The Global Autonomous Logistics Market 2027-2040: Mobile Robots, Delivery Robots, Drones and Driverless Trucks

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

List of Tables

• Table 1. Headline market metrics, 2026–2040 (base case)
• Table 2. Summary of categories: environment, maturity and leading companies (2026)
• Table 3. Major market drivers and restraints
• Table 4. Revenue forecast by category, 2026–2040 (US$ million, base case)
• Table 5. Unit sales forecast by category, 2026–2040 (units, base case)
• Table 6. Fixed automation versus mobile automation in logistics
• Table 7. Product categories in scope - definitions, payloads, environments and leading examples
• Table 8. Mapping mobile robot and autonomous vehicle categories to logistics value-chain stages
• Table 9. Selected historical milestones by segment
• Table 10. Autonomy framework for intralogistics mobile robots
• Table 11. SAE J3016 driving automation levels and their relevance to logistics vehicles
• Table 12. Operational autonomy levels for delivery and cargo drones
• Table 13. Comparison of operating domains for logistics mobile robots, AVs and drones
• Table 14. Technology and commercial readiness by category, 2026
• Table 15. Key definitions and conventions used in this report
• Table 16. Glossary of key terms
• Table 17. Summary of structural market drivers
• Table 18. Labour-market indicators for warehousing and freight transport
• Table 19. Summary of market restraints and adoption barriers
• Table 20. Selected vendor failures, exits and distressed transactions, 2023–2026
• Table 21. Impact assessment of drivers and restraints over time (–3 strong negative to +3 strong positive)
• Table 22. Indicative economics and payback by robot and vehicle type, 2026 (US/Western Europe)
• Table 23. Productivity and estimated cost per order line by fulfilment method (US, 2026)
• Table 24. Total cost of ownership components for mobile robot and autonomous vehicle deployments
• Table 25. Comparison of business models in logistics mobile automation
• Table 26. Indicative pricing structures by segment, 2026
• Table 27. Go-to-market channels and representative partnerships
• Table 28. Selected major funding rounds and listings, 2024–2026
• Table 29. Selected mergers, acquisitions and strategic transactions, 2023–2026
• Table 30. Mobile robot component supply chain
• Table 31. Where engineering effort concentrates in the stack, by robot category
• Table 32. Comparison of navigation and guidance technologies
• Table 33. Map strategies for outdoor and on-road logistics autonomy
• Table 34. Navigation approaches used by representative logistics robots and vehicles
• Table 35. Localisation challenges in logistics environments and common mitigations
• Table 36. Selected navigation and localisation software vendors for logistics vehicles
• Table 37. Perception sensor modalities for logistics robots and vehicles
• Table 38. Representative safety-rated 2D LiDAR scanners used on AGVs and AMRs
• Table 39. Representative 3D LiDAR and depth sensors used in logistics robots and vehicles
• Table 40. Sensor suites on representative logistics robots, vehicles and drones
• Table 41. AI models and simulation platforms relevant to logistics robotics
• Table 42. Compute platforms for logistics robots, autonomous vehicles and drones
• Table 43. Safety standards and frameworks by robot category
• Table 44. Interoperability standards and frameworks for multi-vendor robot fleets
• Table 45. Fleet management and multi-vendor orchestration platforms
• Table 46. Indicative network requirements for remote operations of logistics robots and vehicles
• Table 47. Battery chemistries for logistics robots, vehicles and drones
• Table 48. Typical battery sizing by logistics robot and vehicle type
• Table 49. Charging and energy-replenishment approaches for logistics robots and vehicles
• Table 50. Ground robot drive and chassis configurations
• Table 51. Delivery drone propulsion configurations and payload-release methods
• Table 52. Technology roadmap for logistics mobile robots, autonomous vehicles and drones, 2027–2040
• Table 53. Working definitions of intralogistics transport robot categories
• Table 54. Main types of automated guided vehicle
• Table 55. Representative AGV-based automated container terminals
• Table 56. AGV guidance and navigation methods compared
• Table 57. AGV and AMR use cases in automotive and battery manufacturing
• Table 58. Leading grid-based AGC / shelf-to-person robot product families
• Table 59. Amazon Robotics fleet: principal mobile and handling robots
• Table 60. Collaborative picking and picking-assist AMR platforms
• Table 61. Selected cart-transport, tugger and top-module AMR platforms
• Table 62. Robotic sortation systems compared with fixed sortation
• Table 63. Forklift OEM automation portfolios
• Table 64. Representative autonomous forklift, pallet-mover and tugger products
• Table 65. AGV/AGC versus AMR: comparison of key attributes
• Table 66. Mobile robots versus fixed automation: deployment, cost and performance characteristics
• Table 67. Generations of mobile robot navigation technology
• Table 68. Pain points of intralogistics mobile robots and engineering responses
• Table 69. Key safety standards for intralogistics mobile robots
• Table 70. Applications of intralogistics transport robots by end market
• Table 71. Benchmark of selected intralogistics transport robots
• Table 72. Selected intralogistics transport robot companies by region and category
• Table 73. Leading AGV suppliers and their positioning
• Table 74. Leading grid-based AGC and cube-storage robot suppliers
• Table 75. Leading AMR suppliers and positioning
• Table 76. Leading autonomous forklift and AMV developers
• Table 77. Reported revenues of selected listed and IPO-filing intralogistics robot companies
• Table 78. Strategic partnerships between autonomy developers and forklift OEMs / logistics users
• Table 79. Selected mergers and acquisitions in intralogistics mobile robotics
• Table 80. Alternative goods-to-person architectures and their status
• Table 81. Classes of mobile picking, manipulation and inventory robots
• Table 82. HAI Robotics HaiPick product family (principal configurations)
• Table 83. Selected case-handling and tote-retrieval robot products
• Table 84. Comparison of goods-to-person storage technologies (typical ranges)
• Table 85. Navigation and positioning technologies used by case-handling robots
• Table 86. Representative mobile manipulator products for logistics and industrial use
• Table 87. Generations of robotic grasping and picking algorithms
• Table 88. Robot foundation-model and AI-manipulation companies relevant to logistics
• Table 89. Publicly stated performance indicators for picking and handling robots
• Table 90. Truck and container unloading and loading robots
• Table 91. Humanoid robots in logistics: programmes and deployment status (October 2026)
• Table 92. Humanoids versus purpose-built robots for warehouse tote handling
• Table 93. Inventory robot and drone vendors
• Table 94. Comparison of automated inventory approaches
• Table 95. Selected deployments of case-handling, picking and unloading robots
• Table 96. Indicative fully loaded cost per pick by configuration (US/Western Europe, 2026)
• Table 97. Payback drivers for mobile picking, unloading and inventory robots
• Table 98. Case-handling robot and mobile goods-to-person companies
• Table 99. Mobile manipulator, truck-unloading and logistics humanoid companies
• Table 100. Safety standards relevant to mobile manipulators, case-handling robots and humanoids
• Table 101. Key challenges by category
• Table 102. Indicative cost structure of conventional last-mile delivery (2025–2026)
• Table 103. Main last-mile volume pools relevant to autonomous ground delivery
• Table 104. Autonomous ground delivery form factors - typical specifications (2026)
• Table 105. "Last metre" delivery approaches
• Table 106. Ground delivery systems versus delivery drones - performance comparison
• Table 107. Ground delivery systems versus delivery drones - regulatory and commercial comparison
• Table 108. Supporting infrastructure requirements by form factor
• Table 109. Sensor suites of representative autonomous ground delivery platforms
• Table 110. Localisation and autonomy approaches in ground delivery
• Table 111. Remote supervision models in autonomous ground delivery (indicative)
• Table 112. Major Chinese unmanned delivery vehicle companies (October 2026)
• Table 113. Representative Chinese robovan products and prices
• Table 114. Robovan adoption by Chinese express and logistics companies (2025–2026)
• Table 115. Selected Chinese city regimes for unmanned delivery vehicles (2025–2026)
• Table 116. US and European road-going autonomous delivery players (October 2026)
• Table 117. Benchmarking of leading sidewalk and small road delivery robots (2026)
• Table 118. Sidewalk delivery robot deployment status (October 2026)
• Table 119. Major funding events in autonomous ground delivery, 2025–2026
• Table 120. Illustrative annual cost model for a sidewalk delivery robot (US$, Future Markets estimates)
• Table 121. Key operating metrics by delivery model (2025–2026, indicative)
• Table 122. Typical provisions of US state personal delivery device (PDD) laws
• Table 123. Regulatory status of ground delivery robots and vehicles by country (October 2026)
• Table 124. What autonomous ground delivery players deliver today (2026)
• Table 125. Autonomous ground last-mile delivery companies (October 2026)
• Table 126. Key challenges for autonomous ground delivery and mitigation approaches
• Table 127. Principal use cases for delivery and cargo drones
• Table 128. Cumulative drone deliveries reported by leading operators (latest disclosure)
• Table 129. Comparison of last-mile, mid-mile and long-haul drone logistics
• Table 130. Drone logistics operating scenarios
• Table 131. Comparison of delivery drone configurations
• Table 132. Payload delivery mechanisms
• Table 133. Specifications of selected delivery and cargo drones
• Table 134. Sensing and detect-and-avoid technologies used in delivery drones
• Table 135. Operating constraints for delivery drones and mitigation approaches
• Table 136. UTM and airspace integration initiatives relevant to drone delivery
• Table 137. Evolution of drone delivery staffing models
• Table 138. US regulatory status of leading drone delivery operators
• Table 139. Key provisions of the proposed FAA Part 108 BVLOS rule
• Table 140. Drone delivery regulatory status by country/region (October 2026)
• Table 141. Principal US drone delivery operators and partners (2026)
• Table 142. Drone delivery activity in Europe and the UK
• Table 143. Leading Chinese drone logistics operators
• Table 144. Drone delivery activity in Asia-Pacific (excluding China)
• Table 145. Zipline and other drone delivery operations in Africa
• Table 146. Drone delivery activity in India, the Middle East and Latin America
• Table 147. Delivery and cargo drone company landscape
• Table 148. Wing and Zipline compared
• Table 149. China's leading drone logistics players compared
• Table 150. Mid-mile and long-haul cargo drone programmes
• Table 151. Estimated cost per last-mile delivery, van versus drone (US$, 2026)
• Table 152. Disclosed utilisation and throughput indicators
• Table 153. Drone delivery pricing models
• Table 154. Indicative infrastructure requirements and costs by model
• Table 155. Selected funding rounds and strategic partnerships in drone delivery
• Table 156. Barriers to drone delivery and their trajectory
• Table 157. Trucking pain points and how Level 4 autonomy addresses them
• Table 158. Road freight context for autonomous trucking by region
• Table 159. SAE levels of driving automation applied to heavy trucks
• Table 160. Indicative segment characteristics of a hub-to-hub autonomous lane
• Table 161. Business model options for autonomous trucking developers
• Table 162. Truck OEM autonomy strategies and partnerships (October 2026)
• Table 163. Business and operating models of leading autonomous freight companies
• Table 164. Sensor suites and compute of leading autonomous truck developers
• Table 165. OEM autonomy-ready redundant truck platforms
• Table 166. Operational infrastructure required for driverless trucking
• Table 167. Key US federal instruments affecting autonomous trucks (October 2026)
• Table 168. State regulatory position for driverless heavy trucks (October 2026)
• Table 169. International regulatory frameworks for L4 autonomous trucks
• Table 170. Notable exits and pivots in autonomous trucking
• Table 171. Market readiness of autonomous truck and freight autonomy companies (October 2026)
• Table 172. Selected autonomous trucking funding and capital-markets events, 2024–2026
• Table 173. Headquarters and operating bases of autonomous freight companies
• Table 174. Autonomous truck deployment status, routes and safety supervision (October 2026)
• Table 175. Principal US autonomous trucking corridors
• Table 176. Selected autonomous truck deployments outside the United States
• Table 177. TCO model assumptions - US Class 8 long-haul tractor
• Table 178. Per-mile TCO comparison - human-driven vs L4 DaaS (US$ per mile)
• Table 179. Sensitivity of early-phase L4 TCO to key variables
• Table 180. Key drivers and hurdles for heavy-duty L4 trucks - impact assessment
• Table 181. SWOT analysis - heavy-duty Level 4 autonomous trucks
• Table 182. Yard, port and closed-site autonomous freight vehicle providers
• Table 183. Future Markets view of autonomous trucking milestones, 2027–2040
• Table 184. Ones to watch in autonomous trucking
• Table 185. The four regulatory domains for logistics mobile robots, autonomous vehicles and drones
• Table 186. Key regulatory "gates", September 2026 – August 2028
• Table 187. EU Machinery Regulation 2023/1230 - key changes for mobile robots versus Directive 2006/42/EC
• Table 188. Safety and product standards for industrial mobile robots
• Table 189. Typical AMR safety functions and target performance levels
• Table 190. Interoperability standards for mobile robot fleets
• Table 191. Representative US state personal delivery device laws
• Table 192. International frameworks for ground delivery robots
• Table 193. United States framework for autonomous delivery vehicles and trucks (October 2026)
• Table 194. Road AV and autonomous truck regulation by country (October 2026)
• Table 195. US regulatory pathways for drone delivery operations
• Table 196. Drone delivery regulation by country (October 2026)
• Table 197. US national-security measures affecting Chinese robotics, AV and drone technology
• Table 198. Cybersecurity, data protection and AI governance instruments
• Table 199. Trade and tariff measures affecting logistics robots, AVs and drones (2018–2026)
• Table 200. Liability and insurance frameworks by segment and jurisdiction
• Table 201. Expected regulatory milestones, 2026–2030
• Table 202. Regional comparison of adoption drivers for logistics mobile robots, autonomous vehicles and drones (2026)
• Table 203. Industrial robot installations and robot density, selected countries (latest IFR data)
• Table 204. Selected notable US deployments of logistics autonomy (to October 2026)
• Table 205. European country profiles for logistics mobile autonomy (2026)
• Table 206. China's principal policy instruments for logistics robots, unmanned vehicles and drones
• Table 207. Leading Chinese players in logistics robots, unmanned vehicles and drones
• Table 208. Japan's responses to the logistics "2024 problem" relevant to autonomy
• Table 209. South Korea: key policies and players in logistics autonomy
• Table 210. India and Southeast Asia: leading players and representative deployments
• Table 211. Representative deployments in the Middle East, Latin America and Africa
• Table 212. Government programmes and regulatory milestones supporting logistics autonomy, by region
• Table 213. Leading players by region and segment (2026)
• Table 214. Labour context and its implications for logistics automation, by region
• Table 215. Competitive structure by segment (October 2026)
• Table 216. Captive, hybrid and merchant supply models in logistics automation
• Table 217. Strategic groups in logistics mobile autonomy
• Table 218. Revenue and profitability of leading listed companies in logistics automation and autonomy (latest fiscal year)
• Table 219. Key mergers, acquisitions and take-privates in logistics mobile autonomy, 2020–2026
• Table 220. Notable failures, shutdowns and restructurings, 2022–2026
• Table 221. Major disclosed funding rounds in logistics mobile autonomy, 2021–2026
• Table 222. IPO and listing pipeline for logistics mobile autonomy companies (October 2026)
• Table 223. Strategic partnerships shaping the logistics autonomy market (selected, to October 2026)
• Table 224. Chinese versus Western vendors: competitive positioning in warehouse robotics
• Table 225. Competitive strategy archetypes in logistics mobile autonomy
• Table 226. Ones to watch, 2027–2028
• Table 227. Base-year assumptions and key forecast drivers by category
• Table 228. Global market revenue and unit sales for logistics mobile robots, autonomous vehicles and drones, 2025–2040 (base case)
• Table 229. Global market revenue by scenario, 2025–2040 (US$ billion)
• Table 230. Global revenue by application group, 2025–2040 (US$ billion)
• Table 231. Global unit sales by application group, 2025–2040 (thousand units)
• Table 232. Global revenue by category, 2026–2040 (US$ million)
• Table 233. Global unit sales by category, 2026–2040 (units)
• Table 234. Average selling price assumptions by category, 2026–2040 (US$ per unit)
• Table 235. Intralogistics transport robots: revenue by category, 2025–2040 (US$ million)
• Table 236. Intralogistics transport robots: unit sales by category, 2025–2040 (units)
• Table 237. Mobile picking and handling robots: revenue by category, 2025–2040 (US$ million)
• Table 238. Mobile picking and handling robots: unit sales by category, 2025–2040 (units)
• Table 239. Last-mile delivery robots, vehicles and drones: revenue by category, 2025–2040 (US$ million)
• Table 240. Last-mile delivery robots, vehicles and drones: unit sales by category, 2025–2040 (units)
• Table 241. Last-mile delivery revenue by end application, 2025–2040 (US$ million)
• Table 242. Level 4 autonomous trucks and middle-mile freight: revenue by category, 2025–2040 (US$ million)
• Table 243. Level 4 autonomous trucks and middle-mile freight: unit sales by category, 2025–2040 (units)
• Table 244. Autonomous freight: annual deliveries and installed fleet, 2025–2040 (units)
• Table 245. Global revenue by region, 2025–2040 (US$ million)
• Table 246. Revenue by region and application group, 2030 and 2040 (US$ million)
• Table 247. Regional revenue share by category, 2026 and 2040
• Table 248. Intralogistics and mobile picking robot revenue by end-use industry, 2025–2040 (US$ million)
• Table 249. Share of revenue delivered through RaaS, leasing and as-a-service contracts, 2026–2040
• Table 250. Key forecast sensitivities and risks
• Table 251. Technology and commercial roadmap by segment, 2027–2040
• Table 252. Convergence themes in logistics autonomy, 2027–2040
• Table 253. Expected labour impact of logistics autonomy by occupation, 2027–2040
• Table 254. Scenario comparison: key drivers and outcomes (qualitative)
• Table 255. Opportunities for component, software and service suppliers
• Table 256. Strategic recommendations by stakeholder and horizon
• Table 257. Forecast categories and application groups

List of Figures

• Figure 1. Global market revenue by application group, 2026–2040 (US$ billion)
• Figure 2. Taxonomy of logistics mobile robots, autonomous vehicles and drones covered in this report
• Figure 3. Global unit sales by application group, 2026–2040 (thousand units)
• Figure 4. Global market revenue by scenario, 2026–2040 (US$ billion)
• Figure 5. Taxonomy of logistics mobile robots, autonomous vehicles and drones covered in this report
• Figure 6. The logistics value chain from first mile to last metre
• Figure 7. Milestones in logistics mobile robots, autonomous vehicles and drones, 1953–2026
• Figure 8. Readiness heatmap by category, 2026
• Figure 9. E-commerce share of total US retail sales, 2019–2026 (approximate annual averages; 2026 = Q2)
• Figure 10. Strength of drivers and restraints by segment group (1 = weak, 5 = very strong)
• Figure 11. Estimated fully loaded cost per order line by fulfilment method, US, 2026 (mid-point estimates)
• Figure 12. Estimated cost per delivery by mode, small on-demand orders (US$)
• Figure 13. Illustrative cumulative net cash flow, 50-robot AMR deployment - purchase versus RaaS (US$ million)
• Figure 14. Largest disclosed funding rounds and listing proceeds in logistics mobile automation, 2025–2026 (US$ million)
• Figure 15. Estimated hardware bill of materials for a typical indoor AMR, 2026 (% of hardware cost)
• Figure 16. Technology stack of a logistics mobile robot, autonomous vehicle or drone
• Figure 17. Evolution of navigation and guidance technologies for logistics vehicles
• Figure 18. Approximate price of representative 3D LiDAR units for autonomous navigation, 2007–2026 (US$)
• Figure 19. Vendor-quoted AI performance of selected robot compute modules (TOPS/TFLOPS, mixed precision)
• Figure 20. Typical battery pack energy by logistics robot/vehicle class (midpoint, kWh)
• Figure 21. Positioning of charging approaches by infrastructure cost and achievable fleet uptime
• Figure 22. Technology benchmarking heatmap by robot category (1 = low, 5 = high)
• Figure 23. Payload versus maximum speed for representative logistics robots and vehicles (log scales)
• Figure 24. Taxonomy of intralogistics transport robots
• Figure 25. Key milestones in intralogistics transport robots
• Figure 26. Technology maturity versus remaining opportunity by intralogistics robot category
• Figure 27. Intralogistics automation technologies scored against buyer criteria (5 = most favourable)
• Figure 28. Technology evolution towards fully autonomous intralogistics robots
• Figure 29. Current adoption depth and remaining automation potential by end market (qualitative score, 1–5)
• Figure 30. Payload versus maximum speed of selected intralogistics transport robots
• Figure 31. Latest annual revenues of selected intralogistics robot companies (US$ million)
• Figure 32. Revenue of Geek+ and Symbotic, 2022–2025 (US$ million)
• Figure 33. Geek+ revenue by geography, 2025
• Figure 34. Cumulative mobile robots deployed or delivered, selected merchant suppliers (company-reported, units)
• Figure 35. Taxonomy of mobile picking, manipulation and inventory robots in logistics
• Figure 36. HAI Robotics revenue and net loss, 2023–2025 (RMB million)
• Figure 37. Typical maximum storage or reach height by goods-to-person technology (m)
• Figure 38. Milestones in logistics mobile manipulation and picking robots
• Figure 39. Stated maximum unloading throughput versus manual benchmark (cases per hour)
• Figure 40. Readiness of humanoid robots for logistics tasks versus purpose-built alternatives (1 = low, 5 = high)
• Figure 41. Representative hybrid robotic fulfilment flow
• Figure 42. Typical sustained throughput by picking configuration (picks or lines per hour, midpoint estimates)
• Figure 43. Selected funding rounds and capital raises, 2021–2026 (US$ million)
• Figure 44. Technology maturity versus near-term commercial opportunity for mobile picking and handling robots
• Figure 45. Where autonomous ground delivery fits in the fulfilment chain
• Figure 46. China express parcel volume, 2020–2025 (billion parcels)
• Figure 47. Taxonomy of autonomous ground last-mile delivery systems
• Figure 48. Relative strengths of autonomous delivery form factors (1 = weak, 5 = strong)
• Figure 49. Key milestones in China's unmanned delivery vehicle industry
• Figure 50. Fleet growth of China's two leading robovan companies, 2023–2026 (vehicles, approximate)
• Figure 51. Share of China's unmanned delivery vehicle market (2025–2026, approximate)
• Figure 52. Disclosed funding of selected autonomous ground delivery companies (US$ million, approximate)
• Figure 53. Estimated cost per on-demand delivery by delivery model, Western markets (US$, 2026)
• Figure 54. Estimated cost per parcel, China trunk-to-station leg (RMB per parcel)
• Figure 55. Positioning of leading autonomous ground delivery players (2026)
• Figure 56. Cumulative deliveries reported by leading drone delivery operators (thousands, latest disclosure to 2026)
• Figure 57. Taxonomy of delivery and cargo drone configurations
• Figure 58. Payload versus range of selected delivery and cargo drones (log scales; range is total one-way range or twice the service radius)
• Figure 59. Key regulatory and commercial milestones in drone delivery
• Figure 60. Maturity matrix for drone logistics segments and leading players
• Figure 61. Estimated cost per delivery by cost element, van versus drone at low and high density (US$, mid-point estimates)
• Figure 62. Disclosed cumulative funding of independent drone delivery and cargo companies (US$ million, estimated, to October 2026)
• Figure 63. Taxonomy of autonomous trucking operating models
• Figure 64. Hub-to-hub autonomous freight flow
• Figure 65. Estimated cumulative capital raised by autonomous freight developers (US$ million, to October 2026)
• Figure 66. Milestones in autonomous trucking commercialisation
• Figure 67. Per-mile cost of a US Class 8 long-haul truck: human-driven vs Level 4 (US$ per mile)
• Figure 68. Early-phase L4 cost per mile vs annual utilisation at three autonomy-fee levels (US$ per mile)
• Figure 69. Commercial readiness comparison of autonomous vehicle segments
• Figure 70. Technology maturity vs logistics opportunity for autonomous freight segments
• Figure 71. Expected autonomous trucking milestones, 2027–2040
• Figure 72. Major regulatory milestones for logistics robots, AVs and drones, 2023–2027
• Figure 73. EU conformity route for an AMR placed on the market after 20 January 2027
• Figure 74. Drone regulation timeline, 2016–2028
• Figure 75. Regulatory readiness matrix by country and segment, October 2026 (1 = no pathway, 5 = scalable framework)
• Figure 76. Industrial robot installations by country, 2025 (units)
• Figure 77. Regional regulatory and policy milestones for logistics autonomy, 2021–2027
• Figure 78. Regional maturity heatmap by logistics autonomy segment (2026)
• Figure 79. Competitive positioning of leading warehouse automation vendors (Future Markets assessment)
• Figure 80. Competitive positioning of outdoor autonomy operators (Future Markets assessment)
• Figure 81. Latest annual revenue of leading logistics automation companies (US$ million, converted at approximate 2025 average rates)
• Figure 82. Largest disclosed funding rounds in logistics mobile autonomy and adjacent physical AI, 2021–2026 (US$ million)
• Figure 83. Selected listings and listing milestones, 2024–2026
• Figure 84. Global market revenue, 2025–2040 (US$ billion, base case)
• Figure 85. Global market revenue by scenario, 2025–2040 (US$ billion)
• Figure 86. Global revenue by application group, 2026–2040 (US$ billion)
• Figure 87. Revenue share by application group, 2026 and 2040
• Figure 88. Global revenue by category, 2040 (US$ billion, base case)
• Figure 89. Revenue mix by category, 2026 vs 2040
• Figure 90. Intralogistics transport robots: revenue by category, 2025–2040 (US$ billion)
• Figure 91. Intralogistics transport robots: unit sales by category, 2025–2040 (thousand units)
• Figure 92. Mobile picking and handling robots: revenue by category, 2025–2040 (US$ billion)
• Figure 93. Mobile picking and handling robots: unit sales by category, 2025–2040 (thousand units)
• Figure 94. Last-mile delivery robots, vehicles and drones: revenue by category, 2025–2040 (US$ billion)
• Figure 95. Last-mile delivery robots, vehicles and drones: unit sales by category, 2025–2040 (thousand units)
• Figure 96. Level 4 autonomous trucks and middle-mile freight: revenue by category, 2025–2040 (US$ billion)
• Figure 97. Level 4 autonomous trucks and middle-mile freight: unit sales by category, 2025–2040 (thousand units)
• Figure 98. Global revenue by region, 2026–2040 (US$ billion)
• Figure 99. Regional revenue share, 2026 vs 2040 (%)
• Figure 100. Intralogistics and mobile picking robot revenue by end-use industry, 2026 and 2040 (US$ billion)
• Figure 101. Announced targets and expected milestones for logistics autonomy, 2027–2040
• Figure 102. The emerging autonomous freight chain
• Figure 103. Supplier opportunity matrix: maturity versus opportunity (Future Markets assessment)