It’s difficult enough to take an autonomous drone from proof-of-concept to enterprise-production-ready. From remote operation to device management to telemetry, one assembles a system that coordinates hardware with embedded, cloud, and mobile software. The autonomous drone has to know what to do when there is no internet connectivity, or when there is an obstacle in the way, or the battery runs low. However, if you can overcome the multitude of unknown unknowns, then your autonomous helper could prove useful.

However, to control a fleet of them is more difficult than just saying “altogether now”. You will have to contend with a number of new challenges, including but not limited to:

1. Synchronization, or, how to not crash into each other. Also, how to work together. For example, when the crane lifts the container from the boat onto the train, that’s three robots working in harmony. But with flying drones, synchronization is mostly figuring out how not to crash into each other, which is surprisingly hard. It’s one of the reasons that geese flying in formation constantly honk, like cars in some countries, a behavior known as “reverse sonar”.
2. Interference: not just internet connectivity, even more imperative for autonomous drones is knowledge of their position. GPS sensors can be accurate to the centimeter, but are notoriously sensitive to nearby electronics. The transmitters, companion computers, even unshielded cables, on your drone, or drones nearby, can blind your GPS sensor, which needs to receive a clear signal simultaneously from many satellites. Standard drone software, if suddenly robbed of GPS position data, tends to panic.
3. Scale: complexity increases exponentially with the number of drones involved. For autonomous drones that rely on AI reasoning, the speed and quality of decision making must scale automatically to match.

If you think you can just sprinkle AI fairy dust on your collection of drones and then watch them fly up into glory then you have skipped a few important steps. Proper planning and preparation prevents piss poor performance, as they say, as well as broken parts. Or even worse, schedule overruns when your autonomous proof-of-concept doesn’t scale to an enterprise fleet.