Providing speeds up to 10-times faster than LTE, lower latency, and greater network capacity, 5G opens the possibilities of a truly connected world that will disrupt numerous industries. However, designing, architecting, and deploying the end-to-end networks to support this evolutionary shift presents a monumental challenge.
Among the many industrial use cases for 5G technologies, including smart cities, remote healthcare, and efficient retail, uncrewed aerial systems (UAS) is one of the more complex to solve. However, this complexity also makes UAS the most valuable use case for ultimately reaching all 5G possibilities.
That’s precisely why MITRE Engenuity Open Generation 5G Consortium chose UAS as its initial area of focus. By attacking the biggest challenges head-on, Open Generation is laying the groundwork for the next evolution of devices that will save lives and enhance our daily way of life.
The shortcoming of attacking a significant challenge by starting with low-hanging fruit is that you typically don’t solve enough of the overall problem. Frequently, you end up with a lot of simple, more minor problems solved (i.e. individual pieces of the overall problem) rather than resolving big-picture challenges. This incremental progress can be slow and limiting.
Inverting the approach to resolving more complicated problems first opens the door to resolving additional challenges by deriving a simpler version of that solution. You can make significant, tangible progress when you find the simplest, generalizable solution for a complex problem because you can derive extensible solutions to vertically related problems.
UAS — whether a small imaging drone or a large people mover — is a highly challenging use case to solve because it presents a three-dimensional problem. In contrast to automobiles, which present a two-dimensional problem, controlling a UAS system adds a third dimension.
Cars operate on X and Y dimensions, but drones are X, Y, and Z. Furthermore, if we assign X and Y as longitude and latitude, the Z dimension of altitude presents almost no constraint. You could argue that assigned flight paths constrain the X and Y dimensions, but Z is entirely open.
Adding to the complexity, executing UAS in an end-to-end system requires infallible wireless networks. The drone must experience instant, safe handoff from one operator network to another to avoid losing its control signal (and subsequently running into something or falling from the sky).
This requisite network reliability stipulates cooperation between service operators to ensure the control signals never drop. Thus, if next-generation industrial systems aren’t operator-independent, they must, at minimum, interwork seamlessly across operators.
This level of cohesion requires alignment between multiple players: operators who own the spectrum, equipment vendors, drone technology vendors, and industrial users (all with their various use case requirements). Such a cooperative ecosystem can only function behind radical collaboration.
If you apply that solutions to difficult problems can be derived to resolve “easier” challenges, learnings from solutions to the interaction of 3-D movement can be applied to one- and two-dimensional problems, such as automobiles. With 3-D complexities resolved, you can more easily address 1-D linear vehicle problems (e.g. driving down a road) and movement in a full two dimensions (e.g. multiple lanes and turns).
If you solve multiple drones flying safely in close proximity, you more easily solve related autonomous vehicle problems. If you solve the complexities of human-drone interaction, you can solve human-vehicle interaction problems because they are practically a simpler version of the same problem.
As another example from an industrial use perspective, utilizing ground-based, terrestrial robots inside a factory becomes an easier problem to solve because they follow the 2-D precept of rows and columns (aisles and cross-aisles).
Not only will solutions for UAS applications inform other derivative solutions, but based on their potential, uncrewed aircraft offer life-saving and life-changing use cases. Uncrewed aerial vehicles are perhaps the most significant change we could make to transporting ourselves and things, and drones also provide a compelling use case for real-time, high-definition imaging.
Companies have already invested in experimenting with near-term practical applications like UAS delivery. Road-based delivery is congested and will only worsen, making drone delivery a likely pursuit for UAS. Due to obvious safety challenges, transportation of people might be the farthest out application for UAS. Nonetheless, we can assume there ultimately will be some type of autonomous passenger flight (China is already experimenting with this).
Beyond use as a conveyance system, drones make an inviting imaging system. If you consider available means to image the world continuously, static imagers like cameras, LiDAR, or radar are logistically unreasonable because you would need them installed (and maintained) everywhere. However, if you want to capture something with any cadence, a drone provides a logically efficient way to accomplish that.
UAS imaging may not sound life-changing on the surface, especially given that drones have long offered some form of video capture capabilities. However, real-time maps of everywhere can optimize how efficiently people traverse the world. Knowing everything about where you are, every place in front of you, or where you plan to go becomes the optimum use case for efficiency.
Generalize that to imaging indoors, such as in a factory, and people or systems can realistically know everything going on at any given time, allowing businesses to optimize continually by calculating the right change to make, the pertinent service to upgrade, or the appropriate person/robot to send.
Up to and including LTE, previous wireless technologies were designed for human communications. However, humans process information with a tolerable delay of about 100 milliseconds. Machine systems, unrestricted by any tolerable delay, can and prefer to move with the most fidelity possible — to operate with the highest control and lowest latency feasible. Thus, 5G was designed with industrials in mind; for the most efficient transfer of information at the highest speed.
In this new paradigm, physical infrastructure, machines, and systems can all wirelessly connect, but constructing and commercializing this paradigm begins with solving the most challenging use cases. Then, we can derive all the other solutions and make truly generational and life-changing progress.