 13.2. Canada
• 13.3. Mexico
• 13.4. Brazil
• 13.5. United Kingdom
• 13.6. Germany
• 13.7. France
• 13.8. Russia
• 13.9. Italy
• 13.10. Spain
• 13.11. China
• 13.12. India
• 13.13. Japan
• 13.14. Australia
• 13.15. South Korea

14. Competitive Landscape

• 14.1. Market Share Analysis, 2024
• 14.2. FPNV Positioning Matrix, 2024
• 14.3. Competitive Analysis
  • 14.3.1. ABB Ltd.
  • 14.3.2. Cisco Systems, Inc.
  • 14.3.3. Denso Corporation
  • 14.3.4. FANUC Corporation
  • 14.3.5. Geckosystems International Corporation
  • 14.3.6. Google LLC by Alphabet Inc.
  • 14.3.7. Honda Motor Co., Ltd.
  • 14.3.8. Honeywell International Inc
  • 14.3.9. Hyundai Motor Company
  • 14.3.10. Intel Corporation
  • 14.3.11. International Business Machines Corporation
  • 14.3.12. iRobot Corporation
  • 14.3.13. Kuka AG
  • 14.3.14. Locus Robotics Corporation
  • 14.3.15. Microsoft Corporation
  • 14.3.16. Nvidia Corporation
  • 14.3.17. Omron Corporation
  • 14.3.18. Panasonic Holdings Corporation
  • 14.3.19. Robert Bosch GmbH
  • 14.3.20. Rockwell Automation, Inc.
  • 14.3.21. Samsung Electronics Co Ltd.
  • 14.3.22. Seiko Epson Corporation
  • 14.3.23. Siemens AG
  • 14.3.24. ST Engineering
  • 14.3.25. Tesla, Inc.
  • 14.3.26. Universal Robots A/S
  • 14.3.27. Yaskawa Electric Corporation