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

1. Executive Summary 34

• 1.1  Key summary       34
• 1.2  Product taxonomy and scope    35
• 1.3  Market drivers and adoption barriers    37
• 1.4  Segment highlights          38
• 1.5  Regulation and standards: the 2027 market gates       39
• 1.6  Forecast summary           39
• 1.7  Competitive landscape and investment             41
• 1.8  Key findings and strategic implications               42

2. Introduction and Market Definition 43

• 2.1  What are logistics mobile robots, autonomous vehicles and drones?            43
• 2.2  Report scope, segmentation and taxonomy    44
• 2.3  The logistics value chain and where mobile automation fits 47
• 2.4  Historical evolution          49
• 2.5  Levels of autonomy for logistics robots and vehicles 52
  • 2.5.1  Autonomy framework for intralogistics robots           52
  • 2.5.2  SAE J3016 levels applied to logistics vehicles            53
  • 2.5.3  Drone autonomy and BVLOS operating levels            53
• 2.6  Indoor, outdoor, public-road and airspace operating domains           54
• 2.7  Commercial readiness by category, 2026         55
• 2.8  Methodology overview   57
• 2.9  Glossary of key terms      59

3. Market Drivers, Economics and Business Models 62

• 3.1  Structural drivers               62
  • 3.1.1  Labour shortages and wage inflation in warehousing           63
  • 3.1.2  Driver shortages and the trucking labour crisis         63
  • 3.1.3  E-commerce growth and fulfilment complexity        65
  • 3.1.4  Same-day delivery and the economics of speed      66
  • 3.1.5  Nearshoring, reshoring and supply-chain resilience              66
  • 3.1.6  Safety and injury reduction     66
  • 3.1.7  Sustainability and electrification         66
  • 3.1.8  AI, software and component cost declines  67
• 3.2  Restraints and adoption barriers             67
  • 3.2.1  Capital cost and the warehouse capex cycle              68
  • 3.2.2  Integration complexity and brownfield sites                68
  • 3.2.3  Interoperability               68
  • 3.2.4  Safety certification and machinery regulation            69
  • 3.2.5  Regulation for public roads and airspace      69
  • 3.2.6  Public acceptance, labour relations and cybersecurity        69
  • 3.2.7  ROI uncertainty and vendor failures 70
• 3.3  Impact assessment of drivers and restraints  70
• 3.4  Economics: ROI, payback and total cost of ownership            72
  • 3.4.1  Capex, labour displacement and payback by robot type    72
  • 3.4.2  Cost per pick in the warehouse            74
  • 3.4.3  Cost per delivery in the last mile         75
  • 3.4.4  Total cost of ownership             76
• 3.5  Business models               77
  • 3.5.1  Robotics-as-a-Service               78
  • 3.5.2  Autonomous trucking: from capacity-as-a-service to driver-as-a-service               79
  • 3.5.3  Drone and robot delivery-as-a-service            79
  • 3.5.4  The system integrator's role    80
• 3.6  Go-to-market and channels       80
• 3.7  Investment, funding and M&A, 2024–2026      82
• 3.8  Supply chain overview   86
• 3.9  Key takeaways      88

4. Enabling Technologies 89

• 4.1  Technology stack overview          89
• 4.2  Navigation and guidance technologies               91
  • 4.2.1  From buried wire to infrastructure-free navigation  91
  • 4.2.2  Comparison of navigation methods  92
  • 4.2.3  Outdoor and road navigation: HD maps versus map-light 93
  • 4.2.4  Navigation used by leading products               94
• 4.3  Localisation and SLAM in depth               95
  • 4.3.1  LiDAR SLAM, visual SLAM and fused approaches   95
  • 4.3.2  Hard cases: dynamic warehouses, long aisles and glass   95
  • 4.3.3  Navigation software and localisation vendors            96
• 4.4  Perception sensors          97
  • 4.4.1  Sensor modalities compared                97
  • 4.4.2  Safety-rated 2D LiDAR               98
  • 4.4.3  3D LiDAR: from luxury to commodity               99
  • 4.4.4  Cameras, depth sensors and the RealSense question         101
  • 4.4.5  Radar, ultrasonic, IMU and odometry              101
  • 4.4.6  Sensor suites on representative products    101
• 4.5  Sensor fusion, AI perception and foundation models                102
  • 4.5.1  From modular pipelines to learned systems               102
  • 4.5.2  Foundation models, VLAs and world models             103
  • 4.5.3  Simulation and digital twins   103
• 4.6  Compute platforms          104
• 4.7  Safety systems and functional safety   106
  • 4.7.1  Indoor vehicles: protective fields and ISO 3691-4   106
  • 4.7.2  Road vehicles: ISO 26262, SOTIF and UL 4600          107
  • 4.7.3  Drones: DO-178C, ASTM F38 and detect-and-avoid               107
• 4.8  Fleet management, orchestration and interoperability             108
  • 4.8.1  Fleet managers and traffic control     108
  • 4.8.2  Interoperability standards       108
  • 4.8.3  WMS/WES integration and multi-vendor orchestration        109
• 4.9  Connectivity and remote operations    110
  • 4.9.1  Wi-Fi and private 5G in facilities          110
  • 4.9.2  Cellular, V2X and satellite for vehicles and drones 110
  • 4.9.3  Teleoperation and remote assistance             110
• 4.10  Batteries               111
  • 4.10.1  Chemistries by robot class  111
  • 4.10.2  Battery sizing by robot type  112
• 4.11  Charging and energy management     113
  • 4.11.1  Charging strategies   113
  • 4.11.2  Contact, wireless and swap technologies 114
• 4.12  Drives, motors and chassis     115
  • 4.12.1  Ground robot drive configurations  115
  • 4.12.2  Drone propulsion, VTOL configurations and payload release        116
• 4.13  Technology benchmarking       117
• 4.14  Technology roadmap 2027–2040         119

5. Intralogistics Transport Robots 122

• 5.1  Overview: Types of Mobile Robots in Material Handling           122
• 5.2  Automated Guided Vehicles and Carts (AGV/AGC)     124
  • 5.2.1  Origins and definition 124
  • 5.2.2  Types of AGV     125
  • 5.2.3  Heavy-load and port AGVs      126
  • 5.2.4  AGV navigation methods          127
  • 5.2.5  Strengths, limitations and use in automotive and manufacturing 128
• 5.3  Grid-Based AGCs and Goods-to-Person Robots          128
  • 5.3.1  From Kiva to the mass-market shelf-lifting robot     128
  • 5.3.2  Amazon Robotics: the world's largest mobile robot fleet    129
  • 5.3.3  Geek+, Hikrobot, Quicktron and the Chinese shelf-to-person ecosystem              131
  • 5.3.4  GreyOrange, Swisslog CarryPick and Western grid-based systems             131
  • 5.3.5  Cube storage robots: AutoStore and Ocado                131
• 5.4  Autonomous Mobile Robots       132
  • 5.4.1  What makes a robot an AMR  132
  • 5.4.2  Collaborative picking AMRs    133
  • 5.4.3  Cart-transport and tugger AMRs         134
  • 5.4.4  Top modules and conveyor-top AMRs             135
  • 5.4.5  Sortation robots             135
  • 5.4.6  Tote-to-person and hospital AMRs    136
• 5.5  Autonomous Mobile Vehicles: Autonomous Forklifts, Pallet Movers and Tuggers  136
  • 5.5.1  Why the forklift is the next frontier      136
  • 5.5.2  Pure-play autonomous forklift developers    137
  • 5.5.3  Forklift OEMs and their automation strategies           138
  • 5.5.4  Heavy-duty AMRs and tow tractors   139
• 5.6  Technology Comparison: AGV/AGC, AMR, Grid-Based AGC and Fixed Automation              140
  • 5.6.1  AGV/AGC versus AMR 140
  • 5.6.2  Mobile robots versus fixed automation          141
  • 5.6.3  Transition of navigation technology   142
• 5.7  Pain Points and Engineering Challenges            143
  • 5.7.1  Why deployments fail 143
  • 5.7.2  Gap, slope and floor-condition detection      144
  • 5.7.3  Mixed traffic, safety zones and standards     145
  • 5.7.4  Connectivity, batteries and fleet scaling        145
  • 5.7.5  Uptime, service and the RaaS model               146
• 5.8  Application Analysis        146
  • 5.8.1  Overview by end market            146
  • 5.8.2  E-commerce, retail and grocery          147
  • 5.8.3  Third-party logistics     147
  • 5.8.4  Manufacturing, electronics and new energy                147
  • 5.8.5  Healthcare and pharma           148
• 5.9  Product Benchmarking  148
• 5.10  Competitive Landscape             151
  • 5.10.1  Regional overview      151
  • 5.10.2  Leading companies by segment       154
  • 5.10.3  Revenue and scale of leading listed and filing companies              157
  • 5.10.4  Installed base and market share indications           160
  • 5.10.5  Strategic partnerships with forklift OEMs and navigation suppliers          161
  • 5.10.6  Mergers and acquisitions     162
• 5.11  Other Goods-to-Person Approaches 163
• 5.12  Outlook and Key Takeaways     165

6. Mobile Picking, Manipulation and Inventory Robots 167

• 6.1  Forms of Mobile Picking, Manipulation and Inventory Robots             167
• 6.2  Autonomous Case-Handling Robots (ACRs)   169
  • 6.2.1  Concept and architecture        169
  • 6.2.2  HAI Robotics    170
  • 6.2.3  Other ACR and tote-handling vendors             171
  • 6.2.4  Exotec Skypod 172
  • 6.2.5  ACRs versus grid AGCs and shuttle systems              173
  • 6.2.6  Navigation technologies for ACRs      174
  • 6.3  Mobile Manipulators for Piece Picking and Machine Tending               175
  • 6.3.1  From research platform to product    175
  • 6.3.2  Autonomous in-aisle piece picking   176
  • 6.3.3  Industrial mobile manipulators and machine tending          177
  • 6.3.4  Evolution of picking algorithms            177
  • 6.3.5  Published picking performance           179
• 6.4  Truck and Container Unloading and Loading Robots 180
  • 6.4.1  Why the dock is the hardest problem               180
  • 6.4.2  Leading systems            180
• 6.5  Humanoid and Legged Robots in Logistics      182
  • 6.5.1  Commercial status in October 2026 182
  • 6.5.2  Realistic assessment of readiness    183
• 6.6  Inventory Robots and Drones    185
  • 6.6.1  The case for automated inventory      185
  • 6.6.2  Leading vendors             185
• 6.7  Hybrid Systems and Integration               187
  • 6.7.1  Orchestrating mixed fleets      187
  • 6.7.2  Case studies    187
• 6.8  Economics of Mobile Picking and Handling Robots    188
  • 6.8.1  Throughput comparisons        188
  • 6.8.2  Cost per pick and payback      189
• 6.9  Players      190
  • 6.9.1  Case-handling and goods-to-person robot companies       190
  • 6.9.2  Mobile manipulator, unloading and humanoid companies               191
• 6.10  Outlook, Challenges and Key Takeaways         193
  • 6.10.1  Technology positioning          193
  • 6.10.2  Challenges     193

7. Autonomous Ground Last-Mile Delivery 196

• 7.1  Last-mile delivery: definition, cost and volumes          196
  • 7.1.1  What is last-mile delivery?      196
  • 7.1.2  Why the last mile is the most expensive part of the chain  196
  • 7.1.3  Why autonomous last mile?  197
  • 7.1.4  Parcel and food delivery volumes       198
• 7.2  Delivery form factors       199
  • 7.2.1  Overview             199
  • 7.2.2  Sidewalk robots             200
  • 7.2.3  Road-going low-speed delivery vehicles and robovans       200
  • 7.2.4  Full-size autonomous delivery vans and pods           200
  • 7.2.5  "Last metre" doorstep solutions         201
  • 7.2.6  Ground versus air: comparison with delivery drones             201
• 7.3  Supporting infrastructure             203
  • 7.3.1  Micro-fulfilment centres, dark stores and decentralised warehousing     203
  • 7.3.2  Hubs, mobile lockers, charging and maintenance  203
  • 7.3.3  Teleoperation and remote operations centres            204
• 7.4  Technologies for ground delivery vehicles         204
  • 7.4.1  Sensors               204
  • 7.4.2  Localisation, mapping and autonomy software        205
  • 7.4.3  Connectivity, teleoperation and remote-operator ratios     206
  • 7.4.4  Vehicle platforms, batteries and cost reduction       207
• 7.5  China's robovan boom   207
  • 7.5.1  From pilot to industry 207
  • 7.5.2  Neolix              208
  • 7.5.3  Zelos             209
  • 7.5.4  Platform operators and other Chinese players           209
  • 7.5.5  Courier adoption, unit prices and economics            212
  • 7.5.6  City road rights and operating data    212
• 7.6  United States and Europe: road-going delivery vehicles          213
  • 7.6.1  Nuro: from delivery pods to autonomy licensing      213
  • 7.6.2  Middle-mile and specialist road delivery players      213
  • 7.6.3  DoorDash Dot and the bike-lane robot           214
• 7.7  Sidewalk delivery robots               215
  • 7.7.1  Starship Technologies                215
  • 7.7.2  Serve Robotics                215
  • 7.7.3  Coco, Avride, Robot.com and other US operators   216
  • 7.7.4  Asia: Korea, Japan and Russia              216
  • 7.7.5  Product benchmarking              216
  • 7.7.6  Deployment status and funding          217
• 7.8  Operating economics     219
  • 7.8.1  The cost structure of a delivery robot               219
  • 7.8.2  Utilisation, deliveries per day and supervision in practice  220
  • 7.8.3  Comparative cost per delivery              221
• 7.9  Regulations for ground delivery robots and vehicles  222
  • 7.9.1  United States: personal delivery device laws              222
  • 7.9.2  China, Japan and South Korea              223
  • 7.9.3  United Kingdom and Europe  223
• 7.10  Players   225
  • 7.10.1  Regional distribution and what players deliver        225
  • 7.10.2  Company overview table       226
• 7.11  Challenges and key takeaways               228
  • 7.11.1  Challenges     228
  • 7.11.2  Key takeaways              230

8. Delivery and Cargo Drones 231

• 8.1  Drone Logistics Overview             231
  • 8.1.1  Use Cases         231
  • 8.1.2  Deliveries to Date          232
  • 8.1.3  Last-Mile, Mid-Mile and Long-Haul    234
• 8.2  Drone Designs     235
  • 8.2.1  Multicopters     236
  • 8.2.2  Fixed-Wing Aircraft       237
  • 8.2.3  Hybrid VTOL: Lift+Cruise, Tilt-Rotor and Tail-Sitter  237
  • 8.2.4  Payload Delivery Mechanisms             238
  • 8.2.5  Payload and Range of Commercial Models 239
• 8.3  Technologies         240
  • 8.3.1  Sensors and Detect-and-Avoid             240
  • 8.3.2  Navigation and Autonomy       241
  • 8.3.3  Operating Restrictions: Weather, Noise and Endurance     242
  • 8.3.4  UTM, U-Space and Strategic Deconfliction  242
  • 8.3.5  Docking, Nests and Ground Infrastructure   243
  • 8.3.6  Fleet Operations and Remote-Pilot Ratios   243
• 8.4  Regulation              244
  • 8.4.1  United States   244
  • 8.4.2  European Union             246
  • 8.4.3  United Kingdom             246
  • 8.4.4  China    246
  • 8.4.5  Japan, South Korea and India                247
  • 8.4.6  Australia, Africa and the Middle East               247
  • 8.4.7  Comparative Summary             247
• 8.5  Commercialisation Status by Region   249
  • 8.5.1  United States   249
  • 8.5.2  Europe and the United Kingdom         250
  • 8.5.3  China    251
  • 8.5.4  Japan and South Korea              252
  • 8.5.5  Africa     252
  • 8.5.6  India, the Middle East and Latin America      253
• 8.6  Landscape and Maturity Assessment 253
  • 8.6.1  Maturity Matrix 255
• 8.7  Case Studies        256
  • 8.7.1  Wing versus Zipline: Competing Approaches to US Home Delivery             256
  • 8.7.2  China's Drone Logistics Race: Meituan, SF Express, JD and EHang            257
  • 8.7.3  Amazon Prime Air          258
  • 8.7.4  Manna in Ireland and Finland                258
  • 8.7.5  Matternet and Healthcare Delivery    259
  • 8.7.6  Mid-Mile and Long-Haul Cargo            259
• 8.8  Economics             260
  • 8.8.1  Cost per Delivery           260
  • 8.8.2  Fleet Utilisation and Site Throughput               261
  • 8.8.3  Pricing Models and Infrastructure Costs       262
• 8.9  Funding and Partnerships            263
• 8.10  Barriers and Key Takeaways      265

9. Level 4 Autonomous Trucks, Middle-Mile and Yard Automation 267

• 9.1  The case for automating heavy-duty trucks      267
  • 9.1.1  Pain points in road freight        267
  • 9.1.2  Freight market context by region         268
  • 9.1.3  Why trucks first               269
• 9.2  Levels of automation and operating models    269
  • 9.2.1  SAE levels applied to trucking               269
  • 9.2.2  Level 2 versus Level 4 trucking             270
  • 9.2.3  Operating models         270
  • 9.2.4  Hub-to-hub segment economics       272
• 9.3  Business models               273
  • 9.3.1  From technology developer to freight provider           273
  • 9.3.2  Driver-as-a-Service: the emerging default    274
  • 9.3.3  OEM strategies                274
  • 9.3.4  Business models by company              275
• 9.4  Technology             276
  • 9.4.1  Sensor suites   276
  • 9.4.2  Redundant platforms 277
  • 9.4.3  Simulation, validation and AI architecture    277
  • 9.4.4  Remote assistance and operations   278
• 9.5  Regulation              278
  • 9.5.1  United States: federal framework       278
  • 9.5.2  United States: state laws          280
  • 9.5.3  China    281
  • 9.5.4  Europe  281
  • 9.5.5  Japan, Korea and others           281
• 9.6  Players and market maturity       282
  • 9.6.1  Industry structure after the shake-out            282
  • 9.6.2  Market readiness of leading companies        283
  • 9.6.3  Profiles of the leaders 284
  • 9.6.4  Funding               286
  • 9.6.5  Company locations     287
  • 9.6.6  Deployment timeline  288
• 9.7  Deployment status, routes and safety supervision     288
  • 9.7.1  United States   288
  • 9.7.2  China and rest of world             289
• 9.8  Total cost of ownership analysis             290
  • 9.8.1  Approach and assumptions  290
  • 9.8.2  Results 291
  • 9.8.3  Sensitivity           293
• 9.9  Drivers, hurdles and competitive position        294
  • 9.9.1  Six key drivers  294
  • 9.9.2  Remaining hurdles       294
  • 9.9.3  SWOT analysis 295
  • 9.9.4  Commercial readiness compared with other autonomous vehicles           296
• 9.10  Yard trucks, ports and closed-site logistics   297
  • 9.10.1  Distribution and logistics yards         297
  • 9.10.2  Ports and container terminals           297
  • 9.10.3  Mining and industrial haulage            298
• 9.11  Timelines and outlook 299
  • 9.11.1  Our view of milestones, 2027–2040              299
  • 9.11.2  Ones to watch               300
  • 9.11.3  Conclusion    301

10. Regulation, Safety Standards and Policy 302

• 10.1  Overview: four regulatory domains and the 2027 market gates        302
  • 10.1.1  The four regulatory domains               302
  • 10.1.2  The 2027 market gates            303
  • 10.1.3  The EU Machinery Regulation and AMRs    304
  • 10.1.4  UK equivalents             306
• 10.2  Industrial mobile robot safety standards        306
  • 10.2.1  ISO 3691-4 and ANSI/ITSDF B56.5  306
  • 10.2.2  ANSI/A3 R15.08 series            307
  • 10.2.3  ISO 10218:2025 and collaborative operation           307
  • 10.2.4  Functional safety, electrical and product standards          307
• 10.3  Interoperability standards        309
• 10.4  Ground delivery robots               310
  • 10.4.1  United States: PDD statutes                310
  • 10.4.2  China 311
  • 10.4.3  Japan and South Korea           312
  • 10.4.4  Europe and the UK     312
• 10.5  Road autonomous vehicles and trucks            313
  • 10.5.1  United States: federal              313
  • 10.5.2  United States: states                313
  • 10.5.3  China 314
  • 10.5.4  Europe, the UK, Japan and Korea     315
• 10.6  Drones and airspace    316
  • 10.6.1  United States 316
  • 10.6.2  Europe and the UK     317
  • 10.6.3  China, Japan, India and Australia    317
• 10.7  Cybersecurity, data protection and AI governance   319
  • 10.7.1  Product and vehicle cybersecurity   319
  • 10.7.2  Data protection            319
  • 10.7.3  AI governance               320
  • 10.7.4  National-security restrictions on Chinese technology       320
• 10.8  Trade, tariffs and geopolitics   322
• 10.9  Insurance and liability frameworks     323
• 10.10  Regulatory readiness matrix and outlook to 2030  325
  • 10.10.1  Outlook to 2030       326

11. Regional Market Analysis 328

• 11.1  Regional Overview: How Geography Shapes Adoption          328
  • 11.1.1  Four forces that explain regional differences           328
  • 11.1.2  Automation indicators            330
• 11.2  North America  331
  • 11.2.1  United States 331
  • 11.2.2  Canada            333
• 11.3  Europe   333
  • 11.3.1  Regional characteristics and policy framework      333
  • 11.3.2  Germany          334
  • 11.3.3  United Kingdom          335
  • 11.3.4  France, the Nordics, the Netherlands and the rest of Europe        335
• 11.4  China      335
  • 11.4.1  Market characteristics            335
  • 11.4.2  Policy support               336
  • 11.4.3  Adoption by segment               336
• 11.5  Japan      338
  • 11.5.1  The "2024 problem" and the logistics labour crisis              338
  • 11.5.2  Adoption by segment and leading players  338
• 11.6  South Korea        339
• 11.7  India and Southeast Asia          340
  • 11.7.1  India   340
  • 11.7.2  Southeast Asia            340
• 11.8  Middle East         341
• 11.9  Latin America    342
• 11.10  Africa   342
• 11.11  Comparative Assessment      343
  • 11.11.1  Government programmes 343
  • 11.11.2  Leading players by region   344
  • 11.11.3  Regional maturity by segment         345
  • 11.11.4  Labour context across regions       346

12. Competitive Landscape, Strategy and Investment 348

• 12.1  Market Structure by Segment 348
  • 12.1.1  A fragmented market with concentrated pockets  348
  • 12.1.2  In-house versus merchant supply   349
• 12.2  Strategic Groupings       350
  • 12.2.1  Competitive positioning        351
  • 12.2.2  Competitive dynamics within the principal segments       353
• 12.3  Financial Benchmarking of Leading Listed Companies         354
  • 12.3.1  Revenues and profitability    354
  • 12.3.2  What the numbers show       356
• 12.4  Mergers and Acquisitions, 2020–2026             357
  • 12.4.1  Patterns in deal activity          357
• 12.5  Investment and Funding            359
  • 12.5.1  Funding cycle, 2021–2026   359
  • 12.5.2  IPO pipeline    361
• 12.6  Strategic Partnerships 363
• 12.7  Competitive Strategies                365
  • 12.7.1  Vertical integration    365
  • 12.7.2  Robots-as-a-service and outcome-based pricing 365
  • 12.7.3  Ecosystems, interoperability and software platforms        365
  • 12.7.4  Price competition from China            365
  • 12.7.5  Summary of strategic archetypes    366
• 12.8  Ones to Watch  367

13. Market Forecasts 2027–2040 369

• 13.1  Forecast scope, definitions and methodology            369
• 13.2  Global market revenue and unit sales, 2025–2040  370
• 13.3  Scenario analysis           372
• 13.4  Forecast by application group                373
• 13.5  Forecast by robot and vehicle category            376
• 13.6  Intralogistics transport robots                380
• 13.7  Mobile picking and handling robots    382
• 13.8  Last-mile delivery robots, vehicles and drones           385
• 13.9  Level 4 autonomous trucks and middle-mile freight               388
• 13.10  Forecast by region       392
• 13.11  Intralogistics and picking robots by end-use industry         394
• 13.12  Business model mix: equipment sales, leasing and Robotics-as-a-Service         395
• 13.13  Captive deployments, sensitivities and forecast risks        396

14. Future Outlook and Technology Roadmap 398

• 14.1  Outlook: From Islands of Automation to Autonomous Logistics Networks               398
• 14.2  Technology Roadmap by Segment       398
  • 14.2.1  Roadmap overview   398
  • 14.2.2  Key milestones            399
  • 14.2.3  Cost curves and the economics of scale    400
• 14.3  Convergence Themes  400
  • 14.3.1  Humanoids and AMRs            400
  • 14.3.2  Robovans, drones and the "mothership" model    401
  • 14.3.3  Autonomous trucks and automated hubs 401
  • 14.3.4  Lights-out warehouses           401
• 14.4  Impact on Labour and Jobs      402
  • 14.4.1  Scale of exposure       402
  • 14.4.2  Displacement, augmentation and new roles           403
  • 14.4.3  Policy and social licence        403
• 14.5  Scenarios to 2040          404
• 14.6  Opportunities for Suppliers      405
  • 14.6.1  Enabling technologies            405
• 14.7  Strategic Recommendations  407
  • 14.7.1  For technology vendors          407
  • 14.7.2  For investors  407
  • 14.7.3  For logistics operators and shippers              407

15. Company Profiles 409 (205 company profiles)

16. Appendix: Research Methodology 614

• 16.1  Research approach       614
• 16.2  Market definition and segmentation  614
• 16.3  Forecast methodology 615
• 16.4  Limitations          615

17. References 616

List of Tables

• Table 1. Headline market metrics, 2026–2040 (base case)             34
• Table 2. Summary of categories: environment, maturity and leading companies (2026)            36
• Table 3. Major market drivers and restraints             38
• Table 4. Revenue forecast by category, 2026–2040 (US$ million, base case)      39
• Table 5. Unit sales forecast by category, 2026–2040 (units, base case)  40
• Table 6. Fixed automation versus mobile automation in logistics               43
• Table 7. Product categories in scope — definitions, payloads, environments and leading examples 45
• Table 8. Mapping mobile robot and autonomous vehicle categories to logistics value-chain stages  48
• Table 9. Selected historical milestones by segment            51
• Table 10. Autonomy framework for intralogistics mobile robots  52
• Table 11. SAE J3016 driving automation levels and their relevance to logistics vehicles              53
• Table 12. Operational autonomy levels for delivery and cargo drones     54
• Table 13. Comparison of operating domains for logistics mobile robots, AVs and drones          54
• Table 14. Technology and commercial readiness by category, 2026         55
• Table 15. Key definitions and conventions used in this report        58
• Table 16. Glossary of key terms         59
• Table 17. Summary of structural market drivers     62
• Table 18. Labour-market indicators for warehousing and freight transport           64
• Table 19. Summary of market restraints and adoption barriers    67
• Table 20. Selected vendor failures, exits and distressed transactions, 2023–2026        70
• Table 21. Impact assessment of drivers and restraints over time (–3 strong negative to +3 strong positive)            71
• Table 22. Indicative economics and payback by robot and vehicle type, 2026 (US/Western Europe)  73
• Table 23. Productivity and estimated cost per order line by fulfilment method (US, 2026)        74
• Table 24. Total cost of ownership components for mobile robot and autonomous vehicle deployments                76
• Table 25. Comparison of business models in logistics mobile automation          77
• Table 26. Indicative pricing structures by segment, 2026 80
• Table 27. Go-to-market channels and representative partnerships          81
• Table 28. Selected major funding rounds and listings, 2024–2026            82
• Table 29. Selected mergers, acquisitions and strategic transactions, 2023–2026          84
• Table 30. Mobile robot component supply chain   86
• Table 31. Where engineering effort concentrates in the stack, by robot category             90
• Table 32. Comparison of navigation and guidance technologies 92
• Table 33. Map strategies for outdoor and on-road logistics autonomy    93
• Table 34. Navigation approaches used by representative logistics robots and vehicles               94
• Table 35. Localisation challenges in logistics environments and common mitigations               95
• Table 36. Selected navigation and localisation software vendors for logistics vehicles                96
• Table 37. Perception sensor modalities for logistics robots and vehicles              97
• Table 38. Representative safety-rated 2D LiDAR scanners used on AGVs and AMRs     99
• Table 39. Representative 3D LiDAR and depth sensors used in logistics robots and vehicles  100
• Table 40. Sensor suites on representative logistics robots, vehicles and drones              102
• Table 41. AI models and simulation platforms relevant to logistics robotics       103
• Table 42. Compute platforms for logistics robots, autonomous vehicles and drones   104
• Table 43. Safety standards and frameworks by robot category     107
• Table 44. Interoperability standards and frameworks for multi-vendor robot fleets        108
• Table 45. Fleet management and multi-vendor orchestration platforms                109
• Table 46. Indicative network requirements for remote operations of logistics robots and vehicles       111
• Table 47. Battery chemistries for logistics robots, vehicles and drones  112
• Table 48. Typical battery sizing by logistics robot and vehicle type             112
• Table 49. Charging and energy-replenishment approaches for logistics robots and vehicles   114
• Table 50. Ground robot drive and chassis configurations 116
• Table 51. Delivery drone propulsion configurations and payload-release methods        117
• Table 52. Technology roadmap for logistics mobile robots, autonomous vehicles and drones, 2027–2040    119
• Table 53. Working definitions of intralogistics transport robot categories              123
• Table 54. Main types of automated guided vehicle                125
• Table 55. Representative AGV-based automated container terminals     126
• Table 56. AGV guidance and navigation methods compared         127
• Table 57. AGV and AMR use cases in automotive and battery manufacturing    128
• Table 58. Leading grid-based AGC / shelf-to-person robot product families       129
• Table 59. Amazon Robotics fleet: principal mobile and handling robots 130
• Table 60. Collaborative picking and picking-assist AMR platforms            133
• Table 61. Selected cart-transport, tugger and top-module AMR platforms           134
• Table 62. Robotic sortation systems compared with fixed sortation         136
• Table 63. Forklift OEM automation portfolios           138
• Table 64. Representative autonomous forklift, pallet-mover and tugger products           139
• Table 65. AGV/AGC versus AMR: comparison of key attributes     140
• Table 66. Mobile robots versus fixed automation: deployment, cost and performance characteristics                141
• Table 67. Generations of mobile robot navigation technology       142
• Table 68. Pain points of intralogistics mobile robots and engineering responses             144
• Table 69. Key safety standards for intralogistics mobile robots    145
• Table 70. Applications of intralogistics transport robots by end market  146
• Table 71. Benchmark of selected intralogistics transport robots 149
• Table 72. Selected intralogistics transport robot companies by region and category     151
• Table 73. Leading AGV suppliers and their positioning      154
• Table 74. Leading grid-based AGC and cube-storage robot suppliers      155
• Table 75. Leading AMR suppliers and positioning 156
• Table 76. Leading autonomous forklift and AMV developers          156
• Table 77. Reported revenues of selected listed and IPO-filing intralogistics robot companies                157
• Table 78. Strategic partnerships between autonomy developers and forklift OEMs / logistics users   161
• Table 79. Selected mergers and acquisitions in intralogistics mobile robotics  162
• Table 80. Alternative goods-to-person architectures and their status      164
• Table 81. Classes of mobile picking, manipulation and inventory robots              168
• Table 82. HAI Robotics HaiPick product family (principal configurations)             171
• Table 83. Selected case-handling and tote-retrieval robot products         172
• Table 84. Comparison of goods-to-person storage technologies (typical ranges)            173
• Table 85. Navigation and positioning technologies used by case-handling robots          174
• Table 86. Representative mobile manipulator products for logistics and industrial use              177
• Table 87. Generations of robotic grasping and picking algorithms             178
• Table 88. Robot foundation-model and AI-manipulation companies relevant to logistics          178
• Table 89. Publicly stated performance indicators for picking and handling robots          179
• Table 90. Truck and container unloading and loading robots         181
• Table 91. Humanoid robots in logistics: programmes and deployment status (October 2026)               183
• Table 92. Humanoids versus purpose-built robots for warehouse tote handling              184
• Table 93. Inventory robot and drone vendors            186
• Table 94. Comparison of automated inventory approaches           186
• Table 95. Selected deployments of case-handling, picking and unloading robots          187
• Table 96. Indicative fully loaded cost per pick by configuration (US/Western Europe, 2026)    189
• Table 97. Payback drivers for mobile picking, unloading and inventory robots   190
• Table 98. Case-handling robot and mobile goods-to-person companies              190
• Table 99. Mobile manipulator, truck-unloading and logistics humanoid companies     191
• Table 100. Safety standards relevant to mobile manipulators, case-handling robots and humanoids                194
• Table 101. Key challenges by category          194
• Table 102. Indicative cost structure of conventional last-mile delivery (2025–2026)     197
• Table 103. Main last-mile volume pools relevant to autonomous ground delivery           198
• Table 104. Autonomous ground delivery form factors — typical specifications (2026) 200
• Table 105. "Last metre" delivery approaches           201
• Table 106. Ground delivery systems versus delivery drones — performance comparison         202
• Table 107. Ground delivery systems versus delivery drones — regulatory and commercial comparison                202
• Table 108. Supporting infrastructure requirements by form factor             204
• Table 109. Sensor suites of representative autonomous ground delivery platforms       205
• Table 110. Localisation and autonomy approaches in ground delivery    206
• Table 111. Remote supervision models in autonomous ground delivery (indicative)     206
• Table 112. Major Chinese unmanned delivery vehicle companies (October 2026)         209
• Table 113. Representative Chinese robovan products and prices               210
• Table 114. Robovan adoption by Chinese express and logistics companies (2025–2026)         212
• Table 115. Selected Chinese city regimes for unmanned delivery vehicles (2025–2026)            213
• Table 116. US and European road-going autonomous delivery players (October 2026)               214
• Table 117. Benchmarking of leading sidewalk and small road delivery robots (2026)   216
• Table 118. Sidewalk delivery robot deployment status (October 2026)  217
• Table 119. Major funding events in autonomous ground delivery, 2025–2026    218
• Table 120. Illustrative annual cost model for a sidewalk delivery robot (US$, Future Markets estimates)                219
• Table 121. Key operating metrics by delivery model (2025–2026, indicative)      220
• Table 122. Typical provisions of US state personal delivery device (PDD) laws  223
• Table 123. Regulatory status of ground delivery robots and vehicles by country (October 2026)          224
• Table 124. What autonomous ground delivery players deliver today (2026)        225
• Table 125. Autonomous ground last-mile delivery companies (October 2026) 226
• Table 126. Key challenges for autonomous ground delivery and mitigation approaches             229
• Table 127. Principal use cases for delivery and cargo drones        231
• Table 128. Cumulative drone deliveries reported by leading operators (latest disclosure)         232
• Table 129. Comparison of last-mile, mid-mile and long-haul drone logistics     234
• Table 130. Drone logistics operating scenarios       234
• Table 131. Comparison of delivery drone configurations  237
• Table 132. Payload delivery mechanisms   238
• Table 133. Specifications of selected delivery and cargo drones 239
• Table 134. Sensing and detect-and-avoid technologies used in delivery drones              241
• Table 135. Operating constraints for delivery drones and mitigation approaches            242
• Table 136. UTM and airspace integration initiatives relevant to drone delivery   243
• Table 137. Evolution of drone delivery staffing models      244
• Table 138. US regulatory status of leading drone delivery operators         244
• Table 139. Key provisions of the proposed FAA Part 108 BVLOS rule         245
• Table 140. Drone delivery regulatory status by country/region (October 2026) 248
• Table 141. Principal US drone delivery operators and partners (2026)    249
• Table 142. Drone delivery activity in Europe and the UK    250
• Table 143. Leading Chinese drone logistics operators        251
• Table 144. Drone delivery activity in Asia-Pacific (excluding China)          252
• Table 145. Zipline and other drone delivery operations in Africa  253
• Table 146. Drone delivery activity in India, the Middle East and Latin America  253
• Table 147. Delivery and cargo drone company landscape               254
• Table 148. Wing and Zipline compared         257
• Table 149. China's leading drone logistics players compared       258
• Table 150. Mid-mile and long-haul cargo drone programmes       259
• Table 151. Estimated cost per last-mile delivery, van versus drone (US$, 2026)               260
• Table 152. Disclosed utilisation and throughput indicators            261
• Table 153. Drone delivery pricing models   262
• Table 154. Indicative infrastructure requirements and costs by model   263
• Table 155. Selected funding rounds and strategic partnerships in drone delivery            263
• Table 156. Barriers to drone delivery and their trajectory  265
• Table 157. Trucking pain points and how Level 4 autonomy addresses them      268
• Table 158. Road freight context for autonomous trucking by region          268
• Table 159. SAE levels of driving automation applied to heavy trucks        269
• Table 160. Indicative segment characteristics of a hub-to-hub autonomous lane          272
• Table 161. Business model options for autonomous trucking developers             273
• Table 162. Truck OEM autonomy strategies and partnerships (October 2026)   274
• Table 163. Business and operating models of leading autonomous freight companies               275
• Table 164. Sensor suites and compute of leading autonomous truck developers            276
• Table 165. OEM autonomy-ready redundant truck platforms        277
• Table 166. Operational infrastructure required for driverless trucking     278
• Table 167. Key US federal instruments affecting autonomous trucks (October 2026)  279
• Table 168. State regulatory position for driverless heavy trucks (October 2026)               280
• Table 169. International regulatory frameworks for L4 autonomous trucks          282
• Table 170. Notable exits and pivots in autonomous trucking         282
• Table 171. Market readiness of autonomous truck and freight autonomy companies (October 2026)                283
• Table 172. Selected autonomous trucking funding and capital-markets events, 2024–2026  286
• Table 173. Headquarters and operating bases of autonomous freight companies         287
• Table 174. Autonomous truck deployment status, routes and safety supervision (October 2026)       288
• Table 175. Principal US autonomous trucking corridors    289
• Table 176. Selected autonomous truck deployments outside the United States              290
• Table 177. TCO model assumptions — US Class 8 long-haul tractor        290
• Table 178. Per-mile TCO comparison — human-driven vs L4 DaaS (US$ per mile)          292
• Table 179. Sensitivity of early-phase L4 TCO to key variables        293
• Table 180. Key drivers and hurdles for heavy-duty L4 trucks — impact assessment      294
• Table 181. SWOT analysis — heavy-duty Level 4 autonomous trucks      295
• Table 182. Yard, port and closed-site autonomous freight vehicle providers       298
• Table 183. Future Markets view of autonomous trucking milestones, 2027–2040           299
• Table 184. Ones to watch in autonomous trucking               300
• Table 185. The four regulatory domains for logistics mobile robots, autonomous vehicles and drones                302
• Table 186. Key regulatory "gates", September 2026 – August 2028            303
• Table 187. EU Machinery Regulation 2023/1230 — key changes for mobile robots versus Directive 2006/42/EC    305
• Table 188. Safety and product standards for industrial mobile robots     308
• Table 189. Typical AMR safety functions and target performance levels 309
• Table 190. Interoperability standards for mobile robot fleets         310
• Table 191. Representative US state personal delivery device laws             311
• Table 192. International frameworks for ground delivery robots  312
• Table 193. United States framework for autonomous delivery vehicles and trucks (October 2026)     314
• Table 194. Road AV and autonomous truck regulation by country (October 2026)         315
• Table 195. US regulatory pathways for drone delivery operations               317
• Table 196. Drone delivery regulation by country (October 2026) 318
• Table 197. US national-security measures affecting Chinese robotics, AV and drone technology        320
• Table 198. Cybersecurity, data protection and AI governance instruments          321
• Table 199. Trade and tariff measures affecting logistics robots, AVs and drones (2018–2026) 322
• Table 200. Liability and insurance frameworks by segment and jurisdiction       324
• Table 201. Expected regulatory milestones, 2026–2030   326
• Table 202. Regional comparison of adoption drivers for logistics mobile robots, autonomous vehicles and drones (2026)      329
• Table 203. Industrial robot installations and robot density, selected countries (latest IFR data)            330
• Table 204. Selected notable US deployments of logistics autonomy (to October 2026)             332
• Table 205. European country profiles for logistics mobile autonomy (2026)       334
• Table 206. China's principal policy instruments for logistics robots, unmanned vehicles and drones                336
• Table 207. Leading Chinese players in logistics robots, unmanned vehicles and drones            337
• Table 208. Japan's responses to the logistics "2024 problem" relevant to autonomy    338
• Table 209. South Korea: key policies and players in logistics autonomy 339
• Table 210. India and Southeast Asia: leading players and representative deployments              341
• Table 211. Representative deployments in the Middle East, Latin America and Africa  342
• Table 212. Government programmes and regulatory milestones supporting logistics autonomy, by region  343
• Table 213. Leading players by region and segment (2026)               345
• Table 214. Labour context and its implications for logistics automation, by region        346
• Table 215. Competitive structure by segment (October 2026)     348
• Table 216. Captive, hybrid and merchant supply models in logistics automation           350
• Table 217. Strategic groups in logistics mobile autonomy               350
• Table 218. Revenue and profitability of leading listed companies in logistics automation and autonomy (latest fiscal year)        354
• Table 219. Key mergers, acquisitions and take-privates in logistics mobile autonomy, 2020–2026     357
• Table 220. Notable failures, shutdowns and restructurings, 2022–2026 359
• Table 221. Major disclosed funding rounds in logistics mobile autonomy, 2021–2026 359
• Table 222. IPO and listing pipeline for logistics mobile autonomy companies (October 2026)               362
• Table 223. Strategic partnerships shaping the logistics autonomy market (selected, to October 2026)                363
• Table 224. Chinese versus Western vendors: competitive positioning in warehouse robotics 366
• Table 225. Competitive strategy archetypes in logistics mobile autonomy          366
• Table 226. Ones to watch, 2027–2028          367
• Table 227. Base-year assumptions and key forecast drivers by category               370
• Table 228. Global market revenue and unit sales for logistics mobile robots, autonomous vehicles and drones, 2025–2040 (base case)        371
• Table 229. Global market revenue by scenario, 2025–2040 (US$ billion)               372
• Table 230. Global revenue by application group, 2025–2040 (US$ billion)            374
• Table 231. Global unit sales by application group, 2025–2040 (thousand units)              375
• Table 232. Global revenue by category, 2026–2040 (US$ million)               376
• Table 233. Global unit sales by category, 2026–2040 (units)          377
• Table 234. Average selling price assumptions by category, 2026–2040 (US$ per unit)   378
• Table 235. Intralogistics transport robots: revenue by category, 2025–2040 (US$ million)         380
• Table 236. Intralogistics transport robots: unit sales by category, 2025–2040 (units)    381
• Table 237. Mobile picking and handling robots: revenue by category, 2025–2040 (US$ million)            383
• Table 238. Mobile picking and handling robots: unit sales by category, 2025–2040 (units)       383
• Table 239. Last-mile delivery robots, vehicles and drones: revenue by category, 2025–2040 (US$ million)                385
• Table 240. Last-mile delivery robots, vehicles and drones: unit sales by category, 2025–2040 (units)                386
• Table 241. Last-mile delivery revenue by end application, 2025–2040 (US$ million)     388
• Table 242. Level 4 autonomous trucks and middle-mile freight: revenue by category, 2025–2040 (US$ million)              388
• Table 243. Level 4 autonomous trucks and middle-mile freight: unit sales by category, 2025–2040 (units)                389
• Table 244. Autonomous freight: annual deliveries and installed fleet, 2025–2040 (units)          391
• Table 245. Global revenue by region, 2025–2040 (US$ million)    392
• Table 246. Revenue by region and application group, 2030 and 2040 (US$ million)        393
• Table 247. Regional revenue share by category, 2026 and 2040  393
• Table 248. Intralogistics and mobile picking robot revenue by end-use industry, 2025–2040 (US$ million)                394
• Table 249. Share of revenue delivered through RaaS, leasing and as-a-service contracts, 2026–2040                396
• Table 250. Key forecast sensitivities and risks         396
• Table 251. Technology and commercial roadmap by segment, 2027–2040         398
• Table 252. Convergence themes in logistics autonomy, 2027–2040         401
• Table 253. Expected labour impact of logistics autonomy by occupation, 2027–2040 403
• Table 254. Scenario comparison: key drivers and outcomes (qualitative)             404
• Table 255. Opportunities for component, software and service suppliers            405
• Table 256. Strategic recommendations by stakeholder and horizon         408
• Table 257. Forecast categories and application groups     614

List of Figures

• Figure 1. Global market revenue by application group, 2026–2040 (US$ billion)              35
• Figure 2. Taxonomy of logistics mobile robots, autonomous vehicles and drones covered in this report                36
• Figure 3. Global unit sales by application group, 2026–2040 (thousand units)  41
• Figure 4. Global market revenue by scenario, 2026–2040 (US$ billion)   41
• Figure 5. Taxonomy of logistics mobile robots, autonomous vehicles and drones covered in this report                45
• Figure 6. The logistics value chain from first mile to last metre     48
• Figure 7. Milestones in logistics mobile robots, autonomous vehicles and drones, 1953–2026            50
• Figure 8. Readiness heatmap by category, 2026    57
• Figure 9. E-commerce share of total US retail sales, 2019–2026 (approximate annual averages; 2026 = Q2)       65
• Figure 10. Strength of drivers and restraints by segment group (1 = weak, 5 = very strong)        72
• Figure 11. Estimated fully loaded cost per order line by fulfilment method, US, 2026 (mid-point estimates)        75
• Figure 12. Estimated cost per delivery by mode, small on-demand orders (US$)             76
• Figure 13. Illustrative cumulative net cash flow, 50-robot AMR deployment — purchase versus RaaS (US$ million)  79
• Figure 14. Largest disclosed funding rounds and listing proceeds in logistics mobile automation, 2025–2026 (US$ million)      84
• Figure 15. Estimated hardware bill of materials for a typical indoor AMR, 2026 (% of hardware cost) 87
• Figure 16. Technology stack of a logistics mobile robot, autonomous vehicle or drone               90
• Figure 17. Evolution of navigation and guidance technologies for logistics vehicles       92
• Figure 18. Approximate price of representative 3D LiDAR units for autonomous navigation, 2007–2026 (US$)   100
• Figure 19. Vendor-quoted AI performance of selected robot compute modules (TOPS/TFLOPS, mixed precision)         106
• Figure 20. Typical battery pack energy by logistics robot/vehicle class (midpoint, kWh)             113
• Figure 21. Positioning of charging approaches by infrastructure cost and achievable fleet uptime      115
• Figure 22. Technology benchmarking heatmap by robot category (1 = low, 5 = high)      118
• Figure 23. Payload versus maximum speed for representative logistics robots and vehicles (log scales)                119
• Figure 24. Taxonomy of intralogistics transport robots       123
• Figure 25. Key milestones in intralogistics transport robots            132
• Figure 26. Technology maturity versus remaining opportunity by intralogistics robot category               137
• Figure 27. Intralogistics automation technologies scored against buyer criteria (5 = most favourable)                141
• Figure 28. Technology evolution towards fully autonomous intralogistics robots            143
• Figure 29. Current adoption depth and remaining automation potential by end market (qualitative score, 1–5)       148
• Figure 30. Payload versus maximum speed of selected intralogistics transport robots               151
• Figure 31. Latest annual revenues of selected intralogistics robot companies (US$ million)   158
• Figure 32. Revenue of Geek+ and Symbotic, 2022–2025 (US$ million)   159
• Figure 33. Geek+ revenue by geography, 2025         160
• Figure 34. Cumulative mobile robots deployed or delivered, selected merchant suppliers (company-reported, units)            161
• Figure 35. Taxonomy of mobile picking, manipulation and inventory robots in logistics              168
• Figure 36. HAI Robotics revenue and net loss, 2023–2025 (RMB million)              170
• Figure 37. Typical maximum storage or reach height by goods-to-person technology (m)         174
• Figure 38. Milestones in logistics mobile manipulation and picking robots          176
• Figure 39. Stated maximum unloading throughput versus manual benchmark (cases per hour)           182
• Figure 40. Readiness of humanoid robots for logistics tasks versus purpose-built alternatives (1 = low, 5 = high) 184
• Figure 41. Representative hybrid robotic fulfilment flow   187
• Figure 42. Typical sustained throughput by picking configuration (picks or lines per hour, midpoint estimates)        189
• Figure 43. Selected funding rounds and capital raises, 2021–2026 (US$ million)            192
• Figure 44. Technology maturity versus near-term commercial opportunity for mobile picking and handling robots            193
• Figure 45. Where autonomous ground delivery fits in the fulfilment chain           196
• Figure 46. China express parcel volume, 2020–2025 (billion parcels)      198
• Figure 47. Taxonomy of autonomous ground last-mile delivery systems                199
• Figure 48. Relative strengths of autonomous delivery form factors (1 = weak, 5 = strong)          203
• Figure 49. Key milestones in China's unmanned delivery vehicle industry            208
• Figure 50. Fleet growth of China's two leading robovan companies, 2023–2026 (vehicles, approximate)                211
• Figure 51. Share of China's unmanned delivery vehicle market (2025–2026, approximate)      211
• Figure 52. Disclosed funding of selected autonomous ground delivery companies (US$ million, approximate) 218
• Figure 53. Estimated cost per on-demand delivery by delivery model, Western markets (US$, 2026) 221
• Figure 54. Estimated cost per parcel, China trunk-to-station leg (RMB per parcel)         222
• Figure 55. Positioning of leading autonomous ground delivery players (2026)   226
• Figure 56. Cumulative deliveries reported by leading drone delivery operators (thousands, latest disclosure to 2026)    233
• Figure 57. Taxonomy of delivery and cargo drone configurations 236
• Figure 58. Payload versus range of selected delivery and cargo drones (log scales; range is total one-way range or twice the service radius)          240
• Figure 59. Key regulatory and commercial milestones in drone delivery 249
• Figure 60. Maturity matrix for drone logistics segments and leading players       256
• Figure 61. Estimated cost per delivery by cost element, van versus drone at low and high density (US$, mid-point estimates) 261
• Figure 62. Disclosed cumulative funding of independent drone delivery and cargo companies (US$ million, estimated, to October 2026)             264
• Figure 63. Taxonomy of autonomous trucking operating models 271
• Figure 64. Hub-to-hub autonomous freight flow    272
• Figure 65. Estimated cumulative capital raised by autonomous freight developers (US$ million, to October 2026)              286
• Figure 66. Milestones in autonomous trucking commercialisation            288
• Figure 67. Per-mile cost of a US Class 8 long-haul truck: human-driven vs Level 4 (US$ per mile)        292
• Figure 68. Early-phase L4 cost per mile vs annual utilisation at three autonomy-fee levels (US$ per mile)                293
• Figure 69. Commercial readiness comparison of autonomous vehicle segments           296
• Figure 70. Technology maturity vs logistics opportunity for autonomous freight segments       297
• Figure 71. Expected autonomous trucking milestones, 2027–2040          300
• Figure 72. Major regulatory milestones for logistics robots, AVs and drones, 2023–2027           304
• Figure 73. EU conformity route for an AMR placed on the market after 20 January 2027             306
• Figure 74. Drone regulation timeline, 2016–2028  319
• Figure 75. Regulatory readiness matrix by country and segment, October 2026 (1 = no pathway, 5 = scalable framework) 325
• Figure 76. Industrial robot installations by country, 2025 (units) 331
• Figure 77. Regional regulatory and policy milestones for logistics autonomy, 2021–2027         344
• Figure 78. Regional maturity heatmap by logistics autonomy segment (2026)  346
• Figure 79. Competitive positioning of leading warehouse automation vendors (Future Markets assessment)  352
• Figure 80. Competitive positioning of outdoor autonomy operators (Future Markets assessment)     353
• Figure 81. Latest annual revenue of leading logistics automation companies (US$ million, converted at approximate 2025 average rates)      356
• Figure 82. Largest disclosed funding rounds in logistics mobile autonomy and adjacent physical AI, 2021–2026 (US$ million)        361
• Figure 83. Selected listings and listing milestones, 2024–2026   363
• Figure 84. Global market revenue, 2025–2040 (US$ billion, base case)  372
• Figure 85. Global market revenue by scenario, 2025–2040 (US$ billion) 373
• Figure 86. Global revenue by application group, 2026–2040 (US$ billion)             375
• Figure 87. Revenue share by application group, 2026 and 2040  376
• Figure 88. Global revenue by category, 2040 (US$ billion, base case)      379
• Figure 89. Revenue mix by category, 2026 vs 2040               380
• Figure 90. Intralogistics transport robots: revenue by category, 2025–2040 (US$ billion)           382
• Figure 91. Intralogistics transport robots: unit sales by category, 2025–2040 (thousand units)              382
• Figure 92. Mobile picking and handling robots: revenue by category, 2025–2040 (US$ billion)               384
• Figure 93. Mobile picking and handling robots: unit sales by category, 2025–2040 (thousand units)  385
• Figure 94. Last-mile delivery robots, vehicles and drones: revenue by category, 2025–2040 (US$ billion)                387
• Figure 95. Last-mile delivery robots, vehicles and drones: unit sales by category, 2025–2040 (thousand units)   387
• Figure 96. Level 4 autonomous trucks and middle-mile freight: revenue by category, 2025–2040 (US$ billion) 390
• Figure 97. Level 4 autonomous trucks and middle-mile freight: unit sales by category, 2025–2040 (thousand units)          391
• Figure 98. Global revenue by region, 2026–2040 (US$ billion)      393
• Figure 99. Regional revenue share, 2026 vs 2040 (%)          394
• Figure 100. Intralogistics and mobile picking robot revenue by end-use industry, 2026 and 2040 (US$ billion) 395
• Figure 101. Announced targets and expected milestones for logistics autonomy, 2027–2040 400
• Figure 102. The emerging autonomous freight chain           402
• Figure 103. Supplier opportunity matrix: maturity versus opportunity (Future Markets assessment) 406

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• Comprehensive Excel spreadsheet of all data including data on all investments & deals segmented by technology, sub-sector, government, region, and company
• Mid-year Update

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

PDF Download + Excel Database.

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

PDF Download + Excel Database + Print Edition (including tracked delivery).