자율 물류 시장 : 이동 로봇, 배송 로봇, 드론, 무인 트럭(2027-2040년)

Autonomous logistics is fast becoming one of the most important layers of the global supply chain. Warehouses, factories, distribution centres, parcel networks, food-delivery platforms and freight carriers are under sustained pressure to move goods faster and more reliably while facing labour shortages, rising wages, volatile demand and growing fulfilment complexity. Mobile robots, autonomous delivery vehicles, drones and driverless trucks answer that pressure with automation that moves. Capacity can be added in increments, layouts can be changed in weeks rather than months, and many systems can be paid for as a service rather than bought outright.

The market spans every link in the logistics chain. Inside the warehouse, automated guided vehicles (AGVs), grid-based goods-to-person robots, autonomous mobile robots (AMRs) and autonomous forklifts transport goods. They are joined by case-handling robots, mobile manipulators, truck-unloading robots and inventory-scanning robots and drones. Beyond the warehouse, China’s unmanned delivery vehicles have reached fleets of tens of thousands, and sidewalk robots deliver food and groceries through platform partnerships. Delivery drones have passed millions of commercial deliveries. Level 4 autonomous trucks now haul freight without a driver on selected US highway routes.

These technologies sit on very different maturity curves. AGVs and AMRs ship in hundreds of thousands of units a year. Mobile manipulation, robovans outside China and driverless trucking are moving from early commercial deployment to fleet scale. Indoors, technology integration and return on investment are the main challenges; outdoors, regulation is the gating factor. City road rights govern delivery vehicles, beyond-visual-line-of-sight rules govern drones, and federal and EU frameworks govern autonomous trucks. China leads in unit volume and cost, with domestic manufacturers setting global price levels in AGVs, AMRs, autonomous forklifts and robovans. North America leads in driverless trucking, sidewalk robots, truck unloading and consumer drone delivery, and Europe is strong in intralogistics systems and safety certification. Falling costs for lidar, cameras, edge compute and batteries are widening the range of tasks that can be automated economically. So are advances in AI navigation and robot foundation models.

The Global Autonomous Logistics Market 2027–2040: Mobile Robots, Delivery Robots, Drones and Driverless Trucks report provides a comprehensive analysis of the mobile robots, autonomous vehicles and drones transforming logistics, warehousing and delivery. It covers intralogistics AGVs, AGCs, grid-based goods-to-person robots, AMRs and autonomous forklifts. It also covers case-handling robots, mobile manipulators, truck unloaders and inventory robots; robovans, autonomous delivery vehicles and sidewalk robots; delivery and cargo drones; and Level 4 autonomous trucks, middle-mile and yard automation. The report combines technology benchmarking, company and deployment tracking, regulatory analysis and bottom-up forecasts. It includes 257 tables, 103 figures and 205 company profiles, with data current to October 2026.

Report contents include:

• Market definition and taxonomy: twelve robot and vehicle categories, mapped across the logistics value chain from first mile to last metre, with autonomy levels and commercial readiness.
• Market drivers and economics: labour shortages, e-commerce growth, ROI and payback by robot type, cost per pick and per delivery, leasing and Robotics-as-a-Service, investment, funding and M&A from 2024 to 2026.
• Enabling technologies: navigation and SLAM, lidar, cameras, radar and IMUs, AI and foundation models, compute, functional safety, fleet orchestration and interoperability (VDA 5050), connectivity and teleoperation, batteries and charging.
• Intralogistics transport robots: product benchmarking by payload, speed and navigation, comparison with fixed automation, applications, and the competitive landscape.
• Mobile picking and handling: case-handling robots, mobile manipulators, truck unloading, humanoids in logistics, and inventory robots and drones.
• Ground last-mile delivery: China’s robovan boom, sidewalk robots, operating economics, and regulation by country.
• Delivery and cargo drones: aircraft designs, the FAA Part 108 BVLOS rule, regional commercialisation, case studies of Zipline, Wing and Meituan, and unit economics.
• Level 4 autonomous trucks: hub-to-hub and driver-as-a-service models, OEM platforms, deployment status, total cost of ownership analysis and timelines.
• Regulation and standards: ISO 3691-4, R15.08, the EU Machinery Regulation, the Cyber Resilience Act, AV laws, drone rules, and trade and security restrictions.
• Regional and competitive analysis: North America, Europe, China, Japan and Korea and emerging markets; market structure, leading company revenues, IPOs and strategic positioning.
• Market forecasts 2027–2040: revenue, unit sales and average selling prices by category, application group, region, end-use industry and business model, with conservative, base and optimistic scenarios.
• Future outlook: technology roadmaps, convergence trends and strategic recommendations.
• Company profiles: 205 companies across the value chain. Companies profiled include A2Z Drone Delivery, ABB Robotics, Accerion, ACSL, Addverb Technologies, Aeva, Agility Robotics, Agilox, Aitonomi (formerly TeleRetail), Amazon Prime Air, Amazon Robotics, Ambi Robotics, ANRA Technologies, Antwork Technology (Xunyi), Anyware Robotics, Applied Intuition, Apptronik, Arrive AI, Aurora Innovation, AutoStore, Aviant, Avride, Avy, BALYO, Bastian Solutions, BionicHive, BlueBotics, Boston Dynamics, Bot Auto, Brain Corp, Brightpick, Cainiao (Alibaba Group), Caja Robotics, Cartken, Clearpath Robotics / OTTO Motors, Clevon (now indiGO Europe), Coco Robotics, Corvus Robotics, Covariant, CreateAI (formerly TuSimple), Crown Equipment, Daifuku, Daimler Truck, DeepWay, Dematic, DEXA, Dexory, Dexterity, DJI, DoorDash (Dot / DoorDash Labs), Draganfly, Dronamics, DroneUp, DS Automotion, EHang, Einride, ek robotics (NEURA Mobile Robots), Elettric80, Elroy Air, Exotec, Fabric, Fernride, Flytrex, Formant, FORT Robotics, ForwardX Robotics, Fox Robotics, Gather AI, Gatik, Geek+ (Geekplus Technology), Gideon, Grenzebach, GreyOrange, HAI Robotics, Hesai Technology, Hikrobot, Hokuyo Automatic, Honeywell Intelligrated, Hyster-Yale, Hyundai Motor Group, Inceptio Technology, InOrbit, International Motors, inVia Robotics, Iplusmobot, ISEE, Isuzu Motors, JD Logistics, Jedsy, Jungheinrich, Kalmar and more.......

Table of Contents

1. Executive Summary

• 1.1 Key summary
• 1.2 Product taxonomy and scope
• 1.3 Market drivers and adoption barriers
• 1.4 Segment highlights
• 1.5 Regulation and standards: the 2027 market gates
• 1.6 Forecast summary
• 1.7 Competitive landscape and investment
• 1.8 Key findings and strategic implications

2. Introduction and Market Definition

• 2.1 What are logistics mobile robots, autonomous vehicles and drones?
• 2.2 Report scope, segmentation and taxonomy
• 2.3 The logistics value chain and where mobile automation fits
• 2.4 Historical evolution
• 2.5 Levels of autonomy for logistics robots and vehicles
  • 2.5.1 Autonomy framework for intralogistics robots
  • 2.5.2 SAE J3016 levels applied to logistics vehicles
  • 2.5.3 Drone autonomy and BVLOS operating levels
• 2.6 Indoor, outdoor, public-road and airspace operating domains
• 2.7 Commercial readiness by category,
• 2.8 Methodology overview
• 2.9 Glossary of key terms

3. Market Drivers, Economics and Business Models

• 3.1 Structural drivers
  • 3.1.1 Labour shortages and wage inflation in warehousing
  • 3.1.2 Driver shortages and the trucking labour crisis
  • 3.1.3 E-commerce growth and fulfilment complexity
  • 3.1.4 Same-day delivery and the economics of speed
  • 3.1.5 Nearshoring, reshoring and supply-chain resilience
  • 3.1.6 Safety and injury reduction
  • 3.1.7 Sustainability and electrification
  • 3.1.8 AI, software and component cost declines
• 3.2 Restraints and adoption barriers
  • 3.2.1 Capital cost and the warehouse capex cycle
  • 3.2.2 Integration complexity and brownfield sites
  • 3.2.3 Interoperability
  • 3.2.4 Safety certification and machinery regulation
  • 3.2.5 Regulation for public roads and airspace
  • 3.2.6 Public acceptance, labour relations and cybersecurity
  • 3.2.7 ROI uncertainty and vendor failures
• 3.3 Impact assessment of drivers and restraints
• 3.4 Economics: ROI, payback and total cost of ownership
  • 3.4.1 Capex, labour displacement and payback by robot type
  • 3.4.2 Cost per pick in the warehouse
  • 3.4.3 Cost per delivery in the last mile
  • 3.4.4 Total cost of ownership
• 3.5 Business models
  • 3.5.1 Robotics-as-a-Service
  • 3.5.2 Autonomous trucking: from capacity-as-a-service to driver-as-a-service
  • 3.5.3 Drone and robot delivery-as-a-service
  • 3.5.4 The system integrator's role
• 3.6 Go-to-market and channels
• 3.7 Investment, funding and M&A, 2024–2026
• 3.8 Supply chain overview
• 3.9 Key takeaways

4. Enabling Technologies

• 4.1 Technology stack overview
• 4.2 Navigation and guidance technologies
  • 4.2.1 From buried wire to infrastructure-free navigation
  • 4.2.2 Comparison of navigation methods
  • 4.2.3 Outdoor and road navigation: HD maps versus map-light
  • 4.2.4 Navigation used by leading products
• 4.3 Localisation and SLAM in depth
  • 4.3.1 LiDAR SLAM, visual SLAM and fused approaches
  • 4.3.2 Hard cases: dynamic warehouses, long aisles and glass
  • 4.3.3 Navigation software and localisation vendors
• 4.4 Perception sensors
  • 4.4.1 Sensor modalities compared
  • 4.4.2 Safety-rated 2D LiDAR
  • 4.4.3 3D LiDAR: from luxury to commodity
  • 4.4.4 Cameras, depth sensors and the RealSense question
  • 4.4.5 Radar, ultrasonic, IMU and odometry
  • 4.4.6 Sensor suites on representative products
• 4.5 Sensor fusion, AI perception and foundation models
  • 4.5.1 From modular pipelines to learned systems
  • 4.5.2 Foundation models, VLAs and world models
  • 4.5.3 Simulation and digital twins
• 4.6 Compute platforms
• 4.7 Safety systems and functional safety
  • 4.7.1 Indoor vehicles: protective fields and ISO 3691-4
  • 4.7.2 Road vehicles: ISO 26262, SOTIF and UL
  • 4.7.3 Drones: DO-178C, ASTM F38 and detect-and-avoid
• 4.8 Fleet management, orchestration and interoperability
  • 4.8.1 Fleet managers and traffic control
  • 4.8.2 Interoperability standards
  • 4.8.3 WMS/WES integration and multi-vendor orchestration
• 4.9 Connectivity and remote operations
  • 4.9.1 Wi-Fi and private 5G in facilities
  • 4.9.2 Cellular, V2X and satellite for vehicles and drones
  • 4.9.3 Teleoperation and remote assistance
• 4.10 Batteries
  • 4.10.1 Chemistries by robot class
  • 4.10.2 Battery sizing by robot type
• 4.11 Charging and energy management
  • 4.11.1 Charging strategies
  • 4.11.2 Contact, wireless and swap technologies
• 4.12 Drives, motors and chassis
  • 4.12.1 Ground robot drive configurations
  • 4.12.2 Drone propulsion, VTOL configurations and payload release
• 4.13 Technology benchmarking
• 4.14 Technology roadmap 2027–2040

5. Intralogistics Transport Robots

• 5.1 Overview: Types of Mobile Robots in Material Handling
• 5.2 Automated Guided Vehicles and Carts (AGV/AGC)
  • 5.2.1 Origins and definition
  • 5.2.2 Types of AGV
  • 5.2.3 Heavy-load and port AGVs
  • 5.2.4 AGV navigation methods
  • 5.2.5 Strengths, limitations and use in automotive and manufacturing
• 5.3 Grid-Based AGCs and Goods-to-Person Robots
  • 5.3.1 From Kiva to the mass-market shelf-lifting robot
  • 5.3.2 Amazon Robotics: the world's largest mobile robot fleet
  • 5.3.3 Geek+, Hikrobot, Quicktron and the Chinese shelf-to-person ecosystem
  • 5.3.4 GreyOrange, Swisslog CarryPick and Western grid-based systems
  • 5.3.5 Cube storage robots: AutoStore and Ocado
• 5.4 Autonomous Mobile Robots
  • 5.4.1 What makes a robot an AMR
  • 5.4.2 Collaborative picking AMRs
  • 5.4.3 Cart-transport and tugger AMRs
  • 5.4.4 Top modules and conveyor-top AMRs
  • 5.4.5 Sortation robots
  • 5.4.6 Tote-to-person and hospital AMRs
• 5.5 Autonomous Mobile Vehicles: Autonomous Forklifts, Pallet Movers and Tuggers
  • 5.5.1 Why the forklift is the next frontier
  • 5.5.2 Pure-play autonomous forklift developers
  • 5.5.3 Forklift OEMs and their automation strategies
  • 5.5.4 Heavy-duty AMRs and tow tractors
• 5.6 Technology Comparison: AGV/AGC, AMR, Grid-Based AGC and Fixed Automation
  • 5.6.1 AGV/AGC versus AMR
  • 5.6.2 Mobile robots versus fixed automation
  • 5.6.3 Transition of navigation technology
• 5.7 Pain Points and Engineering Challenges
  • 5.7.1 Why deployments fail
  • 5.7.2 Gap, slope and floor-condition detection
  • 5.7.3 Mixed traffic, safety zones and standards
  • 5.7.4 Connectivity, batteries and fleet scaling
  • 5.7.5 Uptime, service and the RaaS model
• 5.8 Application Analysis
  • 5.8.1 Overview by end market
  • 5.8.2 E-commerce, retail and grocery
  • 5.8.3 Third-party logistics
  • 5.8.4 Manufacturing, electronics and new energy
  • 5.8.5 Healthcare and pharma
• 5.9 Product Benchmarking
• 5.10 Competitive Landscape
  • 5.10.1 Regional overview
  • 5.10.2 Leading companies by segment
  • 5.10.3 Revenue and scale of leading listed and filing companies
  • 5.10.4 Installed base and market share indications
  • 5.10.5 Strategic partnerships with forklift OEMs and navigation suppliers
  • 5.10.6 Mergers and acquisitions
• 5.11 Other Goods-to-Person Approaches
• 5.12 Outlook and Key Takeaways

6. Mobile Picking, Manipulation and Inventory Robots

• 6.1 Forms of Mobile Picking, Manipulation and Inventory Robots
• 6.2 Autonomous Case-Handling Robots (ACRs)
  • 6.2.1 Concept and architecture
  • 6.2.2 HAI Robotics
  • 6.2.3 Other ACR and tote-handling vendors
  • 6.2.4 Exotec Skypod
  • 6.2.5 ACRs versus grid AGCs and shuttle systems
  • 6.2.6 Navigation technologies for ACRs
• 6.3 Mobile Manipulators for Piece Picking and Machine Tending
  • 6.3.1 From research platform to product
  • 6.3.2 Autonomous in-aisle piece picking
  • 6.3.3 Industrial mobile manipulators and machine tending
  • 6.3.4 Evolution of picking algorithms
  • 6.3.5 Published picking performance
• 6.4 Truck and Container Unloading and Loading Robots
  • 6.4.1 Why the dock is the hardest problem
  • 6.4.2 Leading systems
• 6.5 Humanoid and Legged Robots in Logistics
  • 6.5.1 Commercial status in October
  • 6.5.2 Realistic assessment of readiness
• 6.6 Inventory Robots and Drones
  • 6.6.1 The case for automated inventory
  • 6.6.2 Leading vendors
• 6.7 Hybrid Systems and Integration
  • 6.7.1 Orchestrating mixed fleets
  • 6.7.2 Case studies
• 6.8 Economics of Mobile Picking and Handling Robots
  • 6.8.1 Throughput comparisons
  • 6.8.2 Cost per pick and payback
• 6.9 Players
  • 6.9.1 Case-handling and goods-to-person robot companies
  • 6.9.2 Mobile manipulator, unloading and humanoid companies
• 6.10 Outlook, Challenges and Key Takeaways
  • 6.10.1 Technology positioning
  • 6.10.2 Challenges

7. Autonomous Ground Last-Mile Delivery

• 7.1 Last-mile delivery: definition, cost and volumes
  • 7.1.1 What is last-mile delivery?
  • 7.1.2 Why the last mile is the most expensive part of the chain
  • 7.1.3 Why autonomous last mile?
  • 7.1.4 Parcel and food delivery volumes
• 7.2 Delivery form factors
  • 7.2.1 Overview
  • 7.2.2 Sidewalk robots
  • 7.2.3 Road-going low-speed delivery vehicles and robovans
  • 7.2.4 Full-size autonomous delivery vans and pods
  • 7.2.5 "Last metre" doorstep solutions
  • 7.2.6 Ground versus air: comparison with delivery drones
• 7.3 Supporting infrastructure
  • 7.3.1 Micro-fulfilment centres, dark stores and decentralised warehousing
  • 7.3.2 Hubs, mobile lockers, charging and maintenance
  • 7.3.3 Teleoperation and remote operations centres
• 7.4 Technologies for ground delivery vehicles
  • 7.4.1 Sensors
  • 7.4.2 Localisation, mapping and autonomy software
  • 7.4.3 Connectivity, teleoperation and remote-operator ratios
  • 7.4.4 Vehicle platforms, batteries and cost reduction
• 7.5 China's robovan boom
  • 7.5.1 From pilot to industry
  • 7.5.2 Neolix
  • 7.5.3 Zelos
  • 7.5.4 Platform operators and other Chinese players
  • 7.5.5 Courier adoption, unit prices and economics
  • 7.5.6 City road rights and operating data
• 7.6 United States and Europe: road-going delivery vehicles
  • 7.6.1 Nuro: from delivery pods to autonomy licensing
  • 7.6.2 Middle-mile and specialist road delivery players
  • 7.6.3 DoorDash Dot and the bike-lane robot
• 7.7 Sidewalk delivery robots
  • 7.7.1 Starship Technologies
  • 7.7.2 Serve Robotics
  • 7.7.3 Coco, Avride, Robot.com and other US operators
  • 7.7.4 Asia: Korea, Japan and Russia
  • 7.7.5 Product benchmarking
  • 7.7.6 Deployment status and funding
• 7.8 Operating economics
  • 7.8.1 The cost structure of a delivery robot
  • 7.8.2 Utilisation, deliveries per day and supervision in practice
  • 7.8.3 Comparative cost per delivery
• 7.9 Regulations for ground delivery robots and vehicles
  • 7.9.1 United States: personal delivery device laws
  • 7.9.2 China, Japan and South Korea
  • 7.9.3 United Kingdom and Europe
• 7.10 Players
  • 7.10.1 Regional distribution and what players deliver
  • 7.10.2 Company overview table
• 7.11 Challenges and key takeaways
  • 7.11.1 Challenges
  • 7.11.2 Key takeaways

8. Delivery and Cargo Drones

• 8.1 Drone Logistics Overview
  • 8.1.1 Use Cases
  • 8.1.2 Deliveries to Date
  • 8.1.3 Last-Mile, Mid-Mile and Long-Haul
• 8.2 Drone Designs
  • 8.2.1 Multicopters
  • 8.2.2 Fixed-Wing Aircraft
  • 8.2.3 Hybrid VTOL: Lift+Cruise, Tilt-Rotor and Tail-Sitter
  • 8.2.4 Payload Delivery Mechanisms
  • 8.2.5 Payload and Range of Commercial Models
• 8.3 Technologies
  • 8.3.1 Sensors and Detect-and-Avoid
  • 8.3.2 Navigation and Autonomy
  • 8.3.3 Operating Restrictions: Weather, Noise and Endurance
  • 8.3.4 UTM, U-Space and Strategic Deconfliction
  • 8.3.5 Docking, Nests and Ground Infrastructure
  • 8.3.6 Fleet Operations and Remote-Pilot Ratios
• 8.4 Regulation
  • 8.4.1 United States
  • 8.4.2 European Union
  • 8.4.3 United Kingdom
  • 8.4.4 China
  • 8.4.5 Japan, South Korea and India
  • 8.4.6 Australia, Africa and the Middle East
  • 8.4.7 Comparative Summary
• 8.5 Commercialisation Status by Region
  • 8.5.1 United States
  • 8.5.2 Europe and the United Kingdom
  • 8.5.3 China
  • 8.5.4 Japan and South Korea
  • 8.5.5 Africa
  • 8.5.6 India, the Middle East and Latin America
• 8.6 Landscape and Maturity Assessment
  • 8.6.1 Maturity Matrix
• 8.7 Case Studies
  • 8.7.1 Wing versus Zipline: Competing Approaches to US Home Delivery
  • 8.7.2 China's Drone Logistics Race: Meituan, SF Express, JD and EHang
  • 8.7.3 Amazon Prime Air
  • 8.7.4 Manna in Ireland and Finland
  • 8.7.5 Matternet and Healthcare Delivery
  • 8.7.6 Mid-Mile and Long-Haul Cargo
• 8.8 Economics
  • 8.8.1 Cost per Delivery
  • 8.8.2 Fleet Utilisation and Site Throughput
  • 8.8.3 Pricing Models and Infrastructure Costs
• 8.9 Funding and Partnerships
• 8.10 Barriers and Key Takeaways

9. Level 4 Autonomous Trucks, Middle-Mile and Yard Automation

• 9.1 The case for automating heavy-duty trucks
  • 9.1.1 Pain points in road freight
  • 9.1.2 Freight market context by region
  • 9.1.3 Why trucks first
• 9.2 Levels of automation and operating models
  • 9.2.1 SAE levels applied to trucking
  • 9.2.2 Level 2 versus Level 4 trucking
  • 9.2.3 Operating models
  • 9.2.4 Hub-to-hub segment economics
• 9.3 Business models
  • 9.3.1 From technology developer to freight provider
  • 9.3.2 Driver-as-a-Service: the emerging default
  • 9.3.3 OEM strategies
  • 9.3.4 Business models by company
• 9.4 Technology
  • 9.4.1 Sensor suites
  • 9.4.2 Redundant platforms
  • 9.4.3 Simulation, validation and AI architecture
  • 9.4.4 Remote assistance and operations
• 9.5 Regulation
  • 9.5.1 United States: federal framework
  • 9.5.2 United States: state laws
  • 9.5.3 China
  • 9.5.4 Europe
  • 9.5.5 Japan, Korea and others
• 9.6 Players and market maturity
  • 9.6.1 Industry structure after the shake-out
  • 9.6.2 Market readiness of leading companies
  • 9.6.3 Profiles of the leaders
  • 9.6.4 Funding
  • 9.6.5 Company locations
  • 9.6.6 Deployment timeline
• 9.7 Deployment status, routes and safety supervision
  • 9.7.1 United States
  • 9.7.2 China and rest of world
• 9.8 Total cost of ownership analysis
  • 9.8.1 Approach and assumptions
  • 9.8.2 Results
  • 9.8.3 Sensitivity
• 9.9 Drivers, hurdles and competitive position
  • 9.9.1 Six key drivers
  • 9.9.2 Remaining hurdles
  • 9.9.3 SWOT analysis
  • 9.9.4 Commercial readiness compared with other autonomous vehicles
• 9.10 Yard trucks, ports and closed-site logistics
  • 9.10.1 Distribution and logistics yards
  • 9.10.2 Ports and container terminals
  • 9.10.3 Mining and industrial haulage
• 9.11 Timelines and outlook
  • 9.11.1 Our view of milestones, 2027–2040
  • 9.11.2 Ones to watch
  • 9.11.3 Conclusion

10. Regulation, Safety Standards and Policy

• 10.1 Overview: four regulatory domains and the 2027 market gates
  • 10.1.1 The four regulatory domains
  • 10.1.2 The 2027 market gates
  • 10.1.3 The EU Machinery Regulation and AMRs
  • 10.1.4 UK equivalents
• 10.2 Industrial mobile robot safety standards
  • 10.2.1 ISO 3691-4 and ANSI/ITSDF B56.5
  • 10.2.2 ANSI/A3 R15.08 series
  • 10.2.3 ISO 10218:2025 and collaborative operation
  • 10.2.4 Functional safety, electrical and product standards
• 10.3 Interoperability standards
• 10.4 Ground delivery robots
  • 10.4.1 United States: PDD statutes
  • 10.4.2 China
  • 10.4.3 Japan and South Korea
  • 10.4.4 Europe and the UK
• 10.5 Road autonomous vehicles and trucks
  • 10.5.1 United States: federal
  • 10.5.2 United States: states
  • 10.5.3 China
  • 10.5.4 Europe, the UK, Japan and Korea
• 10.6 Drones and airspace
  • 10.6.1 United States
  • 10.6.2 Europe and the UK
  • 10.6.3 China, Japan, India and Australia
• 10.7 Cybersecurity, data protection and AI governance
  • 10.7.1 Product and vehicle cybersecurity
  • 10.7.2 Data protection
  • 10.7.3 AI governance
  • 10.7.4 National-security restrictions on Chinese technology
• 10.8 Trade, tariffs and geopolitics
• 10.9 Insurance and liability frameworks
• 10.10 Regulatory readiness matrix and outlook to
  • 10.10.1 Outlook to

11. Regional Market Analysis

• 11.1 Regional Overview: How Geography Shapes Adoption
  • 11.1.1 Four forces that explain regional differences
  • 11.1.2 Automation indicators
• 11.2 North America
  • 11.2.1 United States
  • 11.2.2 Canada
• 11.3 Europe
  • 11.3.1 Regional characteristics and policy framework
  • 11.3.2 Germany
  • 11.3.3 United Kingdom
  • 11.3.4 France, the Nordics, the Netherlands and the rest of Europe
• 11.4 China
  • 11.4.1 Market characteristics
  • 11.4.2 Policy support
  • 11.4.3 Adoption by segment
• 11.5 Japan
  • 11.5.1 The "2024 problem" and the logistics labour crisis
  • 11.5.2 Adoption by segment and leading players
• 11.6 South Korea
• 11.7 India and Southeast Asia
  • 11.7.1 India
  • 11.7.2 Southeast Asia
• 11.8 Middle East
• 11.9 Latin America
• 11.10 Africa
• 11.11 Comparative Assessment
  • 11.11.1 Government programmes
  • 11.11.2 Leading players by region
  • 11.11.3 Regional maturity by segment
  • 11.11.4 Labour context across regions

12. Competitive Landscape, Strategy and Investment

• 12.1 Market Structure by Segment
  • 12.1.1 A fragmented market with concentrated pockets
  • 12.1.2 In-house versus merchant supply
• 12.2 Strategic Groupings
  • 12.2.1 Competitive positioning
  • 12.2.2 Competitive dynamics within the principal segments
• 12.3 Financial Benchmarking of Leading Listed Companies
  • 12.3.1 Revenues and profitability
  • 12.3.2 What the numbers show
• 12.4 Mergers and Acquisitions, 2020–2026
  • 12.4.1 Patterns in deal activity
• 12.5 Investment and Funding
  • 12.5.1 Funding cycle, 2021–2026
  • 12.5.2 IPO pipeline
• 12.6 Strategic Partnerships
• 12.7 Competitive Strategies
  • 12.7.1 Vertical integration
  • 12.7.2 Robots-as-a-service and outcome-based pricing
  • 12.7.3 Ecosystems, interoperability and software platforms
  • 12.7.4 Price competition from China
  • 12.7.5 Summary of strategic archetypes
• 12.8 Ones to Watch

13. Market Forecasts 2027–2040

• 13.1 Forecast scope, definitions and methodology
• 13.2 Global market revenue and unit sales, 2025–2040
• 13.3 Scenario analysis
• 13.4 Forecast by application group
• 13.5 Forecast by robot and vehicle category
• 13.6 Intralogistics transport robots
• 13.7 Mobile picking and handling robots
• 13.8 Last-mile delivery robots, vehicles and drones
• 13.9 Level 4 autonomous trucks and middle-mile freight
• 13.10 Forecast by region
• 13.11 Intralogistics and picking robots by end-use industry
• 13.12 Business model mix: equipment sales, leasing and Robotics-as-a-Service
• 13.13 Captive deployments, sensitivities and forecast risks

14. Future Outlook and Technology Roadmap

• 14.1 Outlook: From Islands of Automation to Autonomous Logistics Networks
• 14.2 Technology Roadmap by Segment
  • 14.2.1 Roadmap overview
  • 14.2.2 Key milestones
  • 14.2.3 Cost curves and the economics of scale
• 14.3 Convergence Themes
  • 14.3.1 Humanoids and AMRs
  • 14.3.2 Robovans, drones and the "mothership" model
  • 14.3.3 Autonomous trucks and automated hubs
  • 14.3.4 Lights-out warehouses
• 14.4 Impact on Labour and Jobs
  • 14.4.1 Scale of exposure
  • 14.4.2 Displacement, augmentation and new roles
  • 14.4.3 Policy and social licence
• 14.5 Scenarios to
• 14.6 Opportunities for Suppliers
  • 14.6.1 Enabling technologies
• 14.7 Strategic Recommendations
  • 14.7.1 For technology vendors
  • 14.7.2 For investors
  • 14.7.3 For logistics operators and shippers

15. Company Profiles (205 company profiles)

16. Appendix: Research Methodology

• 16.1 Research approach
• 16.2 Market definition and segmentation
• 16.3 Forecast methodology
• 16.4 Limitations