Mobile Robots, Autonomous Vehicles, and Drones in
Logistics, Warehousing, and Delivery 2020-2040
Automated guided vehicles/carts; autonomous industrial material handling vehicles, goods-to-person robots, collaborative autonomous mobile robots, mobile picking robots, and sidewalk last mile delivery robots: technologies, markets, forecasts.
Automation in the delivery/logistic and warehousing/fulfilment chain is a growing market. A particularly exciting subset of this is the use of mobile robots, drones, and autonomous vehicles for automation of movement-based tasks. This field encompasses all manner of robots, drones, and autonomous vehicles, which help goods in their journey from origin to destination (see below). This report finds that the market for mobile robots, drones, and autonomous vehicles in delivery and warehousing is likely to reach a staggering $81 and $290 Billion in 2030 and 2040, respectively.
The report provides a comprehensive analysis of all the key players, technologies, and markets. It covers automated as well as autonomous carts and robots, automated goods-to-person robots, autonomous and collaborative robots, delivery robots, mobile picking robots, autonomous material handling vehicles such as tuggers and forklifts, autonomous trucks, vans, and last mile delivery robots and drones.
We provide technology roadmaps and twenty-year market forecasts, in unit numbers and revenue, for all the technologies outlined above (13 forecast lines). We built a twenty-year model because our technology roadmap suggests that these changes will take place over long timescales. In our detailed forecasts we clearly explain the different stages of market growth and outline the key assumptions/conditions as well as data points that underpin our model.
Furthermore, our granular forecast model includes price projections, often at component level, for all the technologies outlined above. Our technology assessments and price projections feed directly into our market forecast model, governing the adoption timescales and the estimated technology market share evolutions.
We further provide investment/trend analysis, always seeking to put each technology within its greater quantitative as well as qualitative context. We also include company interviews/profiles/reviews. Our company profiles and interviews provide valuable insight on company positioning, strategy, opportunities, and challenges
For a long time, automated guide carts and vehicles (AGC and AGV) have been in use. They are infrastructure dependent, meaning that they follow a fixed infrastructure, such as conductive wire or magnetic tape, in going from A to B. They are reliable and trusted to handle all manner of payloads. Their installation is however time-consuming, and their workflow is difficult to adapt.
Consequently, as a technology, they are on shaky ground, unless they adapt. This is because the technology is evolving towards more autonomous and infrastructure-independent navigation. We forecast that they will tend towards obsolescence and increasingly become confined to ever narrower market niches. Overall, we predict that their market will shrink by 50% in 2030 compared to the 2019 level.
A bright spot for automated robots
One very bright spot for automated robots is in goods-to-person automation within fulfilment centres. Special robot-only zones are created within warehouses in which these robot fleets move racks at high speeds to a manned picking station. The productivity gains are clear and proven.
This is a fast-growing market space. The landscape was set on fire when Amazon acquired Kiva Systems for $775M in 2012, thereby leaving a gap on the market. Today, significant well-funded alternatives such as GeekPlus (389$M), GreyOrange (170$M), and HIK Vision ($6Bn revenue) have emerged, achieving promising and growing deployment figures. The number of start-ups has also increased, especially between 2015-2017.
We forecast the annual unit sales to double within 6 years. Despite the large deployments already, we assess the real global inflection point to arrive around 2024. Indeed, our report forecasts that between 2020 and 2030, more than 1 million such robots will be sold accumulatively.
Towards autonomous indoor robots and vehicles
The navigation technology is transitioning from automated to autonomous. The primary benefit is that the navigation becomes infrastructure independent, allowing the workflow to be easily modified and enabling various modes of collaborative workflows between robots and humans.
The technology is enabled by better SLAM algorithms. The algorithms- based on different sensors including stereo camera and 2D lidars- are evolved enough to handle safe autonomous navigation within many structured indoor environments. These robots are easy to install and to train.
The technology options are still many. A common approach is to use 2D lidars to develop a map of the facilities during the training phase, e.g., walking the robot around the facilities. The fixed reference objects will be marked during the set-up phase. Another approach is to use camera vision and deep learning to also identify and classify objects. This is computationally more complex, but will enable a more flexible system that can have more intelligent decision making in complex and changing environments.
The business models are also various and evolving. Some are offering their technology as RaaS (robot as a service). Others are following a traditional model of equipment sales. Even these suppliers will also need to build in a subscription platform into their business model to offer maintenance and upgrades, especially cloud-based software updates.
We assess that the market for such autonomous mobile robots (AMRs) will grow. There have also been prominent investments and acquisitions this year, e.g., by Shopify and Amazon. Overall, we forecast that more than 200k robots could be sold within the 2020-2030 period (this figure includes those that can perform picking of regularly or irregularly shaped items).
Picking mobile robots to dominate AMRs?
Picking or grasping technology is an essential component of warehouse automation. Today, many firms and research groups are deploying deep learning to enable robots to rapidly pick novel and irregularly-shaped items with high success rates. To this end, various strategies to data collection/annotation and to DNN training are being followed.
A limited number of firms have integrated picking arms on mobile platforms. Today, these mainly picked box-shaped items in known environments. However, technology progress will bring these technologies to more varied items. It will also allow better integration of robotic arm with the mobile platform.
We forecast that picking mobile robots able to pick regularly shaped items will be in the learning and low volume deployment phase until 2024. Thereafter the sales will pick up. However, only after 2030 we forecast significant annual sales volumes. As for robots able to pick irregularly shaped items, we consider that the development and low-volume deployment phase could last until 2030. In the longer term though, we forecast that 36% and 38% of AMRs in warehouses sold in 2040 will be able to pick regular- as well as irregular-shaped items, respectively. This points towards a major technology transformation, requiring automation beyond just autonomy of movement.
Is the future of forklifts and tuggers autonomous?
Today, nearly all forklifts are manually operated. However, the rise of autonomous mobility technology will transform this trend. Indeed, the journey has already long begun. Many have developed, demonstrated, and deployed autonomous forklifts and trucks.
The choice of navigation technology is varied. Some use RGB camera and RGB image processing technologies to navigate. Others deploy 2D lidars for navigating in structured indoor environments. The merits and strategic consequences of each approach is assessed in the report.
The cost of these forklifts is naturally higher, but the claimed ROI by many suppliers is within 12-18 months. The cost includes the installation and maintenance cost as well as the cost of the autonomous sensor suite, traction control and drivers, and the software, which can be amortized over a growing deployed fleet. Overall, price parity on an annual operational cost basis is nearly at hand in some high wage territories.
Our analysis and interviews suggest that inflection point is likely to be reached around the 2025-2027. After this point, we project the sales to grow, already exceeding 100k units by 2030. Note that we generally develop 20-year forecasts for autonomous mobility as the technology will inevitable take time to be rolled out.
Sidewalk last mile delivery robots: a billion-dollar-market by 2030?
Last mile delivery is the most expensive part of the delivery chain, often representing more than 50% of the overall cost. In recent years, many companies have been innovating to utilize autonomous mobile robots, drones, and autonomous vehicle technology to automate this step. Examples of such products were shown in the image above.
The sidewalk robots are often designed to travel slowly at 4-6 km/hr. This is to increase safety, to give robots more thinking time, to give remote teleoperators the chance to intervene, and to enable categorising the robot as a personal device (vs. a vehicle), thus easing the legislative challenges.
These robots also come with various hardware choices, e.g., number of motor-controlled wheels, payload size and compartment design, battery size, etc. Almost all have HD cameras around the robot to give teleoperators the ability to intervene All also have IMUs and GPS and most have ultrasound sensors for near-field sensing.
A critical choice is whether to use lidar-only, stereo-vision-only, or hybrid. Lidar can give excellent 360deg ranging information with spatial resolution and a dense point cloud which enables good signal processing. Lidars however have are expensive and can have near-field (a few cm) blindspot. The first could jeopardize the business model unless lidar prices- as we have forecasted- fall. The other approach is to go lidar-free, using stereo camera as the main perception-for-navigation sensor. This will require the development of camera-based algorithms for localization, object detection, classification, semantic segmentation, and path planning.
These sidewalk robots are still far from being totally autonomous. First, they are often deployed in environments such as US university campuses where there is little sidewalk traffic and where the sidewalks are well-structured. Many robots are also restricted to daylight and perception-free conditions. Critically, the suppliers also have remote teleoperator centres. The ratio of operators to robots will need to be kept to an absolute minimum if such businesses are to succeed.
There is still much work to do to improve the navigation technology. The robots will need to learn to operate in more complex and varied environments with minimal intervention. Furthermore, capital is also essential. The end markets are also highly competitive, imposing tough price constraints.
In general, we forecast a 200k unit fleet size until 2035 (accounting for replacement). The inflection point will not occur until around 2025 period given the readiness level of the technology.
Autonomous Delivery Trucks and Vans
Autonomous mobility seeks to address several key painpoints facing the trucking industry. One is driver shortage. In the US alone, the driver shortage is estimated to reach 160k persons by 2028. The other is productivity. Currently, legislations limits drive time per driver per day, thus decreasing asset up time in favour of increasing safety.
Start-ups have raised more than $400M since late 2016 to enable autonomous trucks. Major firms such as Daimler have also announced significant investment plans ($500M) for the next five years. Many are now in the semi-commercial prototyping phase. Nonetheless, the number deployed autonomous trucks is currently low with the main players having a fleet of fifty or so. Next year we expect to see an expansion, bringing the total to 1200-1500 trucks. Nonetheless, these will also be in the trial phase, seeking to collect data to further improve the driving AI.
In general, highway driving in certain territories might offer a relatively low hanging fruit in terms of autonomous mobility technology. However, the technology will need to be specifically developed for trucks as these vehicles have different braking distances and different perception requirements.
The business models and exact approaches are in a state of flux. Some propose to rely only on cameras for navigation and to restrict autonomous driving to highways with humans or teleoperators taking over in complex urban environments. Others have a hybrid approach in which the camera scans the long-distance and lidars only focuses on objects of interest within the 150-200meter radius. The challenge for using lidar for very long-range detection is in power consumption and signal-to-noise ratio.
We forecast level-4 autonomous trucks to exceed 300k units per year by 2028. Within this period, level-5 deployments will remain nearly zero. By 2035, more than 800k and 170k of autonomous level-4 and level-5 trucks will be sold per year. Thereafter, we project deployments of level-5 autonomous trucks to rapidly take off. Our forecasts are in market share, unit numbers, and market value. The latter is also segmented by contribution of each autonomous mobility technology, e.g., lidar, radar, software, mapping, etc.
Autonomous delivery vans and pods are also emerging. Many are electrical built-to-purpose on-road vehicles. They are also a competitor to sidewalk last mile delivery robots. The sensor suite and algorithms are similar to autonomous cars although two considerations ease the technical burden: (1) delivery pods travel slowly therefore the perception technology does not need to accommodate as long a range and also the robot has more thinking and reaction time, and (2) the robots are likely to be deployed in limited known neighbourhood areas, thus allowing more detailed HD maps to be developed to aid autonomous navigation. Our report also provides forecasts for these delivery vans in unit number and value segmented by the level of autonomy.
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TABLE OF CONTENTS
1. EXECUTIVE SUMMARY
- 1.1. The big picture
- 1.2. Report scope
- 1.3. Report structure (with hyper-links)
- 1.4. Market and technology readiness by warehouse/logistics robotic activity
- 1.5. All twenty-year market forecasts segmented by category: AGVs, AGCs, autonomous mobile robots/carts, mobile picking robots, last mile delivery robots, delivery drones, autonomous trucks (level 4&5), and autonomous light delivery va
- 1.6. 20-year market forecasts for mobile robots in material handling covering mobile picking robots, autonomous mobile industrial vehicles, autonomous mobile cars, automated guided cards and automated guided vehicles
- 1.7. 20-year market forecasts for last mile delivery ground robots (droids) and drones
- 1.8. 20-year forecasts for level-4 and level-5 autonomous trucks, and delivery vans and pods
2. MOBILE ROBOTS IN MATERIAL HANDLING AUTOMATION
- 2.1. Different types of automated/autonomous mobile units
- 2.2. Automated Guide Vehicles & Carts (AGV/Cs)
- 2.3. Grid-based automated guided carts (AGC)
- 2.4. Autonomous Mobile Robots(AMRs)
- 2.5. Transition to AGVs and AMRs
- 2.6. How mobile robots find their way into indoor semi-structured spaces
- 2.7. Technology evolution towards fully autonomous independent mobile robots
- 2.8. AGVs vs. AMRs: an assessment and comparison
- 2.9. AGCs (KIVA-like systems) vs. AMRs: an assessment and comparison
- 2.10. Robotics as a service, just robot sales, or both?
- 2.11. Partnership with a forklift company creates a competitive advantage (why and how)
- 2.12. Hardware and software automation kit, components and trends
- 2.13. Employment figures as material handling operators and truck drivers
- 2.14. Total global addressable markets for 'driving services' in select commercial and industrial vehicles
- 2.15. The forklift market: historic unit sales, and segmentation by forklift type and end-use industry
- 2.16. Top 20 forklift suppliers' revenues (2014 and 2015)
- 2.17. Top 20 forklift suppliers' revenues (2017 and 2018)
- 2.18. Sales of top 20 material handling automation companies (2014, 2015, 2016)
- 2.19. Sales of top 20 material handling automation companies (2017 and 2018)
- 2.20. Rise of e-commerce seen in charts
- 2.21. Number of robotic companies in logistics mushrooms
- 2.22. Mobile robotics companies in the context of logistic and material handling automation
- 2.23. California emerges as a hotspot of AMR start-ups
- 2.24. Investment in logistics/warehouse mobile robotics heats up (2005-2019 statistics)
- 2.25. Existing AGV market projects by third parties: missing the mark
- 2.26. Market forecast for AGVs, AGCs, AMVs and AMCs in market value: a 20-year view in which AMV/Cs rise whilst AGVs go obsolete?
3. EXAMPLES OF AGVS AND AGCS
- 3.1. Traditional AGV/C suppliers and examples
- 3.2. Traditional AGV/C suppliers
- 3.3. Transbotics
- 3.4. Toyota: major player in AGCs?
- 3.5. Dematic: now the biggest AGV company after acquisition by Kion Group?
- 3.6. BA Systems: an established European AGV supplier
- 3.7. ASTI
4. EXAMPLES OF GRID-BASED AGCS
- 4.1. Kiva: the major success story whose acquisition left a massive market gap
- 4.2. Kiva(Amazon): Evolution to address issues since acquisition
- 4.3. Kiva(Amazon): Useful information and metrics
- 4.4. Geek+: Chinese response to Kiva's success?
- 4.5. Geek Plus: hardware, software, installation
- 4.6. Geek Plus: productivity, ROI, and servicing peak demand
- 4.7. Geek+: aggressive funding for an aggressive scale-up
- 4.8. Geek Plus: diversifying into forklifts
- 4.9. Grey Orange: an upcoming well-funded robotics company?
- 4.10. Grey Orange: updates
- 4.11. Flashhold (t/a Quicktron): Chinese warehouse mobile robots firm
- 4.12. Flashhold (t/a Quicktron) deployed in Alibaba's facilities
- 4.13. HIK Vision: largest supplier of AMRs in China?
- 4.14. InVia Robotics: a monthly subscription model for mobile robotic picking
- 4.15. InVia Robotics: updates
- 4.16. Scallog: A French take on the Kiva approach?
- 4.17. Eiratech robotics: a new comer in grid-based autonomous ground cards
- 4.18. Eiratech: background
- 4.19. Bionic HIVE: automated guided vehicle travelling up and across rails
- 4.20. Bleum Robotics (now Prime robotics)
- 4.21. Myrmex: full unmanned grocery delivery system automating the step from order fulfilment to order delivery
- 4.22. Malu Innovation: a Chinese solution provider
- 4.23. Malu Innovation: case studies
- 4.24. Beijing Ares Robot Technology Co., Ltd
- 4.25. Hitachi: Transitioning grid-based AGVs towards AMRs?
- 4.26. Market forecasts for goods-to-person robots
- 4.27. Goods-to-person robots: addressable market and annual deployments thus far
- 4.28. Goods-to-person robots: annual units number market forecasts (2020-2040)
- 4.29. Penetration rate of goods-to-person robots in the global market
- 4.30. Goods-to-person robots: annual value market forecasts (2020-2040)
5. EXAMPLES OF COLLABORATIVE AUTONOMOUS MOBILE ROBOTS (C-AMRS)
- 5.1. Swisslog (Kuka): a pivot towards logistics with a high payload AGC?
- 5.2. Knapp: Flexible independent transport shuttle robot
- 5.3. Omron Adept Mobile Robotics
- 5.4. Omron Adept Mobile Robotics: sensor suite and navigation tech
- 5.5. Mobile Industrial Robots: A flexible autonomous mobile robotic transporter
- 5.6. Locus Robotics: a mobile robot in response to the shortcomings of KIVA?
- 5.7. Locus Robotics: sensor suites in autonomous collaborative pick-assist mobile robot in warehouse logistics
- 5.8. Canvas Technologies (now Amazon): truly autonomous vision-based autonomous mobile robots?
- 5.9. Canvas Technology (now Amazon): vision based navigation technology
- 5.10. 6 River System Inc (now Shopify): a mobile co-working robot that guides the human picker?
- 5.11. 6 River Systems (now Shopify): updates
- 5.12. Otto Motors: autonomous driverless warehouse robots
- 5.13. MiR robots: the sensor suite in its autonomous mobile robotic transports/carts
- 5.14. Fetch Robotics: well-funded CA start-up focused on mobile robots in warehouses
- 5.15. Fetch Robotics: the sensor suite in its autonomous mobile robotic transports/carts or pick-assist AMR
- 5.16. Fetch Robotics: updates
- 5.17. Fetch Robotics: expanding product portfolio
- 5.18. Fetch Robotics: fetching arm
- 5.19. InclubedIT: Austrian software licensor?
- 5.20. Syrius Robotics
- 5.21. Caja Systems: a new Isreali supplier on the scene
- 5.22. NextShift Robotics
- 5.23. Alog Tech: developing the software layers
- 5.24. Gebhardt
- 5.25. iFuture Robotics
- 5.26. Intelligent Robotics
- 5.27. Berkshire Grey: complete solution from automated picking to transporting
- 5.28. CTRLWORKS: small autonomous mobile robotic carrier platform
- 5.29. Hstar Technologies: agile mobile platforms for logistics
- 5.30. Neobotix: A German specialist player?
- 5.31. Anronaut GmbH
- 5.32. Market forecast (2020-2040) collaborative autonomous mobile robots
- 5.33. Market forecast (2020-2040) collaborative autonomous mobile robots: unit number forecasts
- 5.34. Automated Storage and Retrieval System
- 5.35. AutoStore: 3D robot-on-rail retrieval system
- 5.36. Ocado: transitioning into the automation business?
- 5.37. Exotec Solutions: small light-weight mobile transporter robot for small warehouse operators?
- 5.38. Exotec Solutions: sensor suite
- 5.39. Symbotic: mobile robots to maximising space utilization in vertical warehouses?
6. AUTONOMOUS MOBILE PICKING ROBOTS
- 6.1. Shift in algorithms from deterministic to perception-driven open ups robotic picking
- 6.2. Stanford: novel object grasping using supervised learning by synthetic data
- 6.3. Berkley: cloud robotics to estimate grasp pose
- 6.4. Cornell: rapid deep-learning based approach for grasping
- 6.5. Google: large-scale data collection with deep learning for object grasping
- 6.6. Berkeley: multi-fingered robotic picker trained using virtual data
- 6.7. Right Hand Robotics: using the cloud to train the hand
- 6.8. RightHand Robotics: update
- 6.9. Kindered.ai: reinforcement learning for robotic grasping and pick-and-place
- 6.10. Soft robotics: an instruction-free game changing hardware solution to grasping irregular objects that alleviate the grasping intelligence challenge?
- 6.11. Soft robotics gripper technologies
- 6.12. Further examples of soft robotic based gripper technologies
- 6.13. From stationary to mobile robotic picking
- 6.14. Picking robots: addressable market forecasts
- 6.15. Mobile picking robots: market forecasts (2020-2040) in unit numbers
- 6.16. Mobile picking robots: market forecasts (2020-2040) in market value
7. EXAMPLES OF AUTONOMOUS MOBILE PICKING ROBOTIC COMPANIES
- 7.1. InVia Robotics: a monthly subscription model for mobile robotic picking
- 7.2. IAM Robotics
- 7.3. IAMROBOT: updates
- 7.4. IAMROBOT: technology choices
- 7.5. Magazino: implementing perception-driven algorithms to enable mobile picking?
- 7.6. Magazino: update
- 7.7. Magazino: software stack
- 7.8. Boston Dynamics
- 7.9. Servus: a simple picking robotic shuttle?
- 7.10. Plus One Robotics: When will it emerge from its total stealth mode?
8. EXAMPLES OF AMVS INCLUDING HIGH PAYLOAD
- 8.1. SeeGrid: high-load flexible autonomous industrial trucks based on stereo vision
- 8.2. SeeGrid's autonomous forklift: the ensor suite
- 8.3. SeeGrid: recent updates
- 8.4. SeeGrid: recent updates
- 8.5. Baylo: a leading supplier autonomising material handling vehicles
- 8.6. Baylo: navigation technology
- 8.7. Sensor suite: autonomous material handling forklift and trucks
- 8.8. Balyo: high-load flexible autonomous industrial trucks
- 8.9. Vecna Technologies: offering full spectrum of robotic solutions as no solution fits all in warehouse automation?
- 8.10. Sensor suite in autonomous tugger
- 8.11. Sensor suite in autonomous lifter
- 8.12. Vecna: recent updates
- 8.13. RoboCV: converting standard forklifts into autonomous ones
- 8.14. RoboCV forklifts: the sensor suite
- 8.15. Kollmorgen: autonomous navigation kit to convert existing vehicles?
- 8.16. AutoGuide Mobile Robots
- 8.17. HIK Vision: largest supplier of AMRs in China?
- 8.18. The forklift market: historic unit sales, and segmentation by forklift type and end-use industry
- 8.19. Top 20 forklift suppliers' revenues (2014 and 2015)
- 8.20. Top 20 forklift suppliers' revenues (2017 and 2018)
- 8.21. Unit sales forecasts (2020-2040): autonomous forklifts
- 8.22. Annual sales of autonomous forklifts vs forklift fleet size
- 8.23. Autonomous forklift cost forecast (2020-2040) by hardware, software, installation, maintenance, and base vehicle
- 8.24. Estimating the point of parity between autonomous and manual forklifts
- 8.25. Market forecasts (2020-2040) for autonomous forklifts
9. AUTONOMOUS TRUCKING
- 9.1. Pain points in the trucking industry
- 9.2. Why autonomous trucks?
- 9.3. Automation levels of trucking explained
- 9.4. Funding race for autonomous truck start-ups
- 9.5. Announced deployment of L4+ autonomous trucks
- 9.6. Major stakeholders in autonomous trucking
- 9.7. Market readiness level of L4+ autonomous truck companies
- 9.8. Evolving autonomous applications for trucks
- 9.9. What is truck platooning?
- 9.10. Peloton: V2V platooning system
- 9.11. TuSimple - the Chinese autonomous trucking unicorn
- 9.12. Sensor architecture of TuSimple's autonomous trucks
- 9.13. TuSimple's unique perception solution
- 9.14. TuSimple's enhanced night vision camera system
- 9.15. Business landscape for TuSimple
- 9.16. Embark's hybrid approach to autonomous trucking
- 9.17. Embark: Hybrid approach for autonomous truck driving on highways
- 9.18. Starsky's teleoperation-assisted autonomous solution
- 9.19. Teleoperation for autonomous trucks
- 9.20. Starsky Robotics: going lidar-free with radar, camera, and remote human intervention
- 9.21. Ike's highway-specific autonomous trucking solution
- 9.22. T-Pod: Einride's cab-less, electric and autonomous truck
- 9.23. A closer look into the T-pod and T-log
- 9.24. Sensor architecture of T-pod and T-log
- 9.25. Kodiak's long-haul trucking specific autonomous system
- 9.26. PlusAI supported by the Uber of China's trucking industry
- 9.27. FABU's tailored AI platform for autonomous trucks
- 9.28. Volvo Truck's road to autonomous
- 9.29. Daimler's half-billion investment in autonomous trucks
- 9.30. Waymo Driver in autonomous trucks
- 9.31. Hyundai catching up the autonomous trucking race
- 9.32. Baidu: becoming the Android of autonomous vehicles
- 9.33. Market forecast
- 9.34. Market share forecast for autonomous trucks 2020-2040
- 9.35. Global autonomous trucks market forecast 2020-2040 (thousand unit)
- 9.36. Global autonomous trucks market forecast 2020-2040 ($M)
- 9.37. Forecast (2020-2040) of the cost of autonomy system segmented by lidar, radar, camera, software, maps, and other hardware
- 9.38. Historical price evolution for cameras, primary/secondary memory, computing and photovoltaics
- 9.39. Goods delivery vans or pods
- 9.40. Categorization
- 9.41. Boxbot: autonomous last-mile delivery solution
- 9.42. 'Middle mile' delivery gaining attention - Gatik
- 9.43. Ford: trials on autonomous delivery vans
- 9.44. Ford: adding legs to its autonomous delivery vans
- 9.45. Nuro: the unicorn in autonomous delivery?
- 9.46. Sensor suite
- 9.47. Neolix: the Chinese startup riding on the e-commerce boom
- 9.48. Neolix kicked off mass production
- 9.49. Toyota's e-Palette platform
- 9.50. MONET Technologies: on-demand mobility services
- 9.51. Teleretail: targeting rural delivery with long-rage last mile delivery robots?
- 9.52. Alibaba: autonomous on-road delivery vans and pods
- 9.53. Oxbotica & Ocado test autonomous light deliver vans
- 9.54. Delivery vans: addressable market forecast
- 9.55. Unit number market forecasts (2020-2040): autonomous package delivery vans and pods
- 9.56. Market forecasts (2020-2040): autonomous package delivery vans and pods
10. AUTONOMOUS LAST MILE SIDEWALK DELIVERY (GROUND BASED)
- 10.1. Last mile delivery: why motivates the robotic companies
- 10.2. Last mile delivery: large market being technologically disrupted?
- 10.3. Market and technology readiness levels of different technologies seeking to impact last mile delivery
- 10.4. Starship Technologies: strongly-funded last-mile delivery robot?
- 10.5. Starship Technologies: updates
- 10.6. Starship Technologies: technology choices
- 10.7. Starship Technologies: navigation technology
- 10.8. Marble: last-mile delivery robot for more crowded neighbourhoods?
- 10.9. Marble: updates
- 10.10. Marble: navigation technology (lidar + 3D camera)
- 10.11. Marble: navigation technology (semantic classification)
- 10.12. Marble: navigation technology
- 10.13. KiwiBot: using camera vision to navigate?
- 10.14. Dispatch (now Amazon?): last mile delivery starting in San Francisco?
- 10.15. Dispatch: updates
- 10.16. Amazon: launching last mile sidewalk delivery robot
- 10.17. Robby Technologies: camera-based long-range day-and-night delivery robot?
- 10.18. Robby Technologies: navigation
- 10.19. Refraction ai: last-mile side-walk robotic delivery
- 10.20. Segway: moving into indoor and outdoor robotic delivery
- 10.21. Continental: last mile delivery side-walk robot?
- 10.22. Marathon Technologies: Last mile pizza delivery demonstrated
- 10.23. SideWalk Delivery: Pivoting towards a mobile vending machine?
- 10.24. Alibaba: the e-commerce giant developing its own last-mile mobile robot?
- 10.25. JD: foraying into last mile delivery robots with large droids?
- 10.26. TwinsWheel: fast last-mile delivery robot or a gimmick?
- 10.27. Twinwheel: update
- 10.28. ZMP: last-mile side-walk robotic delivery
- 10.29. Meituan-Dianping: last mile side-walk delivery
- 10.30. Zhen Robotics
- 10.31. DroneFutureAviation (DFA) Co., Ltd
- 10.32. Piaggio: autonomous load-carrying follow-me robot?
- 10.33. Analysis: cost competitiveness of droids a function of unit cost and fleet size
- 10.34. Parcel market forecasts (2013 to 2028 data) segmented by region in value with value of last mile delivery demonstrated
- 10.35. Estimated global wage bill spent on human-provided driving services in the delivery sector
- 10.36. How warehouse infrastructures goes de-centralized to adapt to e-commerce needs?
- 10.37. Total addressable market for ground-based delivery: 20-year forecasts in unit numbers and dollars
- 10.38. Market forecasts for ground-based delivery droids: a 20-year analysis in unit numbers and dollars
- 10.39. Sidewalk robots delivery services market forecasts (2020-2040)
- 10.40. Schematically visualization of the delivery process
11. AUTONOMOUS LAST MILE DRONE DELIVERY (AIR BASED)
- 11.1. Delivery and Logistics: past, present and future
- 11.2. Why drone-based last mile delivery?
- 11.3. What different companies think about drone-based delivery?
- 11.4. Trends in charts: rise of e-commerce, fall of department stores, and accelerated closure of commercial real estates
- 11.5. Drones: dominant designs begin to emerge
- 11.6. Drones: moving past the hype? Investment data from 2010 to 2016
- 11.7. Drones: platforms commoditize?
- 11.8. Drones: market forecasts
- 11.9. IDTechEx Market Forecasts
- 11.10. Drones: application pipeline and hype curve
- 11.11. Software opportunities: Vertical focused actionable analytics
- 11.12. Drones: increasing autonomy
- 11.13. Hardware opportunity: specialized sensors
- 11.14. Development timeline of drone use in last mile or remote area delivery
- 11.15. Amazon: Will it make drones as common as mail trucks in the future?
- 11.16. Matternet: transition from humanitarian remote delivery to commercial delivery in Europe?
- 11.17. Zipline: fix-wing drones making medicine delivery to remote areas
- 11.18. DJI: product portfolio diversifies
- 11.19. The rise of DJI to over two billion sales
- 11.20. DJI: IDTechEx assessment
- 11.21. Yuneec pivots back to UAV seeking to be the best alternative supplier?
- 11.22. Yuneec: product portfolio
- 11.23. Yuneec: IDTechEx assessment
- 11.24. Parrot: re-focusing onto the commercial sector
- 11.25. Parrot: prosumer range born from its consumer drones
- 11.26. Parrot revenue rise and fall tells the story of the drone market
- 11.27. Parrot: positioning for future growth with acquisitions and equity investments?
- 11.28. Parrot: IDTechEx assessment
- 11.29. Key drone suppliers: DelairTech
- 11.30. DelairTech's small but increasing turnover
- 11.31. Market forecast methodology
- 11.32. Market forecast (2020-2040) for drone airframe in logistics and delivery