THE EVOLVING RELATIONSHIP BETWEEN DRONES, MOBILE ROBOTS, AUTONOMOUS VEHICLES AND LOGISTICS - Global Trade Magazine
  August 17th, 2020 | Written by

THE EVOLVING RELATIONSHIP BETWEEN DRONES, MOBILE ROBOTS, AUTONOMOUS VEHICLES AND LOGISTICS

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  • The drone industry has grown to a more than $1.5 billion industry.
  • The market will grow and those who plant their seeds today will reap the benefits tomorrow.

Last mile delivery is the most expensive part of the delivery chain, often representing more than 50 percent of the overall cost. This is mainly because it is the least productive and automated step. As such, many are seeking to bring automation into the last mile. In recent years, many companies around the world have been innovating to utilize autonomous mobile robots, drones, and autonomous vehicle technology.

Various autonomous robots and vehicles (sometimes called pods) are being developed around the world. These come in a variety of shapes and forms, reflecting the diversity and breadth of design and technology choices which must be made to create such products.

Drone Delivery: a Game Changer in Instant Fulfilment?

My new IDTechEx report, “Mobile Robots, Autonomous Vehicles, and Drones in Logistics, Warehousing, and Delivery 2020-2040,” covers the use of mobile robots, drones, and autonomous vehicles in delivery, warehousing and logistics—and suggests these could create a $1 billion market by 2030. That shows how far we have come since a previous IDTechEx report, “Mobile Robots and Drones in Material Handling and Logistics 2017-2037,” which analyzed the technologies that were then emerging in the last mile delivery space, including drones and autonomous mobile ground robots (or droids).

Several players, big and small, have entered the drone delivery game since then, but at the time of the 2017 report, the idea of drone delivery was sharply dividing commentator opinion, with some dismissing it as a mere publicity stunt.

Indeed, drone delivery must be viewed within the context of the emerging drone industry, which has grown to a more than $1.5 billion industry. In the ensuing years, consumer drones’ hardware platform became rapidly commoditized with prices falling.

The idea of drone delivery entered the mainstream media in late 2013. Around that time, drone delivery of e-commerce parcels was first demonstrated in parallel with drones successfully delivering medicine to remote areas. Since then numerous deliveries have been made, partnerships announced, and substantial sums invested.

Fleet Operation to Compensate for Poor Individual Drone Productivity?

Drone delivery faced critical challenges in 2017. Individual drones offer limited productivity compared to traditional means of delivery (e.g., consider a van delivering 150 parcels in an eight-hour shift). They can only carry small payloads and battery technology limits their flight duration, constraining them to around 30 minutes radius of their base while further lowering their productivity due to the downtime needed for re-charging/re-loading.

The limited productivity, in our view, is not a showstopper. This is because fleet operation can compensate for poor individual drone productivity. The unit cost of drones will be substantially lower than, say, a van, enabling the conversation of a few, highly-productive vehicles into many small drones with high productivity at the fleet level. This will require a further major reduction in hardware costs for commercial drones, but if the past is to be our guide, this will be inevitable.

Limited payload is also not a showstopper because, according to Amazon statistics, some 85 percent of packages weigh 5 pounds or fewer. Furthermore, the fall in delivery costs and time for customers is changing purchasing habits: frequent orders of small items is replacing that big infrequent order. This matches well to the strong points of drones.

The limited range is also not a showstopper even in suburban areas where customers do not live close to a distribution point. It will, however, mandate a gradual yet wholesale change in the location of warehouses with more placed closer to end customers or the use of large mobile drone carrier vans. The former is already happening in the background, while the latter has also been demonstrated at the proof-of-concept level.

Sidewalk Last-Mile Delivery Robots: a Billion-Dollar-Market by 2030?

Sidewalk robots are often designed to travel slowly at 4-6 km/hr (or 2.5-3.7 mph). This is to increase safety, to give robots more thinking time, to give remote teleoperators the chance to intervene, and to enable categorizing the robot as a personal device (vs. a vehicle), thus easing the legislative challenges.

However,  sidewalk robots are still far from being totally autonomous. First, they are often deployed in environments such as U.S. 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 centers. 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 200,000-unit fleet size until 2035 (accounting for replacement). The inflection point will not occur until around the 2025 period given the readiness level of the technology. This suggests both a large robot sales market and an even larger annual delivery services market provided asset utilization can be high (the services income could reach $1.6 billion by 2035 in a reasonable scenario).

Sidewalk Delivery Robots vs. Autonomous Delivery Vans

These robots, pods and vehicles are mainly designed from scratch to be unmanned. They are also almost always battery-powered and electrically-driven. This is for various reasons, including: (1) electronic drive gives better control of motion, especially when each wheel can be independently controlled; (2) the interface between the electronic control system and the electrical drive train is simpler, eliminating the need for complex by-wire systems found in autonomous ICE vehicles; and (3) their production process needs to handle vastly fewer parts, and as such could be taken on by smaller manufacturers.

Another key technology and business choice is where to navigate. Many robots are designed to travel on sidewalks and pedestrian pavements, while the van-looking pods and vehicles are often designed to be road-going. This choice of where to travel has determining consequences for the design, technology choice, target markets and business model.

Sidewalk robots are an interesting proposition. They come with various hardware choices. For example, some are few-wheeled while many are six-wheeled. Some include a single small-payload compartment, while others carry larger multi-item storage compartments. The key choice, however, is in what perception sensors to use.

Navigation Technology Choices

Mobile robots 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, are expensive and can have near-field (a few cm) blindspot. Therefore, the choice to use lidars will represent a bet for the cost of lidar technology to dramatically fall.

Most robots deploying lidars use 16-channel RoboSense or Velodyne lidars. These are mechanical rotating lidars, giving surround viewing. The technology of lidars is evolving with the likes of MEMS or OPA emerging. These could enable cost reduction but will reduce FoV (field of view), thus mandating the use of more lidar units per robot.

We project that the cost of lidars is to significantly fall over the coming years. This has the potential to put such robots on the path towards business viability. The other challenge is near-field blindspots. This is not an issue with cars, but can be in a sidewalk, where many low-lying objects can reside closely to the robot. To resolve these, complementary sensors will be needed.

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.

No off-the-shelf software solution exists. Indeed, no labeled training dataset exists that would allow training lidar-based, camera-based or hybrid deep neutral networks (DNNs) for sidewalk navigation. The sidewalk environment is vastly different to that of the on-road vehicles. As such, companies will need to collect, calibrate, and meticulously label their own datasets. Furthermore, the datasets will require great diversity to accommodate different light, perception, and local conditions. Deployments in many sites even as pilot programs are essential in further improving the robots and can indeed represent a competitive advantage.

The robots are energy-constrained. As such, the number of on-board processors and GPUs should be kept to a minimum, and heavy-duty computational tasks such as 3D map-making and edge-extraction should be carried off-line in powerful services. This almost always happens when robots are deployed to a new environment: They are walked around to capture data, the data is sent to servers for processing so it can be converted into a suitable map, earmarking edges, many classes of fixed objects, drivable paths, and so on.

Long Road to Profitability Lies Ahead

In general, there is still much work to do to improve navigation technology. The robots will need to learn to operate in more complex and varied environments with minimal intervention. This requires extensive investment in software development. This ranges from gathering data, defining object classes, labeling the data, and training the DNNs in many environments and conditions. It also requires writing algorithms for the many challenges the robots encounter in their autonomous operation.

Furthermore, capital is also essential. The businesses are heavy on development costs, especially software costs. The end markets are also highly competitive, imposing tough price constraints. The hardware itself is likely to be commoditized and many will outsource manufacturing once they have settled on a suitable final design. The payback for many will be having a large fleet to offer robots as a delivery service.

Future Outlook: Significant Robot Sale and Delivery Services Opportunity

Sales and delivery firms are likely to have a long road ahead of them before they reach profitability. They should improve the robots to work in more scenarios beyond well-structured neighborhoods and campuses, to extend their operation to all-day and all-weather conditions, and to extend autonomous operation with little error to nearly all scenarios to drive down the remote operator-to-fleet size ratio.

The deployed fleet size will need to dramatically increase to expand income from delivery services and allow the amortization of the software development costs over many units sold.

We have analyzed all the key companies and technologies in this emerging field. We have also constructed a forecast model, considering how the productivity of last-mile mobile robots is likely to evolve over the years. We have developed various scenarios, assessing the current and future addressable market size in terms of total accumulated fleet size. Our fleet deployment forecasts and penetration rate forecasts are based upon on reasonable market and technology assessments and roadmaps.

Consequently, our forecasts suggest, that despite the upfront technology and market challenges, the market will grow and those who plant their seeds today will reap the benefits tomorrow.

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Dr. Khasha Ghaffarzadeh is the research director at IDTechEx, where he has helped deliver more than 50 consulting projects across the world. The projects have covered custom market research, technology scouting, partnership/customer development, technology road mapping, product positioning, competitive analysis and investment due diligence.

His report “Mobile Robots, Autonomous Vehicles, and Drones in Logistics, Warehousing, and Delivery 2020-2040” covers the use of mobile robots, drones, and autonomous vehicles in delivery, warehousing and logistics. It provides a comprehensive analysis of all the key players, technologies and markets, covering 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. You can find the report here: https://www.idtechex.com/en/research-report/mobile-robots-autonomous-vehicles-and-drones-in-logistics-warehousing-and-delivery-2020-2040/706.

You can find his report “Mobile Robots and Drones in Material Handling and Logistics 2017-2037” here: https://www.idtechex.com/en/research-article/drone-delivery-publicity-stunt-or-game-changer-in-instant-fulfilment/11658.

IDTechEx guides strategic business decisions through its Research, Consultancy and Event products, helping clients profit from emerging technologies. For more information on IDTechEx Research and Consultancy, contact research@IDTechEx.com or visit www.IDTechEx.com.

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