DESCRIPTION
Mobile robotics in material handling and logistics will become a $75bn market by 2027
Mobile robotics in material handling and logistics will become a $75bn market by 2027. It will then more than double by 2038. These staggering headline figures mask turbulent transformative change underneath: some technologies will rise and transform the fortunes of industries, fuelling growth rates far outpacing recent trends, whilst others will face with decay and obsolesce. We are at the beginning of the beginning of a transformative change, and the time to plan is now. The images below demonstrate this point.
This report is focused on all aspects of mobile robotics in material handling and logistics. In particular, we consider the following: automated guided vehicles and carts (AGVs and AGCs); autonomous mobile vehicles and carts/units; mobile picking robots; last mile delivery ground robots (droids) and drones; and autonomous trucks and light delivery vans (level 4 and level 5 automation).
We provide technology roadmaps and twenty-year market forecasts, in unit numbers and revenue, for all the technologies outlined above (12 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.
Our model also considers how technology improvements will increase productivity and/or performance levels, expanding potential target markets or meeting competitive threshold levels with time, and thus raising market adoption. It will also consider how some technologies will lose their value add to emerging technologies, thus facing obsolescence.
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
These figures show a short-term as well as a long-term view of the market evolution. Each colour refers to a different technology, demonstrating how the market composition will completely change in the coming years and how technology improvement (rise of autonomous mobile robots) will bring significant new revenue into the industries considered. We are at the beginning of the beginning of a transformative change, and the time to plan is now. This figure includes automated guided vehicles and carts (AGVs and AGCs); autonomous mobile vehicles and carts/units; mobile picking robots; last mile delivery ground robots (droids) and drones; and autonomous trucks and light delivery vans (level 4 and level 5). Note that the headline figure quoted above is at the level of complete autonomous vehicle. We also provide forecasts at the automation-only level where appropriate (e.g., autonomous trucks).
Incumbents face obsolescence?
AGVs are a mature technology that can safely transport payloads ranging from several Kg to multiple tonnes, essentially acting as semi-rigid distributor conveyer belts covering large areas. Their navigation technology is evolving. Today multiple options are available ranging from the low-cost wire or magnetic tape guidance to the increasingly popular laser guidance. All however requires follow rigid guide points, thus requiring some degree of infrastructure modification and extended onsite installation. This industry is showing healthy, albeit small, grow rates.
This gives an illusion of security to this mature highly-fragmented business where price competition is rise. The next generation of navigation technology, i.e., infrastructure-independent flexible autonomy, may appear as just as the next natural step in navigation technology evolution. It however has the potential to shatter this illusion and fully redraw the competitive landscape.
This report provides a detailed and quantitative (revenue and unit numbers) assessment, forecasting how sales of AGVs will grow then decline in the next twenty-years. In addition, it will show how autonomous mobile robots (AMRs) will rise, not just largely replacing AGVs but in time diffusing beyond the structured confines of warehouses and factories.
Forklifts will never be the same?
Navigational autonomy will induce a colossal transfer of value from wage bills paid for human-provided driving services towards spent on autonomous industrial vehicles. This, in turn, will fuel the growth in this material handling vehicle industry (e.g., forklift), creating significant revenues over a business-as-usual scenario. This is despite our technology roadmap showing that hardware commoditisation will slowly devalue such driving services particularly in high-wage regions.
AGVs barely made a dent in this industry. This is because their navigational rigidity put a low cap on their total market scope, keeping them as a small subset of the warehouse/factory automation business. Autonomous mobile robots are radically different however because they will ultimately enable automation to largely keep the flexibility and versatility of human-operated vehicles.
Our technology roadmap suggests that this change will not happen overnight. It will nonetheless take place much earlier than mobile autonomy in general driving since the structured and controlled environment of indoor industrial facilities lends itself better to automation.
Indeed, our model suggests that autonomous forklifts, for example, will remain a tiny share of the global addressable market until around 2023 but will soon after enter the rapid growth phase, causing a transformation of the industry and dramatically raising adoption levels to as high as 70% by 2038. This trend may not yet be on investor presentations of big, say, forklift suppliers, but will inevitable rise up the agenda as a key feature of the industry for years to come.
Mobile picking robots will learn, fast
Mobile robotic picking is generally restricted to stationary robotic arms operating on known objects in controlled environments. The artificial intelligence technology for robotic grasping is however changing with a transition taking place from deterministic scripting towards perception-driving learning.
Today companies are actively generating data to train their robots, and hope to utilize the cloud to rapidly share data and learnings at scale across distributed fleets. The latter will particularly help energy-constrained mobile picking robots because it partially transitions the computational burden to the cloud.
In parallel, soft robotic technology is offering innovative end effector designs that can adapt their shape- without computer guidance/instruction- to the target object. For certain objects and cases, this will dramatically ease the intelligence/computational challenge in picking (identifying objecting, recognising optimal grasp point, developing approach path, etc).
Our report shows how mobile picking units will evolve through different levels of performance (sub-human, approaching human, and potentially exceeding human) over a twenty-year period for both regular and irregular/mixed shaped items. We provide forecast in unit sales as well as revenue.
Disrupting the last mile delivery using mobile ground robots
Last mile delivery remains an expensive affair in the parcel delivery business, often representing more than half of the total cost. Its importance is also growing thanks to a change in the composition of total deliveries with B2C deliveries rapidly taking on a bigger share. E-commerce companies are also pushing next-day and now same-day services hoping to take away that last stronghold of bricks-and-mortar shops: instant customer fulfilment.
Autonomous mobile delivery robots are currently small slow-moving units that will need to return to base to charge. They often need close supervision and can only operate in sparsely-populated and highly-structured environments such as university campuses or special neighbourhood. They therefore are unproductive and easy to dismiss as gimmicks.
This is however only the beginning of the beginning. Our cost projections in the report suggest that these mobile robots can indeed become low-cost. The robots are now in the trial and learning phase, gathering more data and optimising the navigational algorithms. They will become increasingly more adept at path planning, even when GPS signals fail, and at object avoidance. The increased autonomous mobility capability will in turn enable a lower operator-to-fleet-size ratio, furthering boosting overall fleet productivity.
This report paints a quantitative picture of the emergence of last mile delivery mobile robots, clearly explaining the different phases of evolution from trial/early commercial sales toward rapid market penetration and finally towards maturity and then revenue decline, i.e., our model shows that hardware commoditization outpaces volume growth.
Delivery drones: publicity stunt or a game changer in instant fulfilment?
The idea of drone delivery sharply divides commentator opinion: some dismiss it as a mere publicity stunt whilst others consider it a game changer that will bring near instant product fulfilment to e-commerce, stripping traditional shops of their last major differentiator.
Drone delivery faces critical challenges. Individual drones offer limited productivity compared to traditional means of delivery. They can only carry small payloads and battery technology limits their flight duration, constraining them to around 30min radius of their base whilst further lowering their productivity due to the downtime needed for re-charging/re-loading. Safety is a potential showstopper with many accidents waiting to happen.
Drone delivery however is still in its infancy. Its short-term potential, we find, has been exaggerated. However, the technology has long-term future, particularly within the context of the bigger trend to automate as much as of the logistic chain as possible.
Indeed, we find that delivery drone sales will remain limited until 2027/28. Demand will then start to taking off in remote or sparsely-populated (e.g., suburbs), ultimately enabling companies to establish large accumulated fleets. Despite their ultimate rise, however, drone delivery will remain only a small part of the much bigger commercial drone story.
Trucking: a large attractive business to autonomize?
Trucking is a big business. In the US, the trucking industry revenues are in excess of $726bn. This is the equivalent of combined revenues of Apple, Amazon, Google, Microsoft, IBM, Baidu and then some (a lot) more. It is also a big employer: the US Bureau of Labour Statistics suggests that 1.79m people work in this sector driving 7.2m trucks for inter-city freight transport earning an average salary of 41.3 k$/year. No wonder this is a hot topic now then.
Trucking is also potentially an easier target than general passenger cars. This is because it spends much of its time in intercity roads which are less congested and less sinuous than city ones. The driver may remain in the vehicle, but the commercial incentive, even in this hybrid approach, exists because it may justify a relaxation of the rulebook which limits driving hours. This can therefore boost driver productivity and asset utilization.
In this report we forecast how different levels of automation (level 4 and 5) will rise and fall in trucking over the next twenty years. We offer detailed projections for the future cost of automation hardware systems (Lidar, radar, IMU, GPS, PC, etc) based on historical learning curves of similar technologies. Our forecasts are expressed in unit numbers as well as in value- both at the level of the truck itself and the automation part.
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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
2. MOBILE ROBOTS IN MATERIAL HANDLING AUTOMATION
2.1. Overview
2.1.1. Different types of automated/autonomous mobile units
2.1.2. Automated Guide Vehicles & Carts (AGV/Cs)
2.1.3. Grid-based automated guided carts (AGC)
2.1.4. Autonomous Mobile Robots(AMRs)
2.1.5. Transition to AGVs and AMRs
2.1.6. How mobile robots find their way into indoor semi-structured spaces
2.1.7. Technology evolution towards fully autonomous independent mobile robots
2.1.8. AGVs vs. AMRs: an assessment and comparison
2.1.9. AGCs (KIVA-like systems) vs. AMRs: an assessment and comparison
2.1.10. Robotics as a service, just robot sales, or both?
2.1.11. Partnership with a forklift company creates a competitive advantage (why and how)
2.1.12. Hardware and software automation kit, components and trends
2.1.13. Employment figures as material handling operators and truck drivers
2.1.14. Total global addressable markets for 'driving services' in select commercial and industrial vehicles
2.1.15. The forklift market: historic unit sales, and segmentation by forklift type and end-use industry
2.1.16. Top 20 forklift suppliers' revenues
2.1.17. Sales of top 20 material handling automation companies
2.1.18. Rise of e-commerce seen in charts
2.1.19. Number of robotic companies in logistics mushrooms
2.1.20. Mobile robotics companies in the context of logistic and material handling automation
2.1.21. California emerges as a hotspot of AMR start-ups
2.1.22. Investment in logistics/warehouse mobile robotics heats up (2005-2017 statistics)
2.1.23. Existing AGV market projects by third parties: missing the mark
2.1.24. Market forecast for AGVs, AGCs, AMVs and AMCs in market value: a 20-year view in which AMV/Cs rise whilst AGVs go obsolete?
2.1.25. Market forecast for AGVs, AGCs, AMVs and AMCs in unit numbers: a 20-year view in which AMV/Cs rise whilst AGVs go obsolete?
2.1.26. The rise of autonomous forklifts fuelling rapid growth: a 20-year view
2.1.27. The rise of autonomous forklifts: 20-year market forecasts where equipment suppliers capture the value of driving services?
2.1.28. Market projections: future cost evolutions for autonomous vs automated vs manual systems
2.2. Examples of AGVs and AGCs
2.2.1. Traditional AGV/C suppliers and examples
2.2.2. Toyota: major player in AGCs?
2.2.3. Dematic: now the biggest AGV company after acquisition by Kion Group?
2.2.4. BA Systems: an established European AGV supplier
2.3. Examples of grid-based AGCs
2.3.1. Kiva: the major success story whose acquisition left a massive market gap
2.3.2. Geek+: Chinese response to Kiva's success?
2.3.3. Flashhold: the best funded Chinese warehouse mobile robot?
2.3.4. Grey Orange: India's largest robotics company?
2.3.5. Scallog: A French take on the Kiva approach?
2.4. Examples of AMVs including high payload
2.4.1. SeeGrid: high-load flexible autonomous industrial trucks based on stereo vision
2.4.2. Balyo: high-load flexible autonomous industrial trucks
2.4.3. Vecna Technologies: offering full spectrum of robotic solutions as no solution fits all in warehouse automation?
2.4.4. RoboCV: converting standard forklifts into autonomous ones
2.4.5. RoboCV: converting standard forklifts into autonomous ones
2.4.6. Kollmorgen: autonomous navigation kit to convert existing vehicles?
2.5. Examples of AMRs including small payload
2.5.1. Swisslog (Kuka): a pivot towards logistics with a high payload AGC?
2.5.2. Knapp: Flexible independent transport shuttle robot
2.5.3. Omron Adept Mobile Robotics
2.5.4. Mobile Industrial Robots: A flexible autonomous mobile robotic transporter
2.5.5. Locus Robotics: a mobile robot in response to the shortcomings of KIVA?
2.5.6. Otto Motors: autonomous driverless warehouse robots
2.5.7. Fetch Robotics: well-funded CA start-up focused on mobile robots in warehouses
2.5.8. 6 River System Inc: a mobile co-working robot that guides the human picker?
2.5.9. InclubedIT: Austrian software licensor?
2.5.10. Exotec Solutions: small light-weight mobile transporter robot for small warehouse operators?
2.5.11. Hitachi: Transitioning grid-based AGVs towards AMRs?
2.5.12. CTRLWORKS: small autonomous mobile robotic carrier platform
2.5.13. Hstar Technologies: agile mobile platforms for logistics
2.5.14. Symbotic: mobile robots to maximising space utilization in vertical warehouses?
2.5.15. Neobotix: A German specialist player?
2.5.16. Anronaut GmbH
3. AUTONOMOUS MOBILE PICKING ROBOTS
3.1. Shift in algorithms from deterministic to perception-driven open ups robotic picking
3.2. Stanford: novel object grasping using supervised learning by synthetic data
3.3. Berkley: cloud robotics to estimate grasp pose
3.4. Cornell: rapid deep-learning based approach for grasping
3.5. Google: large-scale data collection with deep learning for object grasping
3.6. Berkeley: multi-fingered robotic picker trained using virtual data
3.7. Right Hand Robotics: using the cloud to train the hand
3.8. Soft robotics: an instruction-free game changing hardware solution to grasping irregular objects that alleviate the grasping intelligence challenge?
3.9. Soft robotics gripper technologies
3.10. Further examples of soft robotic based gripper technologies
3.11. From stationary to mobile robotic picking
3.12. Mobile robotic picking market forecasts for regular- and irregular-shaped objects (unit numbers)
3.13. Mobile robotic picking market forecasts for regular- and irregular-shaped objects (market value)
4. EXAMPLES OF AUTONOMOUS MOBILE PICKING ROBOTIC COMPANIES
4.1. InVia Robotics: a monthly subscription model for mobile robotic picking
4.2. Magazanio: implementing perception-driven algorithms to enable mobile picking?
4.3. IAM Robotics
4.4. Servus: a simple picking robotic shuttle?
4.5. Plus One Robotics: When will it emerge from its total stealth mode?
5. AUTONOMOUS TRUCKING
5.1. The economic case for autonomous trucks
5.2. Automation levels in trucking explained
5.3. Uber acquiring Otto sets the scene on fire?
5.4. Embark: Hybrid approach for autonomous truck driving on highways
5.5. Starsky Robotics: retrofitting existing trucks and making them remote-controlled
5.6. Baidu: becoming the Android of autonomous vehicles
5.7. TuSimple: Chinese Al provider for level-4 autonomous trucks
5.8. Pelton: V2V links to enable closer platooning
5.9. Market forecasts for autonomous trucking: a 20-year view for level-4 and level-5 automation (market share as % total truck unit sales)
5.10. Ten-year and twenty-year component-segmented price projections for hardware for autonomous mobility
5.11. Historical price evolution for cameras, primary/secondary memory, computing and photovoltaics
5.12. Market forecasts for autonomous trucking: a 20-year view for level-4 and level-5 automation (in unit numbers and dollars)
5.13. Market forecasts for autonomous trucking: a 20-year view for the value of automation hardware/components
6. AUTONOMOUS LAST MILE DROID DELIVERY (GROUND BASED)
6.1. Last mile delivery: why motivates the robotic companies
6.2. Last mile delivery: large market being technologically disrupted?
6.3. Market and technology readiness levels of different technologies seeking to impact last mile delivery
6.4. Starship Technologies: strongly-funded last-mile delivery robot?
6.5. Marble: last-mile delivery robot for more crowded neighbourhoods?
6.6. Dispatch: last mile delivery starting in San Francisco?
6.7. Marathon Technologies: Last mile pizza delivery demonstrated
6.8. SideWalk Delivery: Pivoting towards a mobile vending machine?
6.9. Alibaba: the e-commerce giant developing its own last-mile mobile robot?
6.10. TwinsWheel: fast last-mile delivery robot or a gimmick?
6.11. DJ: foraying into last mile delivery robots with large droids?
6.12. Teleretail: targeting rural delivery with long-rage last mile delivery robots?
6.13. Piaggio: autonomous load-carrying follow-me robot?
6.14. Analysis: cost competitiveness of droids a function of unit cost and fleet size
6.15. Parcel market forecasts (2013 to 20028 data) segmented by region in value with value of last mile delivery demonstrated
6.16. Estimated global wage bill spent on human-provided driving services in the delivery sector
6.17. How warehouse infrastructures goes de-centralized to adapt to e-commerce needs?
6.18. Total addressable market for ground-based delivery: 20-year forecasts in unit numbers and dollars
6.19. Market forecasts for ground-based delivery droids: a 20-year analysis in unit numbers and dollars
6.20. Schematically visualization of the delivery process
6.21. Oxbotica & Ocado test autonomous light deliver vans
6.22. Market forecasts for level-4 and level-5 autonomous light vans: a 20-year view in unit numbers
6.23. Market forecasts for level-4 and level-5 autonomous light vans: a 20-year view in market value
7. AUTONOMOUS LAST MILE DRONE DELIVERY (AIR BASED)
7.1. Why drone-based last mile delivery?
7.2. What different companies think about drone-based delivery?
7.3. Trends in charts: rise of e-commerce, fall of department stores, and accelerated closure of commercial real estates
7.4. Drones: dominant designs begin to emerge
7.5. Drones: moving past the hype? Investment data from 2010 to 2016
7.6. Drones: platforms commoditize?
7.7. Drones: market forecasts
7.8. Drones: application pipeline and hype curve
7.9. Software opportunities: Vertical focused actionable analytics
7.10. Drones: increasing autonomy
7.11. Hardware opportunity: specialized sensors
7.12. Development timeline of drone use in last mile or remote area delivery
7.13. Amazon: Will it make drones as common as mail trucks in the future?
7.14. Matternet: transition from humanitarian remote delivery to commercial delivery in Europe?
7.15. Zipline: fix-wing drones making medina delivery to remote areas
7.16. Drone delivery: 20-year market forecast for the rise of UAVs in unit numbers and market value
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