한글목차

산업용 로봇 시장은 케이지에 갇힌 단순한 기계에서 기존 산업용 로봇, 협동 로봇(코봇), 휴머노이드 로봇, 지능형 모바일 시스템을 포함한 정교한 에코시스템으로 진화하고 있습니다. 이러한 확장된 환경은 다양한 산업 분야에서 인간 작업자와 첨단 로봇 기술을 원활하게 통합하는 유연하고 적응력 있는 자동화로 제조가 변화하고 있음을 반영합니다. 오늘날의 산업용 로봇 시장은 다양한 카테고리에 걸쳐 있으며, 각 카테고리는 특정 제조 요구 사항을 충족합니다. 기존 산업용 로봇은 자동차 용접이나 전자제품 조립과 같은 다수 및 고정밀 용도를 계속 독점하고 있습니다. 그러나 협동 로봇은 보호벽 없이 인간 작업자와 함께 안전하게 작업할 수 있도록 설계된 게임 체인저로 부상하고 있습니다. 이러한 코봇은 첨단 힘 제한 기술, 속도 모니터링 시스템, 신속한 배치 및 재구성을 가능하게 하는 직관적인 프로그래밍 인터페이스를 특징으로 합니다.

Boston Dynamics, Figure AI, Agility Robotics와 같은 기업들은 표준 산업 환경을 이동하고, 다양한 물체를 조작하고, 다단계 조립 공정을 수행할 수 있는 2족 보행 휴머노이드 시스템을 개발하고 있습니다. 다단계 조립 공정을 수행할 수 있는 이족보행 휴머노이드 시스템을 개발하고 있습니다. 이러한 시스템은 기존의 고정식 로봇이 수행하기에는 너무 복잡한 작업을 수행하면서 노동력 부족에 대처할 수 있을 것으로 기대되고 있습니다.

자율주행 운반 로봇(AMR)과 모바일 매니퓰레이터는 이동성과 조작 능력을 결합하여 변화하는 생산 레이아웃에 적응할 수 있는 유연한 자동화 솔루션을 제공합니다. 이 시스템은 첨단 SLAM(Simultaneous Localization and Mapping) 기술, LiDAR 센서, AI 기반 네비게이션을 활용하여 인간 작업자와 함께 역동적인 환경에서 안전하게 작업할 수 있습니다.

기술 융합과 AI의 통합: AI의 통합은 산업용 로봇의 능력을 근본적으로 변화시켰습니다. 최신 시스템에는 실시간 품질 검사, 물체 인식, 적응형 조립에 사용되는 컴퓨터 비전이 내장되어 있습니다. 머신러닝 알고리즘은 로봇이 지속적으로 성능을 최적화하고, 생산 변동으로부터 학습하고, 시간이 지남에 따라 정확도를 향상시킬 수 있도록 합니다. 멀티모달 AI 시스템은 시각, 힘, 음성 처리를 결합하여 고급 의사결정을 할 수 있는 로봇을 구현합니다.

엣지 컴퓨팅은 로봇이 센서 데이터를 로컬에서 분석하여 상황 변화에 즉각적으로 대응할 수 있도록 하는 실시간 처리에 필수적인 요소입니다. 이 기능은 사람의 존재나 예기치 못한 장애물에 즉각적으로 대응해야 하는 안전이 요구되는 협업 애플리케이션에서 특히 중요합니다. 첨단 센서 융합은 카메라, LiDAR, 힘 센서, 근접 센서의 데이터를 결합하여 종합적인 환경 인식을 제공합니다.

자동차 산업은 여전히 산업용 로봇을 가장 많이 채택하고 있으며, 최종 조립 작업에는 코봇, 복잡한 배선 및 내부 부품 장착에는 휴머노이드 로봇을 사용하고 있습니다. 전자기기 제조에서는 섬세한 부품 취급 및 테스트 공정에 협동 로봇이 사용되고 있으며, 인간과 같은 손재주가 필요한 스마트폰 및 태블릿 조립에 휴머노이드 시스템이 유망한 것으로 평가받고 있습니다. 식음료 가공에서는 포장, 품질 검사, 자재관리에 첨단 로봇의 활용이 진행되고 있습니다. 협동 로봇은 유연성과 손쉬운 청소가 필수적인 식품 조리 및 포장에 탁월한 성능을 발휘합니다. 의약품 제조에서는 무균 취급, 정밀한 분주, 의료기기의 복잡한 조립에 이러한 기술이 사용되고 있습니다.

인력 부족은 시장 성장을 가속하는 요인으로 작용하고 있으며, 특히 로봇은 항공우주, 조선 등의 산업에서 숙련된 인력 부족을 해소하는 데 적합합니다. 선진국에서는 노동력의 고령화가 진행되면서 숙련된 작업자가 감독과 품질 관리에 전념하는 반면, 로봇이 육체적으로 힘든 작업을 수행할 수 있는 기회가 생기고 있습니다.

세계의 산업용 로봇 시장에 대해 조사 분석했으며, 각 부문별 시장 규모와 성장 예측, 기술 상황과 기술 혁신 동향, 기업 프로파일과 경쟁 구도 등의 정보를 전해드립니다.

목차

제1장 주요 요약

• 시장 개요와 규모
• 로봇 분류
• 산업용 로봇 상황
• 세계 시장 예측
• 주요 촉진요인과 억제요인
• 기술 동향
• 산업 융합
• 경쟁 구도
• 투자 동향

제2장 서론

• 첨단 로봇의 정의
• 산업용 로봇
• 기존 로봇에서 첨단 로봇으로의 진화
• 주요 구현 기술
• 기술 준비도 평가
• 기준과 규제

제3장 세계 시장 분석

• 시장 세분화
  • 로봇 유형별
  • 기술별
  • 컴포넌트별
  • 최종 이용 산업별
• 지역 시장 분석
  • 북미
  • 유럽
  • 일본
  • 중국
  • 인도
• 가격 분석과 비용 구조
  • 비용 분석 : 로봇 유형별
  • 비용 분석 : 컴포넌트별
  • 회수기간/ROI : 용도별
  • 파라미터 비교 - 적재량 vs. 최고 이동 속도

제4장 기술 상황

• 협동 로봇(코봇)
• 자율주행 반송 로봇(AMR)
• 휴머노이드 산업용 로봇
• 모바일 로봇
• 로봇암
• 로봇 그립퍼
• 소프트웨어 및 컨트롤
• 지원 시스템

제5장 기술 컴포넌트와 서브시스템

• AI 및 제어 시스템
• 센서 및 지각

제6장 최종 이용 산업 분석

• 자동차
• 전자
• 식품 및 음료
• 제약
• 새로운 산업 용도

제7장 시장 성장 촉진요인과 억제요인

• 시장 성장 촉진요인

영문목차

The industrial robots market has undergone a dramatic transformation, evolving from simple, cage-enclosed machines into a sophisticated ecosystem encompassing traditional industrial robots, collaborative robots (cobots), humanoid robots, and intelligent mobile systems. This expanded landscape reflects manufacturing's shift toward flexible, adaptive automation that seamlessly integrates human workers with advanced robotic technologies across diverse industrial applications. Today's industrial robotics market spans multiple categories, each addressing specific manufacturing needs. Traditional industrial robots continue to dominate high-volume, high-precision applications like automotive welding and electronics assembly. However, collaborative robots have emerged as a game-changing segment, designed to work safely alongside human operators without protective barriers. These cobots feature advanced force-limiting technology, speed monitoring systems, and intuitive programming interfaces that enable rapid deployment and reconfiguration.

Humanoid robots represent the market's most ambitious frontier, offering human-like dexterity and mobility for complex manufacturing tasks. Companies like Boston Dynamics, Figure AI, and Agility Robotics are pioneering bipedal humanoid systems capable of navigating standard industrial environments, manipulating diverse objects, and performing multi-step assembly processes. These systems promise to address labor shortages while handling tasks too complex for traditional fixed-base robots.

Autonomous Mobile Robots (AMRs) and mobile manipulators combine mobility with manipulation capabilities, creating flexible automation solutions that can adapt to changing production layouts. These systems utilize advanced SLAM (Simultaneous Localization and Mapping) technology, LiDAR sensors, and AI-powered navigation to operate safely in dynamic environments alongside human workers.

Technological Convergence and AI Integration The integration of artificial intelligence has fundamentally transformed industrial robotics capabilities. Modern systems incorporate computer vision for real-time quality inspection, object recognition, and adaptive assembly. Machine learning algorithms enable robots to optimize their performance continuously, learning from production variations and improving accuracy over time. Multi-modal AI systems combine vision, force sensing, and audio processing to create robots capable of sophisticated decision-making.

Edge computing has become crucial for real-time processing, allowing robots to analyze sensor data locally and respond instantly to changing conditions. This capability is particularly important for collaborative applications where safety requires immediate response to human presence or unexpected obstacles. Advanced sensor fusion combines data from cameras, LiDAR, force sensors, and proximity detectors to create comprehensive environmental awareness.

The automotive industry remains the largest adopter of industrial robotics, increasingly deploying cobots for final assembly operations and humanoid robots for complex wiring and interior component installation. Electronics manufacturing has embraced collaborative robots for delicate component handling and testing procedures, while humanoid systems show promise for smartphone and tablet assembly requiring human-like dexterity. Food and beverage processing increasingly utilizes advanced robotics for packaging, quality inspection, and material handling. Collaborative robots excel in food preparation and packaging applications where flexibility and easy cleaning are essential. Pharmaceutical manufacturing adopts these technologies for sterile handling, precise dispensing, and complex assembly of medical devices.

Labor shortages continue driving market growth, with humanoid robots particularly positioned to address skilled labor gaps in industries like aerospace and shipbuilding. The aging workforce in developed nations creates opportunities for robots to perform physically demanding tasks while experienced workers focus on oversight and quality control.

Asia-Pacific leads global adoption, with China implementing ambitious automation initiatives across manufacturing sectors. Japanese companies like Honda and Toyota are pioneering humanoid robot applications in manufacturing, while South Korean firms focus on collaborative robotics for electronics production. European manufacturers emphasize collaborative systems and sustainable automation technologies, particularly in automotive and precision manufacturing. North American adoption focuses on advanced applications in aerospace, medical device manufacturing, and high-tech industries. The region's emphasis on reshoring manufacturing creates opportunities for sophisticated automation systems that can compete with low-cost overseas production.

The industrial robotics market is transitioning toward increasingly intelligent, adaptable systems. Robot-as-a-Service (RaaS) models are emerging to lower entry barriers, particularly for small and medium enterprises. These subscription-based approaches provide access to advanced robotics technology without significant capital investment.

Swarm robotics represents an emerging trend where multiple robots coordinate to accomplish complex tasks, particularly valuable in large-scale manufacturing and logistics operations. The integration of digital twin technology enables virtual testing and optimization of robotic systems before physical deployment.

As artificial intelligence continues advancing, the distinction between different robot types will blur, with systems becoming more versatile and capable of handling diverse tasks. The future industrial robotics market will likely feature increasingly autonomous systems that can adapt to new products, processes, and environments with minimal human intervention, fundamentally reshaping manufacturing's operational paradigms while creating new opportunities for human-robot collaboration.

"The Global Industrial Robots Market 2026-2046" provides in-depth analysis of the industrial robotics ecosystem, covering traditional industrial robots, collaborative robots (cobots), humanoid robots, autonomous mobile robots (AMRs), and emerging robotic technologies that are reshaping manufacturing across industries worldwide.

Report contents include:

• Market Segmentation & Revenue Analysis:
  • Detailed market size and growth forecasts for industrial robots, collaborative robots, humanoid robots, and mobile robots (2026-2046)
  • Revenue projections by robot type, technology, component, and end-use industry
  • Unit sales analysis across manufacturing, healthcare, logistics, agriculture, construction, and emerging sectors
  • Regional market analysis covering North America, Europe, Japan, China, and India
  • Pricing analysis and cost structure evaluation by robot category and application
• Technology Landscape & Innovation Trends:
  • Advanced AI integration including machine learning, computer vision, and sensor fusion technologies
  • Collaborative robotics evolution through six stages of human-robot interaction
  • Humanoid robot development for industrial applications with design considerations and manufacturing use cases
  • Autonomous mobile robot navigation technologies and transition from AGVs to AMRs
  • Robotic arms analysis including SCARA, Delta, and Cartesian robot configurations
  • End-effector technologies and gripper systems for diverse manufacturing applications
• Component Analysis & Supporting Systems:
  • Comprehensive sensor and perception systems including cameras, LiDAR, radar, and thermal imaging
  • AI and control systems featuring neuromorphic computing and edge processing capabilities
  • Software and control platforms for robotics applications
  • Linear motion systems, vision systems, and supporting infrastructure
  • Advanced materials including metals, polymers, composites, smart materials, and nanomaterials
• Industry Applications & End-Use Analysis:
  • Automotive industry opportunities, challenges, and robotic applications
  • Electronics manufacturing including 3C production challenges, quality control, and packaging automation
  • Food and beverage industry requirements, product variety handling, and hygiene considerations
  • Pharmaceutical manufacturing applications including sterile handling and precision dispensing
  • Emerging industrial applications in additive manufacturing and flexible manufacturing systems
• Emerging Technologies & Future Trends:
  • Swarm robotics technologies and multi-robot coordination systems
  • Human-robot collaboration advances and intuitive programming interfaces
  • Self-learning and adaptive robots using reinforcement learning
  • Cloud robotics and distributed computing architectures
  • Digital twin integration for simulation, predictive maintenance, and performance optimization
  • Robot-as-a-Service (RaaS) business models and subscription-based services
  • Soft robotics materials and actuators for delicate handling applications
  • Neuromorphic computing for energy-efficient robot perception
  • Micro-nano robots for medical and industrial applications
  • Brain-computer interfaces for advanced robot control
  • Mobile collaborative robots combining mobility with manipulation
  • Low-carbon robotics manufacturing and sustainable design approaches
• Technical & Implementation Challenges:
  • Perception and sensing limitations in complex environments
  • Manipulation and dexterity requirements for human-like tasks
  • Power and energy management optimization
  • Human-robot interaction safety and regulatory compliance
  • Integration complexity with existing manufacturing systems
  • Skills gaps and workforce training requirements
• Regulatory Landscape Analysis:
  • Safety standards and requirements for collaborative robots
  • Autonomous vehicle regulations and testing certifications
  • Industrial robot safety regulations across major markets
  • Data privacy and security requirements for connected robotics
  • Regional regulatory differences and compliance considerations
• Future Outlook & Technology Roadmap
• Company Profiles & Competitive Landscape. Companies profiled include: 1X Technologies, ABB, Advanced Farm Technologies, Aethon, Agibot, Agility Robotics, Agilox, AheadForm, AIRSKIN, ANYbotics AG, Apptronik, Ati Motors, Aubo Robotics, Boardwalk Robotics, Booster Robotics, Boston Dynamics, BridgeDP Robotics, Bright Machines, Bruker Alicona, Clearpath Robotics, Clone Robotics, Cognibotics, Contoro Robotics, CynLr, Dataa Robotics, Denso, Devanthro, Dexterity Inc., Diligent Robotics, Dobot Robotics, Doosan Robotics, Elephant Robotics, Epson, Estun Automation, Eureka Robotics, F&P Personal Robotics, Fairino, Fanuc, FDROBOT, FESTO, Fetch Robotics, Figure AI, ForwardX, Fourier Intelligence, Franka Emika GmbH, fruitcore robotics GmbH, Furhat Robotics, Geekplus, GrayMatter Robotics, GreyOrange, H2 Clipper Inc., Haber, Han's Robot, Hanwha Robotics, HEBI Robotics, HIWIN, Holiday Robotics, Honda, Hyundai Robotics, Inceptio, Inivation AG, InVia Robotics, Inovance, Jaka Robotics, Kawasaki Heavy Industries, Kepler, Keybotic, Kivnon, KUKA, Leju Robotics, Libiao Robotics, LimX Dynamics, Locus Robotics, Macco Robotics, Magazino GmbH, MagicLab, Mbodi AI, Mecademic, MiR, Monumental, Mitsubishi Electric, NACHI, NAVIGANTIS, Neura Robotics GmbH, Nomagic, NVIDIA, Oinride Oy, Omron, OnRobot, Panasonic and more......

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

• 1.1. Market Overview and Size
• 1.2. Robot Categorization
• 1.3. Industrial Robotics Landscape
• 1.4. Global Market Forecast
  • 1.4.1. Units
  • 1.4.2. Revenues
• 1.5. Key Drivers and Restraints
• 1.6. Technology Trends
  • 1.6.1. Automation for improved efficiency
    • 1.6.1.1. Robot Density in Manufacturing 2020-2024
    • 1.6.1.2. Growth of Robot Users 2020-2024
  • 1.6.2. Humanoid Robots
  • 1.6.3. Collaborative Robots (Cobots)
  • 1.6.4. Physical, Analytic and Generative AI
  • 1.6.5. Robotics Evolution Timeline
  • 1.6.6. Sustainability and Energy Consumption
  • 1.6.7. Addressing Labor Shortages
  • 1.6.8. Key Emerging Transitions in Sensing Technologies
• 1.7. Industry Convergence
  • 1.7.1. Mobile Robots vs. Fixed Automation
  • 1.7.2. Robot-as-a-Service (RaaS) Business Models
  • 1.7.3. Industry 5.0 - Transformative Vision
  • 1.7.4. Collaborative Robots Driving Industry 5.0
  • 1.7.5. Parameter Comparison - Payload vs. Speed
• 1.8. Competitive Landscape
  • 1.8.1. Global Competitive Landscape
  • 1.8.2. Leading Companies by Robot Type
  • 1.8.3. Major Industrial Robot Manufacturers
  • 1.8.4. Service Robot Specialists
  • 1.8.5. Cobot Manufacturers
  • 1.8.6. AI Robotics Companies
  • 1.8.7. Sensor and Component Developers
  • 1.8.8. End-Effector Suppliers
  • 1.8.9. Humanoid Robot Developers
• 1.9. Investment Trends
  • 1.9.1. Historic Funding Trends
  • 1.9.2. Recent investment
  • 1.9.3. Venture Capital Funding of Robotics Startups

2. INTRODUCTION

• 2.1. Defining Advanced Robotics
  • 2.1.1. Definitions of Key Terms
  • 2.1.2. Classification of Robot Types
  • 2.1.3. What are Robots?
    • 2.1.3.1. Industrial Robots
    • 2.1.3.2. Service Robots
    • 2.1.3.3. Collaborative Robots
    • 2.1.3.4. Mobile Robots
    • 2.1.3.5. Humanoid Robots
  • 2.1.4. Why Robots?
    • 2.1.4.1. Productivity Enhancement
    • 2.1.4.2. Labor Shortage Solutions
    • 2.1.4.3. Safety Improvements
    • 2.1.4.4. Quality and Precision Requirements
• 2.2. Industrial Robots
• 2.3. Evolution from Traditional to Advanced Robotics
  • 2.3.1. Historical Overview and Evolution
  • 2.3.2. Current State of Robotics in 2025
  • 2.3.3. Three Phases of Robot Adoption
  • 2.3.4. Evolution from Industrial to Service Robots
• 2.4. Key Enabling Technologies
  • 2.4.1. Artificial Intelligence and Machine Learning
    • 2.4.1.1. What is Artificial Intelligence?
      • 2.4.1.1.1. Key AI Methods for Robotics
    • 2.4.1.2. Deep Learning Approaches
    • 2.4.1.3. Convolutional Neural Networks in Robotics
  • 2.4.2. Computer Vision
    • 2.4.2.1. Image Recognition Technologies
    • 2.4.2.2. Object Detection and Tracking
    • 2.4.2.3. Scene Understanding
  • 2.4.3. Sensor Fusion
    • 2.4.3.1. Multi-sensor Integration
    • 2.4.3.2. Data Processing for Sensor Fusion
  • 2.4.4. Advanced Materials
    • 2.4.4.1. Metals
    • 2.4.4.2. Plastics and Polymers
    • 2.4.4.3. Composites
    • 2.4.4.4. Elastomers
    • 2.4.4.5. Smart Materials
    • 2.4.4.6. Textiles
    • 2.4.4.7. Ceramics
    • 2.4.4.8. Biomaterials
    • 2.4.4.9. Nanomaterials
    • 2.4.4.10. Coatings
      • 2.4.4.10.1. Self-healing coatings
      • 2.4.4.10.2. Conductive coatings
    • 2.4.4.11. Flexible and Soft Materials
  • 2.4.5. Edge Computing
    • 2.4.5.1. Local Processing vs. Cloud Computing
    • 2.4.5.2. Real-time Decision Making
  • 2.4.6. SLAM - Simultaneous Localization and Mapping
    • 2.4.6.1. LiDAR SLAM
    • 2.4.6.2. Visual SLAM (vSLAM)
    • 2.4.6.3. Hybrid SLAM Approaches
  • 2.4.7. Typical Sensors for Object Detection
    • 2.4.7.1. Camera-based Detection
    • 2.4.7.2. LiDAR-based Detection
    • 2.4.7.3. Radar Systems
    • 2.4.7.4. Ultrasonic Sensors
    • 2.4.7.5. Infrared and Thermal Sensors
• 2.5. Technology Readiness Assessment
  • 2.5.1. Technology Readiness Levels (TRL)
  • 2.5.2. Roadmap and Maturity Analysis by Industry
  • 2.5.3. Readiness Level of Technologies by Application Sector
• 2.6. Standards and Regulations
  • 2.6.1. Safety Requirements - Five Main Types
    • 2.6.1.1. Power and Force Limiting
    • 2.6.1.2. Speed and Separation Monitoring
    • 2.6.1.3. Hand Guiding
    • 2.6.1.4. Safety Monitored Stop
    • 2.6.1.5. Soft Impact Design
  • 2.6.2. Regional Safety Standards
    • 2.6.2.1. European Standards
    • 2.6.2.2. Asian Standards
  • 2.6.3. Global Regulatory Landscape
    • 2.6.3.1. Authorities Regulating Autonomous Driving
    • 2.6.3.2. Regulations for Delivery Robots and Drones
    • 2.6.3.3. Industrial Robot Regulations
    • 2.6.3.4. Data Privacy and Security Regulations
    • 2.6.3.5. Regional Differences in Regulations
    • 2.6.3.6. Data Security Requirements

3. GLOBAL MARKET ANALYSIS

• 3.1. Market Segmentation
  • 3.1.1. By Robot Type
    • 3.1.1.1. Industrial Robots
      • 3.1.1.1.1. Units
      • 3.1.1.1.2. Revenues
    • 3.1.1.2. Collaborative Robots (Cobots)
      • 3.1.1.2.1. By revenues
      • 3.1.1.2.2. By Payload Capacity
      • 3.1.1.2.3. By Degrees of Freedom
      • 3.1.1.2.4. By End-Effector Type
    • 3.1.1.3. Humanoid Robots
      • 3.1.1.3.1. By Type (Full-Size, Medium, Small)
      • 3.1.1.3.2. By Application
    • 3.1.1.4. Mobile Robots
      • 3.1.1.4.1. Autonomous Mobile Robots (AMRs)
      • 3.1.1.4.2. Automated Guided Vehicles (AGVs)
      • 3.1.1.4.3. Grid-Based Automated Guided Carts (AGCs)
      • 3.1.1.4.4. Mobile Picking Robots
      • 3.1.1.4.5. Mobile Manipulators
      • 3.1.1.4.6. Last-Mile Delivery Robots
      • 3.1.1.4.7. Heavy-Duty L4 Autonomous Trucks
  • 3.1.2. By Technology
    • 3.1.2.1. Navigation and Mapping
    • 3.1.2.2. Object Recognition and Tracking
    • 3.1.2.3. End-Effector and Manipulation
    • 3.1.2.4. Human-Robot Interaction
    • 3.1.2.5. Artificial Intelligence
  • 3.1.3. By Component
    • 3.1.3.1. Hardware
      • 3.1.3.1.1. Sensors
      • 3.1.3.1.2. Actuators
      • 3.1.3.1.3. Power Systems
      • 3.1.3.1.4. Control Systems
      • 3.1.3.1.5. End-Effectors
    • 3.1.3.2. Software
      • 3.1.3.2.1. Control Software
      • 3.1.3.2.2. Perception Software
      • 3.1.3.2.3. Human-Machine Interface
    • 3.1.3.3. Services
      • 3.1.3.3.1. Installation and Integration
      • 3.1.3.3.2. Maintenance and Support
  • 3.1.4. By End-use Industry
    • 3.1.4.1. Manufacturing
    • 3.1.4.2. Logistics and Warehousing
• 3.2. Regional Market Analysis
  • 3.2.1. North America
  • 3.2.2. Europe
  • 3.2.3. Japan
  • 3.2.4. China
  • 3.2.5. India
• 3.3. Pricing Analysis and Cost Structure
  • 3.3.1. Cost Analysis by Robot Type
    • 3.3.1.1. Industrial Robot Costs
    • 3.3.1.2. Collaborative Robot Costs
    • 3.3.1.3. Service Robot Costs
    • 3.3.1.4. Humanoid Robot Costs
    • 3.3.1.5. Mobile Robot Costs
  • 3.3.2. Cost Analysis by Component
    • 3.3.2.1. Sensor Costs
    • 3.3.2.2. Actuator and Power System Costs
    • 3.3.2.3. Computing and Control System Costs
    • 3.3.2.4. End-Effector Costs
  • 3.3.3. Payback Time/ROI by Application
    • 3.3.3.1. Manufacturing ROI
    • 3.3.3.2. Logistics ROI
  • 3.3.4. Parameter Comparison - Payload vs. Max Traveling Speed
    • 3.3.4.1. Industrial Robots Performance Metrics
    • 3.3.4.2. Mobile Robots Performance Metrics
    • 3.3.4.3. Collaborative Robots Performance Metrics

4. TECHNOLOGY LANDSCAPE

• 4.1. Collaborative Robots (Cobots)
  • 4.1.1. Six Stages of Human-Robot Interaction (HRI)
    • 4.1.1.1. Stage One: Non-Collaborative Robots
    • 4.1.1.2. Stage Two: Non-Collaborative with Virtual Guarding
    • 4.1.1.3. Stage Three: Laser Scanner Separation
    • 4.1.1.4. Stage Four: Shared Workspace
    • 4.1.1.5. Stage Five: Operators and Robots Working Together
    • 4.1.1.6. Stage Six: Autonomous Mobile Collaborative Robots
  • 4.1.2. Traditional Industrial Robots vs. Collaborative Robots
  • 4.1.3. Benefits and Drawbacks of Cobots
  • 4.1.4. Safety Requirements for Cobots
    • 4.1.4.1. Power and Force Limiting
    • 4.1.4.2. Speed and Separation Monitoring
    • 4.1.4.3. Hand Guiding
    • 4.1.4.4. Safety-Rated Monitored Stop
    • 4.1.4.5. Biomechanical Limit Criteria
  • 4.1.5. Cobot Cost Analysis
  • 4.1.6. Payload Summary of Cobots
  • 4.1.7. Overview of Commercialized Cobots
    • 4.1.7.1. Benchmarking Based on DoF, Payload, Weight
    • 4.1.7.2. 6-DoF Cobots
    • 4.1.7.3. 7-DoF Cobots
    • 4.1.7.4. Price Categories of Cobots
  • 4.1.8. Market Players
• 4.2. Autonomous Mobile Robots (AMRs)
  • 4.2.1. Transition from AGVs to AMRs
  • 4.2.2. Technology Evolution Towards Fully Autonomous Mobile Robots
  • 4.2.3. AMR Navigation Technologies
• 4.3. Humanoid Industrial Robots
  • 4.3.1. Applications in Manufacturing
  • 4.3.2. Design Considerations
  • 4.3.3. Market Players
• 4.4. Mobile Robots
  • 4.4.1. Rolling Robots
  • 4.4.2. Market Players
• 4.5. Robotic Arms
  • 4.5.1. Types and Applications
  • 4.5.2. SCARA Robots
  • 4.5.3. Delta Robots
  • 4.5.4. Cartesian (Gantry) Robots
  • 4.5.5. Market Players
• 4.6. Robotic Grippers
  • 4.6.1. Market Players
• 4.7. Software & Control
• 4.8. Supporting Systems
  • 4.8.1. Linear Motion Systems
    • 4.8.1.1. Rails
    • 4.8.1.2. Actuators for Cartesian robots or auxiliary axes
    • 4.8.1.3. Market Players
  • 4.8.2. Vision Systems
    • 4.8.2.1. Cameras
    • 4.8.2.2. LiDAR
    • 4.8.2.3. Sensors for guidance/QC
    • 4.8.2.4. Market Players

5. TECHNOLOGY COMPONENTS AND SUBSYSTEMS

• 5.1. AI and Control Systems
  • 5.1.1. Artificial Intelligence and Machine Learning
    • 5.1.1.1. AI Applications in Robotics
    • 5.1.1.2. Machine Learning Techniques for Robotics
  • 5.1.2. End-to-end AI
    • 5.1.2.1. Perception to Action Systems
    • 5.1.2.2. Implementation Challenges
  • 5.1.3. Multi-modal AI Algorithms
    • 5.1.3.1. Vision-Language Models
    • 5.1.3.2. Sensor-Fusion AI
  • 5.1.4. Intelligent Control Systems and Optimization
    • 5.1.4.1. Control Architectures
    • 5.1.4.2. Motion Planning
• 5.2. Sensors and Perception
  • 5.2.1. Sensory Systems in Robots
    • 5.2.1.1. Importance of Sensing in Robots
    • 5.2.1.2. Typical Sensors Used for Robots
  • 5.2.2. Sensors by Functions and Tasks
    • 5.2.2.1. Navigation and Mapping
    • 5.2.2.2. Object Detection and Recognition
    • 5.2.2.3. Safety and Collision Avoidance
    • 5.2.2.4. Environmental Sensing
  • 5.2.3. Sensors by Robot Type
    • 5.2.3.1. Industrial Robotic Arms
    • 5.2.3.2. AGVs and AMRs
    • 5.2.3.3. Collaborative Robots
    • 5.2.3.4. Drones
    • 5.2.3.5. Service Robots
    • 5.2.3.6. Underwater Robots
    • 5.2.3.7. Agricultural Robots
    • 5.2.3.8. Cleaning Robots
    • 5.2.3.9. Social Robots
  • 5.2.4. Vision Systems
    • 5.2.4.1. Cameras (RGB, Depth, Thermal, Event-based)
      • 5.2.4.1.1. RGB/Visible Light Cameras
      • 5.2.4.1.2. Depth Cameras
      • 5.2.4.1.3. Thermal Cameras
      • 5.2.4.1.4. Event-based Cameras
    • 5.2.4.2. CMOS Image Sensors vs. CCD Cameras
      • 5.2.4.2.1. Comparative Analysis
    • 5.2.4.3. Stereo Vision and 3D Perception
      • 5.2.4.3.1. Depth Calculation Methods
      • 5.2.4.3.2. 3D Reconstruction
    • 5.2.4.4. In-Camera Computer Vision
      • 5.2.4.4.1. Edge Processing
      • 5.2.4.4.2. Applications in Autonomous Vehicles
    • 5.2.4.5. Hyperspectral Imaging Sensors

6. END-USE INDUSTRY ANALYSIS

• 6.1. Automotive
  • 6.1.1. Opportunities and Challenges
  • 6.1.2. Applications
• 6.2. Electronics
  • 6.2.1. 3C Manufacturing Challenges
  • 6.2.2. Production Volume Requirements
  • 6.2.3. Quality Control
  • 6.2.4. Applications
  • 6.2.5. Testing and Inspection
  • 6.2.6. Packaging
• 6.3. Food and Beverage
  • 6.3.1. Industry Challenges and Requirements
  • 6.3.2. Product Variety
  • 6.3.3. Applications
    • 6.3.3.1. Palletizing
    • 6.3.3.2. Packaging
    • 6.3.3.3. Food Processing
• 6.4. Pharmaceutical
  • 6.4.1. Industry Requirements
  • 6.4.2. Applications
• 6.5. Emerging Industrial Applications
  • 6.5.1. Additive manufacturing integration
  • 6.5.2. Flexible manufacturing systems
  • 6.5.3. Lights-out manufacturing
  • 6.5.4. Mass customization robotics

7. MARKET DRIVERS AND RESTRAINTS

• 7.1. Market Drivers
  • 7.1.1. Labor Shortages and Wage Inflation
    • 7.1.1.1. Global Labor Market Trends
    • 7.1.1.2. Industry-Specific Impacts
  • 7.1.2. Productivity and Efficiency Demands
    • 7.1.2.1. Manufacturing Efficiency
    • 7.1.2.2. Logistics Optimization
    • 7.1.2.3. Healthcare Productivity
  • 7.1.3. Quality and Precision Requirements
    • 7.1.3.1. Manufacturing Quality Control
    • 7.1.3.2. Healthcare Precision
  • 7.1.4. Workplace Safety Concerns
    • 7.1.4.1. Hazardous Environment Applications
    • 7.1.4.2. Ergonomic Considerations
  • 7.1.5. Aging Population
    • 7.1.5.1. Healthcare Applications
    • 7.1.5.2. Workforce Replacement
  • 7.1.6. Advancements in Artificial Intelligence and Machine Learning
    • 7.1.6.1. Improved Perception Systems
    • 7.1.6.2. Enhanced Decision Making
    • 7.1.6.3. Autonomous Capabilities
  • 7.1.7. Need for Personal Assistance and Companionship
    • 7.1.7.1. Eldercare Applications
    • 7.1.7.2. Household Assistance
  • 7.1.8. Exploration of Hazardous and Extreme Environments
    • 7.1.8.1. Nuclear Applications
    • 7.1.8.2. Deep Sea Exploration
    • 7.1.8.3. Space Applications
  • 7.1.9. E-commerce Growth
    • 7.1.9.1. Last-Mile Delivery Challenges
    • 7.1.9.2. Warehouse Automation Needs
• 7.2. Market Restraints
  • 7.2.1. High Initial Investment Costs
    • 7.2.1.1. Robot Hardware Costs
    • 7.2.1.2. Integration and Implementation Costs
  • 7.2.2. Technical Limitations
    • 7.2.2.1. AI and Perception Challenges
    • 7.2.2.2. Manipulation Challenges
    • 7.2.2.3. Energy and Power Limitations
  • 7.2.3. Implementation Challenges
    • 7.2.3.1. Integration with Existing Systems
    • 7.2.3.2. User Training and Adoption
  • 7.2.4. Safety and Regulatory Concerns
    • 7.2.4.1. Human-Robot Collaboration Safety
    • 7.2.4.2. Autonomous System Regulations
  • 7.2.5. Workforce Resistance and Social Acceptance
    • 7.2.5.1. Employment Concerns
    • 7.2.5.2. Human-Robot Interaction Challenges

8. EMERGING TRENDS AND DEVELOPMENTS

• 8.1. Swarm Robotics
  • 8.1.1. Technologies and Approaches
  • 8.1.2. Application Potential
  • 8.1.3. Market Outlook
• 8.2. Human-Robot Collaboration
  • 8.2.1. Advances in Safe Interaction
  • 8.2.2. Intuitive Programming Interfaces
  • 8.2.3. Market Implementation Examples
• 8.3. Self-Learning and Adaptive Robots
  • 8.3.1. Reinforcement Learning Applications
  • 8.3.2. Transfer Learning
  • 8.3.3. Continual Learning Systems
• 8.4. Cloud Robotics
  • 8.4.1. Distributed Computing for Robotics
  • 8.4.2. Remote Operation Capabilities
• 8.5. Digital Twin Integration
  • 8.5.1. Simulation and Planning
  • 8.5.2. Predictive Maintenance
  • 8.5.3. Performance Optimization
• 8.6. Robot-as-a-Service (RaaS) Business Models
  • 8.6.1. Subscription-Based Services
  • 8.6.2. Pay-Per-Use Models
  • 8.6.3. Market Adoption Trends
• 8.7. Soft Robotics
  • 8.7.1. Materials and Actuators
• 8.8. Neuromorphic Computing for Robotics
  • 8.8.1. Brain-Inspired Computing Architectures
  • 8.8.2. Applications in Perception
  • 8.8.3. Energy Efficiency Benefits
• 8.9. Micro-nano Robots
  • 8.9.1. Technologies and Designs
  • 8.9.2. Medical Applications
  • 8.9.3. Industrial Applications
• 8.10. Brain Computer Interfaces
  • 8.10.1. Non-Invasive BCIs
  • 8.10.2. Invasive BCIs
  • 8.10.3. Applications in Robot Control
• 8.11. Mobile Cobots
  • 8.11.1. Technologies and Designs
  • 8.11.2. Applications
  • 8.11.3. Market Outlook
• 8.12. Industry 5.0 and Collaborative Robots
  • 8.12.1. Human-Machine Collaboration
  • 8.12.2. Sustainable Manufacturing
  • 8.12.3. Implementation Examples
• 8.13. Low-carbon Robotics Manufacturing
  • 8.13.1. Sustainable Design Approaches
  • 8.13.2. Energy-Efficient Operation
  • 8.13.3. End-of-Life Considerations
• 8.14. Autonomous Navigation and Localization
  • 8.14.1. SLAM Advancements
  • 8.14.2. Multi-Sensor Fusion
  • 8.14.3. GPS-Denied Navigation
• 8.15. Navigation Sensors Driven by Autonomous Mobility
  • 8.15.1. LiDAR Innovations
  • 8.15.2. Computer Vision Advancements
  • 8.15.3. Sensor Fusion Approaches

9. CHALLENGES AND OPPORTUNITIES

• 9.1. Technical Challenges
  • 9.1.1. Perception and Sensing
  • 9.1.2. Manipulation and Dexterity
  • 9.1.3. Power and Energy Management
  • 9.1.4. Human-Robot Interaction
• 9.2. Market Challenges
  • 9.2.1. Cost Barriers
  • 9.2.2. Skills and Training Gaps
  • 9.2.3. Integration Complexity
  • 9.2.4. Supply Chain Issues
• 9.3. Regulatory Challenges
  • 9.3.1. Regulations for Autonomous Vehicles
    • 9.3.1.1. SAE Level 4-5 Regulations
    • 9.3.1.2. Testing and Certification Requirements
  • 9.3.2. Regulations for Delivery Drones
    • 9.3.2.1. Airspace Regulations
    • 9.3.2.2. Payload and Distance Limitations
  • 9.3.3. Recent Regulatory Updates

10. FUTURE OUTLOOK

• 10.1. Technology Roadmap (2025-2046)
  • 10.1.1. Short-term Developments (2025-2030)
  • 10.1.2. Medium-term Developments (2030-2035)
  • 10.1.3. Long-term Developments (2035-2046)
• 10.2. Industry Convergence Opportunities
  • 10.2.1. Robotics and AI
  • 10.2.2. Robotics and IoT
  • 10.2.3. Robotics and Advanced Manufacturing
• 10.3. Robotics and the Future of Work
  • 10.3.1. Job Transformation
  • 10.3.2. New Skill Requirements
  • 10.3.3. Human-Robot Collaboration Models

11. COMPANY PROFILES (120 company profiles)

12. REFERENCES