세계의 첨단 로봇공학 시장(2025-2045년)

The global market for advanced robotics has entered a transformative phase, characterized by unprecedented technological innovation, expanding applications across industries, and massive capital infusions. Adoption is accelerating across manufacturing, healthcare, logistics, agriculture, and consumer sectors. The industrial robotics segment continues to dominate the market, particularly in manufacturing where automotive and electronics industries remain the largest adopters. However, the most significant growth is occurring in collaborative robots (cobots) and autonomous mobile robots (AMRs), which are increasingly deployed in warehouses, hospitals, and retail environments. China, Japan, South Korea, the United States, and Germany represent the largest markets, collectively accounting for over 70% of global installations.

The most telling indicator of robotics' future prospects is the dramatic surge in venture capital investment. Investments in robotic projects (including autonomous vehicles) raised a total of more than $7 billion in October 2024 alone, and there have been several major investments in 2025. This investment surge extends across the robotics value chain, from core hardware innovations to enabling technologies like computer vision, tactile sensing, and AI-driven control systems. Particularly noteworthy is the concentration of mega-rounds in emerging categories like humanoid robots, agricultural automation, and surgical robotics. Defense spending on autonomous systems and AI-powered robotics is rising, with governments investing in drone technology and robotic combat vehicles. This capital influx is enabling longer runways for technology development and commercialization in fields that previously struggled to attract institutional investment due to long development timelines.

The future trajectory of advanced robotics will be shaped by several converging factors. First, the integration of artificial intelligence-particularly large language models and computer vision systems-is dramatically enhancing robots' ability to understand complex environments, learn from experience, and execute sophisticated tasks. This capability expansion is opening entirely new application domains previously deemed too complex for automation. Second, the ongoing global labor shortage, particularly acute in developed economies with aging populations, creates structural demand for robotics solutions. Japan's shrinking workforce and Europe's manufacturing challenges exemplify this dynamic, which shows no signs of abating.

Regulatory frameworks are gradually adapting to accommodate autonomous systems, with standards bodies and government agencies developing certification processes that will accelerate safe deployment in public spaces and sensitive environments. As technical barriers fall and integration complexity diminishes, robotics adoption will accelerate across previously underserved sectors, transforming global productivity and creating new economic paradigms. The long-promised robotics revolution appears to have finally arrived, backed by unprecedented capital investment and technological capability.

"The Global Advanced Robotics Market 2025-2045" is a landmark 790+ page report that delivers an exhaustive assessment of the rapidly evolving global advanced robotics market, providing unprecedented depth of data, analysis, and strategic insight covering the critical 20-year period from 2025 to 2045. Drawing upon extensive primary research and proprietary forecasting methodologies, the report offers granular market sizing, technology analysis, and investment intelligence, making it an essential resource for investors, manufacturers, suppliers, and end-users navigating the robotics revolution.

Report contents include:

• Comprehensive Market Sizing and Forecasts: Detailed unit sales and revenue projections from 2025-2045, segmented by robot type, technology, component, and end-use industry, with both conservative and optimistic scenarios
• Deep Technology Analysis: Thorough evaluation of AI integration, computer vision advancements, sensor fusion innovations, advanced materials development, and emerging technologies shaping the future of robotics
• Regional Market Analysis: Comprehensive breakdown of market dynamics, growth rates, and competitive positioning across North America, Europe, Asia-Pacific (with special focus on Japan and China), Latin America, and Middle East & Africa
• Competitive Landscape: Detailed profiles of 260+ companies spanning the entire robotics value chain, from established industrial robot manufacturers to emerging start-ups. Companies profiled include 1X Technologies, 4AF Robotics, ABB, Advanced Farm Technologies, Aeroptic, Aerobot, Aescape, Agerpoint, Agersens, Agibot, Agility Robotics, AgroBot, Agtonomy, AheadForm, Aigen, AIDIN Robotics, AIRSKIN, Allflex, AMD Xilinx, Anybotic, Apptronik, Arable Labs, Audite Robotics, Aubo Robotics, Aurora, Automated Ag, Automated Architecture, Baidu, Barnstorm Agtec, Bear Robotics, BeeWise Technologies, Bio Bee, Biofeed, BionicM, Blue River Technology, Blue White Robotics, Boardwalk Robotics, Bonsai Robotics, Booster Robotics, Boston Dynamics, BoviSync, BovControl, BridgeDP Robotics, Bright Machines, Bruker Alicona, Burro, BXI Robotics, Cainthus, Capstan Medical, Carbon Bee, Carbon Robotics, Cattle Care, ClearPath Robotics, Clearview Imaging, Clone Robotics, Cloudfarms, CNH Industrial, Cobionix, Collaborative Robotics, Connecterra, Cornerstone Robotics, CowAlert, Cowlar, Cow Manager, Crover, CynLr, Dataa Robotics, DeepWay, DeLaval, Delta, Denso, Devanthro, Dexterity, Digital Harvest, Diligent Robotics, Dobot Robotics, Dogtooth Technologies, Doosan Robotics, Dreame Technology, Dynium Robot, EarthOptics, EarthSense, Ecovacs, eCoRobotix, eCow, Einride, EIO Diagnostics, Electron Robots, Elephant Robotics, Elite Robots, Embark, Embodied, Enchanted Tools, Engineered Arts, ENGINEAI, Eureka Robotics, EX Robots, F&P Personal Robotics, Fanuc, Farm-ng, Faromatics, FDROBOT, FeedFlo, FeedLogic, FESTO, FFRobotics, Figure AI, Fourier Intelligence, Four Growers, Franka Emika, Galbot, Generation Robots, Genrobotics, GRIMME, GrayMatter Robotics, Guardian Agriculture, Halter, Hanson Robotics, Harvest Croo, Herddog, Herdsy, Honda, Horizon Surgical Systems, IceRobotics, Inceptio, Inivation, InterPuls, Interlink Electronics, Kassow Robots, Kawasaki Heavy Industries, Keenon Robotics, Kepler, Kodiak Robotics, Koidra, K-Scale Labs, Kubota, KUKA, Lattice Medical, Leju Robotics, Lely, LimX Dynamics, LuxAI, Macco Robotics, MagicLab, Magnendo, MastiLine, Mendaera, Mentee Robotics, Metabolic Robotics, Milk Moovement, Mimic, Mineral, miRobot, Mirror Me, Molg, Monarch, MooCall, Moonsyst, Mov.ai, MUKS Robotics, NACHI, Naio Technologies, NAVIGANTIS, Neura Robotics GmbH, Nofence, Nomagic, NVIDIA, Octinion, Oinride Oy and more.....
• End-Use Industry Analysis: In-depth examination of robotics applications, market drivers, restraints, and growth projections across 11 key sectors including manufacturing, healthcare, logistics, agriculture, and consumer markets
• Technology Readiness Assessment: Critical evaluation of technology maturity levels across different robotics categories, with roadmaps highlighting commercialization timelines
• Strategic Insights: Analysis of emerging business models, industry convergence opportunities, regulatory developments, and future market evolution scenarios
• Market Analysis and Forecasting:
  • Proprietary forecasting methodology incorporating S-curve adoption patterns and industry-specific growth drivers
  • Historical market analysis (2019-2024) providing crucial context for future projections
  • Detailed market segmentation by robot type (industrial, collaborative, service, humanoid, mobile) with unit and revenue forecasts
  • Pricing analysis and cost structure breakdowns for each robot category
  • Comprehensive value chain analysis covering raw materials, manufacturing, software, integration, and distribution
• Technology Landscape:
  • Detailed assessment of key enabling technologies including AI/ML, computer vision, sensor fusion, and advanced materials
  • Analysis of technology readiness levels (TRLs) by application sector
  • In-depth examination of collaborative robot (cobot) technologies, safety requirements, and commercialization status
  • Comprehensive coverage of autonomous mobile robots (AMRs), articulated robots, and emerging humanoid industrial platforms
  • Detailed analysis of agricultural robotics, healthcare systems, defense applications, and construction platforms
• Investment Intelligence:
  • Complete tracking of all major funding rounds in robotics from 2022-2025, with detailed analysis of investment size, investor profiles, and valuation trends
  • Venture capital funding patterns across different robotics categories and geographies
  • Strategic corporate investment analysis, identifying key partnerships and acquisition targets
  • ROI analysis and payback period calculations across different robotics applications
  • Investment opportunity assessment highlighting high-growth market segments
• End-Use Industry Applications:
  • Manufacturing sector analysis including automotive, electronics, food & beverage, and pharmaceutical
  • Healthcare applications spanning surgical robotics, rehabilitation, hospital logistics, and care robots
  • Logistics and warehousing automation trends including material transport, order picking, and last-mile delivery
  • Agricultural robotics covering harvesting, seeding, crop monitoring, and dairy farming
  • Construction robotics applications including 3D printing, demolition, and masonry
  • Retail, entertainment, defense, energy, and consumer applications
• Regulatory and Strategic Analysis:
  • Comprehensive review of safety standards and regulations by region
  • Analysis of key market drivers and restraints shaping industry growth
  • Emerging trends including swarm robotics, human-robot collaboration, and cloud robotics
  • Technology roadmap (2025-2045) with short, medium, and long-term development projections
  • Future implications for workforce transformation and human-robot collaboration models

This unparalleled analysis covers the entire robotics value chain from component suppliers to full-system integrators, providing competitive positioning, product strategies, technological capabilities, and market focus for each player, enabling readers to comprehensively understand the complex competitive landscape and identify strategic opportunities for partnership, investment, or market entry. The report's exhaustive company coverage reflects the diverse and rapidly evolving robotics ecosystem, capturing both established industrial robotics giants with decades of market presence and cutting-edge startups pioneering new applications across humanoid robotics, agricultural automation, collaborative systems, and autonomous mobility, providing a definitive resource for understanding the companies driving robotics innovation through 2045.

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

• 1.1. Market Overview and Size
• 1.2. Robot Categorization
• 1.3. Global Market Forecast
  • 1.3.1. Units
  • 1.3.2. Revenues
• 1.4. Key Drivers and Restraints
• 1.5. Technology Trends
  • 1.5.1. Humanoid Robots
  • 1.5.2. Collaborative Robots (Cobots)
  • 1.5.3. Physical, Analytic and Generative AI
  • 1.5.4. Robotics Evolution Timeline
  • 1.5.5. Sustainability and Energy Consumption
  • 1.5.6. Addressing Labor Shortages
  • 1.5.7. Key Emerging Transitions in Sensing Technologies
• 1.6. Industry Convergence
  • 1.6.1. Mobile Robots vs. Fixed Automation
  • 1.6.2. Robot-as-a-Service (RaaS) Business Models
  • 1.6.3. Industry 5.0 - Transformative Vision
  • 1.6.4. Collaborative Robots Driving Industry 5.0
  • 1.6.5. Parameter Comparison - Payload vs. Speed
• 1.7. Competitive Landscape
  • 1.7.1. Global Competitive Landscape
  • 1.7.2. Leading Companies by Robot Type
  • 1.7.3. Major Industrial Robot Manufacturers
  • 1.7.4. Service Robot Specialists
  • 1.7.5. Cobot Manufacturers
  • 1.7.6. AI Robotics Companies
  • 1.7.7. Sensor and Component Developers
  • 1.7.8. End-Effector Suppliers
  • 1.7.9. Humanoid Robot Developers
• 1.8. Investment Trends
  • 1.8.1. Historic Funding Trends
  • 1.8.2. Recent investment
  • 1.8.3. Venture Capital Funding of Robotics Startups

2. INTRODUCTION TO ADVANCED ROBOTICS

• 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. Evolution from Traditional to Advanced Robotics
  • 2.2.1. Historical Overview and Evolution
  • 2.2.2. Current State of Robotics in 2025
  • 2.2.3. Three Phases of Robot Adoption
  • 2.2.4. Evolution from Industrial to Service Robots
• 2.3. Key Enabling Technologies
  • 2.3.1. Artificial Intelligence and Machine Learning
    • 2.3.1.1. What is Artificial Intelligence?
      • 2.3.1.1.1. Key AI Methods for Robotics
    • 2.3.1.2. Deep Learning Approaches
    • 2.3.1.3. Convolutional Neural Networks in Robotics
  • 2.3.2. Computer Vision
    • 2.3.2.1. Image Recognition Technologies
    • 2.3.2.2. Object Detection and Tracking
    • 2.3.2.3. Scene Understanding
  • 2.3.3. Sensor Fusion
    • 2.3.3.1. Multi-sensor Integration
    • 2.3.3.2. Data Processing for Sensor Fusion
  • 2.3.4. Advanced Materials
    • 2.3.4.1. Metals
    • 2.3.4.2. Plastics and Polymers
    • 2.3.4.3. Composites
    • 2.3.4.4. Elastomers
    • 2.3.4.5. Smart Materials
    • 2.3.4.6. Textiles
    • 2.3.4.7. Ceramics
    • 2.3.4.8. Biomaterials
    • 2.3.4.9. Nanomaterials
    • 2.3.4.10. Coatings
      • 2.3.4.10.1. Self-healing coatings
      • 2.3.4.10.2. Conductive coatings
    • 2.3.4.11. Flexible and Soft Materials
  • 2.3.5. Edge Computing
    • 2.3.5.1. Local Processing vs. Cloud Computing
    • 2.3.5.2. Real-time Decision Making
  • 2.3.6. SLAM - Simultaneous Localization and Mapping
    • 2.3.6.1. LiDAR SLAM
    • 2.3.6.2. Visual SLAM (vSLAM)
    • 2.3.6.3. Hybrid SLAM Approaches
  • 2.3.7. Typical Sensors for Object Detection
    • 2.3.7.1. Camera-based Detection
    • 2.3.7.2. LiDAR-based Detection
    • 2.3.7.3. Radar Systems
    • 2.3.7.4. Ultrasonic Sensors
    • 2.3.7.5. Infrared and Thermal Sensors
• 2.4. Technology Readiness Assessment
  • 2.4.1. Technology Readiness Levels (TRL)
  • 2.4.2. Roadmap and Maturity Analysis by Industry
  • 2.4.3. Readiness Level of Technologies by Application Sector
• 2.5. Standards and Regulations
  • 2.5.1. Safety Requirements - Five Main Types
    • 2.5.1.1. Power and Force Limiting
    • 2.5.1.2. Speed and Separation Monitoring
    • 2.5.1.3. Hand Guiding
    • 2.5.1.4. Safety Monitored Stop
    • 2.5.1.5. Soft Impact Design
  • 2.5.2. Regional Safety Standards
    • 2.5.2.1. European Standards
    • 2.5.2.2. Asian Standards
  • 2.5.3. Global Regulatory Landscape
    • 2.5.3.1. Authorities Regulating Autonomous Driving
    • 2.5.3.2. Regulations for Delivery Robots and Drones
    • 2.5.3.3. Industrial Robot Regulations
    • 2.5.3.4. Data Privacy and Security Regulations
    • 2.5.3.5. Regional Differences in Regulations
    • 2.5.3.6. Data Security Requirements

3. GLOBAL MARKET ANALYSIS

• 3.1. Market Size and Growth Forecast (2025-2045)
  • 3.1.1. Historical Market Data (2019-2024)
    • 3.1.1.1. Historic Cobot Market Size
    • 3.1.1.2. Historic Service Robot Market Size
    • 3.1.1.3. Historic Mobile Robot Market Size
• 3.2. Market Segmentation
  • 3.2.1. By Robot Type
    • 3.2.1.1. Industrial Robots
    • 3.2.1.2. Collaborative Robots (Cobots)
      • 3.2.1.2.1. By revenues
      • 3.2.1.2.2. By Payload Capacity
      • 3.2.1.2.3. By Degrees of Freedom
      • 3.2.1.2.4. By End-Effector Type
    • 3.2.1.3. Service Robots
      • 3.2.1.3.1. Professional Service Robots
      • 3.2.1.3.2. Personal/Domestic Service Robots
      • 3.2.1.3.3. Entertainment Robots
    • 3.2.1.4. Humanoid Robots
      • 3.2.1.4.1. By Type (Full-Size, Medium, Small)
      • 3.2.1.4.2. By Application
    • 3.2.1.5. Mobile Robots
      • 3.2.1.5.1. Autonomous Mobile Robots (AMRs)
      • 3.2.1.5.2. Automated Guided Vehicles (AGVs)
      • 3.2.1.5.3. Grid-Based Automated Guided Carts (AGCs)
      • 3.2.1.5.4. Mobile Picking Robots
      • 3.2.1.5.5. Mobile Manipulators
      • 3.2.1.5.6. Heavy-Duty L4 Autonomous Trucks
  • 3.2.2. By Technology
    • 3.2.2.1. Navigation and Mapping
    • 3.2.2.2. Object Recognition and Tracking
    • 3.2.2.3. End-Effector and Manipulation
    • 3.2.2.4. Human-Robot Interaction
    • 3.2.2.5. Artificial Intelligence
  • 3.2.3. By Component
    • 3.2.3.1. Hardware
      • 3.2.3.1.1. Sensors
      • 3.2.3.1.2. Actuators
      • 3.2.3.1.3. Power Systems
      • 3.2.3.1.4. Control Systems
      • 3.2.3.1.5. End-Effectors
    • 3.2.3.2. Software
      • 3.2.3.2.1. Control Software
      • 3.2.3.2.2. Perception Software
      • 3.2.3.2.3. Human-Machine Interface
    • 3.2.3.3. Services
      • 3.2.3.3.1. Installation and Integration
      • 3.2.3.3.2. Maintenance and Support
  • 3.2.4. By End-use Industry
    • 3.2.4.1. Manufacturing
    • 3.2.4.2. Healthcare
    • 3.2.4.3. Logistics and Warehousing
    • 3.2.4.4. Agriculture
    • 3.2.4.5. Construction
    • 3.2.4.6. Retail and Hospitality
    • 3.2.4.7. Military and Defense
    • 3.2.4.8. Energy and Utilities
    • 3.2.4.9. Education and Research
    • 3.2.4.10. Consumer and Domestic
    • 3.2.4.11. Entertainment and Leisure
• 3.3. Regional Market Analysis
  • 3.3.1. North America
  • 3.3.2. Europe
  • 3.3.3. Japan
  • 3.3.4. China
  • 3.3.5. India
• 3.4. Pricing Analysis and Cost Structure
  • 3.4.1. Cost Analysis by Robot Type
    • 3.4.1.1. Industrial Robot Costs
    • 3.4.1.2. Collaborative Robot Costs
    • 3.4.1.3. Service Robot Costs
    • 3.4.1.4. Humanoid Robot Costs
    • 3.4.1.5. Mobile Robot Costs
  • 3.4.2. Cost Analysis by Component
    • 3.4.2.1. Sensor Costs
    • 3.4.2.2. Actuator and Power System Costs
    • 3.4.2.3. Computing and Control System Costs
    • 3.4.2.4. End-Effector Costs
  • 3.4.3. Payback Time/ROI by Application
    • 3.4.3.1. Manufacturing ROI
    • 3.4.3.2. Logistics ROI
    • 3.4.3.3. Healthcare ROI
    • 3.4.3.4. Agricultural ROI
  • 3.4.4. Parameter Comparison - Payload vs. Max Traveling Speed
    • 3.4.4.1. Industrial Robots Performance Metrics
    • 3.4.4.2. Mobile Robots Performance Metrics
    • 3.4.4.3. Collaborative Robots Performance Metrics

4. TECHNOLOGY LANDSCAPE

• 4.1. Industrial Robotics
  • 4.1.1. Collaborative Robots (Cobots)
    • 4.1.1.1. Six Stages of Human-Robot Interaction (HRI)
      • 4.1.1.1.1. Stage One: Non-Collaborative Robots
      • 4.1.1.1.2. Stage Two: Non-Collaborative with Virtual Guarding
      • 4.1.1.1.3. Stage Three: Laser Scanner Separation
      • 4.1.1.1.4. Stage Four: Shared Workspace
      • 4.1.1.1.5. Stage Five: Operators and Robots Working Together
      • 4.1.1.1.6. Stage Six: Autonomous Mobile Collaborative Robots
    • 4.1.1.2. Traditional Industrial Robots vs. Collaborative Robots
    • 4.1.1.3. Benefits and Drawbacks of Cobots
    • 4.1.1.4. Safety Requirements for Cobots
      • 4.1.1.4.1. Power and Force Limiting
      • 4.1.1.4.2. Speed and Separation Monitoring
      • 4.1.1.4.3. Hand Guiding
      • 4.1.1.4.4. Safety-Rated Monitored Stop
      • 4.1.1.4.5. Biomechanical Limit Criteria
    • 4.1.1.5. Cobot Cost Analysis
    • 4.1.1.6. Payload Summary of Cobots
    • 4.1.1.7. Overview of Commercialized Cobots
      • 4.1.1.7.1. Benchmarking Based on DoF, Payload, Weight
      • 4.1.1.7.2. 6-DoF Cobots
      • 4.1.1.7.3. 7-DoF Cobots
      • 4.1.1.7.4. Price Categories of Cobots
  • 4.1.2. Autonomous Mobile Robots (AMRs)
    • 4.1.2.1. Transition from AGVs to AMRs
    • 4.1.2.2. Technology Evolution Towards Fully Autonomous Mobile Robots
    • 4.1.2.3. AMR Navigation Technologies
  • 4.1.3. Articulated Robots
    • 4.1.3.1. Types and Applications
  • 4.1.4. Humanoid Industrial Robots
    • 4.1.4.1. Applications in Manufacturing
    • 4.1.4.2. Design Considerations
• 4.2. Service Robotics
  • 4.2.1. Professional Service Robots
    • 4.2.1.1. Market Position of Service Robotics
    • 4.2.1.2. Categories and Applications
    • 4.2.1.3. Key Technologies
  • 4.2.2. Personal/Domestic Service Robots
    • 4.2.2.1. Market Overview
    • 4.2.2.2. Types and Applications
    • 4.2.2.3. Consumer Adoption Trends
  • 4.2.3. Entertainment Robots
    • 4.2.3.1. Market Overview
    • 4.2.3.2. Types and Applications
    • 4.2.3.3. Technology Features
• 4.3. Healthcare and Medical Robotics
  • 4.3.1. Surgical Robots
    • 4.3.1.1. Market Overview
    • 4.3.1.2. Key Technologies
    • 4.3.1.3. Companies
    • 4.3.1.4. Regulatory Considerations
  • 4.3.2. Rehabilitation Robots
    • 4.3.2.1. Types and Applications
    • 4.3.2.2. Market Drivers
  • 4.3.3. Hospital Logistics Robots
    • 4.3.3.1. Applications
    • 4.3.3.2. Market Drivers
  • 4.3.4. Care Robots
    • 4.3.4.1. Eldercare Applications
    • 4.3.4.2. Market Challenges
  • 4.3.5. Robotic Surgery and Minimally Invasive Procedures
    • 4.3.5.1. Key Technologies
    • 4.3.5.2. Market Trends
  • 4.3.6. Intelligent Health Monitoring and Diagnostics
    • 4.3.6.1. Technologies
    • 4.3.6.2. Applications
  • 4.3.7. Telemedicine and Remote Health Management
    • 4.3.7.1. Technologies
    • 4.3.7.2. Applications
  • 4.3.8. Robotics in Mental Health
    • 4.3.8.1. Applications
    • 4.3.8.2. Market Potential
• 4.4. Military and Defense Robotics
  • 4.4.1. Unmanned Ground Vehicles (UGVs)
    • 4.4.1.1. Applications
    • 4.4.1.2. Technologies
  • 4.4.2. Unmanned Aerial Vehicles (UAVs)
    • 4.4.2.1. Applications
    • 4.4.2.2. Technologies
  • 4.4.3. Unmanned Underwater Vehicles (UUVs)
    • 4.4.3.1. Applications
    • 4.4.3.2. Technologies
• 4.5. Agricultural Robotics
  • 4.5.1. Challenges Facing 21st Century Agriculture
    • 4.5.1.1. Productivity and Labor Issues
    • 4.5.1.2. Labor Shortages and Rising Costs
    • 4.5.1.3. Agrochemical Challenges
    • 4.5.1.4. Environmental Considerations
  • 4.5.2. Agricultural Robot Applications
    • 4.5.2.1. Current Uses
    • 4.5.2.2. Potential Uses
    • 4.5.2.3. Technology Readiness by Application Area
  • 4.5.3. Harvesting Robots
    • 4.5.3.1. Fresh Fruit Picking Robots
      • 4.5.3.1.1. Apple Harvesting Robots
      • 4.5.3.1.2. Strawberry Harvesting Robots
      • 4.5.3.1.3. Other Fruit Harvesting Robots
    • 4.5.3.2. Vegetable Harvesting Robots
      • 4.5.3.2.1. Asparagus Harvesting Robots
      • 4.5.3.2.2. Other Vegetable Harvesting Robots
  • 4.5.4. Seeding and Planting Robots
    • 4.5.4.1. Precision Seeding Applications
    • 4.5.4.2. Variable Rate Technology
  • 4.5.5. Crop Monitoring Robots
    • 4.5.5.1. Soil Analysis
    • 4.5.5.2. Plant Health Monitoring
  • 4.5.6. Weed and Pest Control Robotics
    • 4.5.6.1. Commercial Weeding Robots
    • 4.5.6.2. "Green-on-Green" vs. "Green-on-Brown" Technology
    • 4.5.6.3. Precision Spraying Technologies
  • 4.5.7. Agricultural Drones
    • 4.5.7.1. Application Pipeline
    • 4.5.7.2. Imaging Applications
    • 4.5.7.3. Spraying Applications
    • 4.5.7.4. Regulatory Approvals by Region
  • 4.5.8. Dairy Farming Robots
    • 4.5.8.1. Milking Robots
    • 4.5.8.2. Feed Pushers
    • 4.5.8.3. Market Adoption Trends
• 4.6. Construction Robotics
  • 4.6.1. 3D Printing Construction Robots
    • 4.6.1.1. Technologies
    • 4.6.1.2. Applications
  • 4.6.2. Demolition Robots
    • 4.6.2.1. Technologies
    • 4.6.2.2. Applications
  • 4.6.3. Bricklaying and Masonry Robots
    • 4.6.3.1. Technologies
    • 4.6.3.2. Applications

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
      • 5.2.4.2.2. Applications in Robotics
    • 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. Manufacturing
  • 6.1.1. Automotive
    • 6.1.1.1. Opportunities and Challenges
    • 6.1.1.2. Applications
  • 6.1.2. Electronics
    • 6.1.2.1. 3C Manufacturing Challenges
    • 6.1.2.2. Production Volume Requirements
    • 6.1.2.3. Quality Control
    • 6.1.2.4. Applications
    • 6.1.2.5. Testing and Inspection
    • 6.1.2.6. Packaging
  • 6.1.3. Food and Beverage
    • 6.1.3.1. Industry Challenges and Requirements
    • 6.1.3.2. Product Variety
  • 6.1.4. Applications
    • 6.1.4.1. Palletizing
    • 6.1.4.2. Packaging
    • 6.1.4.3. Food Processing
  • 6.1.5. Pharmaceutical
    • 6.1.5.1. Industry Requirements
    • 6.1.5.2. Applications
• 6.2. Healthcare
  • 6.2.1. Challenges in Healthcare Industry
  • 6.2.2. Applications
    • 6.2.2.1. Surgical Assistance
    • 6.2.2.2. Rehabilitation
    • 6.2.2.3. Laboratory Automation
    • 6.2.2.4. Medication Management
  • 6.2.3. Market Drivers
  • 6.2.4. Technology Readiness Level
• 6.3. Logistics and Warehousing
  • 6.3.1. Applications
    • 6.3.1.1. Material Transport
    • 6.3.1.2. Order Picking
    • 6.3.1.3. Inventory Management
    • 6.3.1.4. Palletizing and Depalletizing
  • 6.3.2. Market Drivers
  • 6.3.3. Technology Readiness Level
  • 6.3.4. Last Mile Delivery Solutions
    • 6.3.4.1. Ground-Based Delivery Vehicles
    • 6.3.4.2. Delivery Drones
• 6.4. Agriculture
  • 6.4.1. Market Drivers
  • 6.4.2. Applications
  • 6.4.3. Technology Readiness Level
  • 6.4.4. Emerging Technologies
  • 6.4.5. Sensors in Agricultural Robots
    • 6.4.5.1. Imaging Sensors Comparison
    • 6.4.5.2. Navigation Sensors
    • 6.4.5.3. Environmental Sensors
• 6.5. Construction
  • 6.5.1. Market Drivers
  • 6.5.2. Applications
  • 6.5.3. Technology Readiness Level
• 6.6. Retail and Consumer
  • 6.6.1. Customer Service and Hospitality
    • 6.6.1.1. Front-of-House Applications
    • 6.6.1.2. Back-of-House Applications
  • 6.6.2. Market Drivers
  • 6.6.3. Applications
  • 6.6.4. Technology Readiness Level
• 6.7. Military and Defense
  • 6.7.1. Market Drivers
  • 6.7.2. Applications
  • 6.7.3. Technology Readiness Level
• 6.8. Energy and Utilities
  • 6.8.1. Li-ion Battery Industry
    • 6.8.1.1. Benefits of Robotics in Li-ion Manufacturing
    • 6.8.1.2. Use Cases
      • 6.8.1.2.1. Battery Module Inspection
      • 6.8.1.2.2. Battery Assembly
      • 6.8.1.2.3. End-of-Life Recycling
  • 6.8.2. Photovoltaic Industry
    • 6.8.2.1. Overview and Use Cases
      • 6.8.2.1.1. Robotic Assembly of PV Arrays
      • 6.8.2.1.2. Welding Applications
      • 6.8.2.1.3. Inspection Systems
    • 6.8.2.2. Barriers and Solutions
  • 6.8.3. Semiconductor Industry
    • 6.8.3.1. Emerging Applications
      • 6.8.3.1.1. Photomask Processing
      • 6.8.3.1.2. Wafer Handling
    • 6.8.3.2. Technical Requirements and Barriers
• 6.9. Mining and Resources
  • 6.9.1. Market Drivers
  • 6.9.2. Applications
  • 6.9.3. Technology Readiness Level
• 6.10. Education and Research
  • 6.10.1. Market Drivers
  • 6.10.2. Applications
  • 6.10.3. Technology Readiness Level
• 6.11. Entertainment and Leisure
  • 6.11.1. Market Drivers
  • 6.11.2. Applications
  • 6.11.3. Technology Readiness Level
• 6.12. Personal Use and Domestic Settings
  • 6.12.1. Market Drivers
  • 6.12.2. Applications
  • 6.12.3. Technology Readiness Level
  • 6.12.4. Cleaning and Disinfection Robots
    • 6.12.4.1. Floor Cleaning Robots
    • 6.12.4.2. Window and Wall Cleaning Robots
    • 6.12.4.3. UV-based Disinfection Robots

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
  • 8.4.3. 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 (2025-2045)
  • 10.1.1. Short-term Developments (2025-2030)
  • 10.1.3. Long-term Developments (2035-2045)
• 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 (127 company profiles)

12. REFERENCES