These are the use cases targeted for a commercial drone fleet management software that scales elastically and helps drones manage their flight path in real-time.

Case 1. A retail company owns and operates a fleet of drones with proprietary software to deliver merchandise purchased by its customers to their residences. The software tracks each drone individually providing route and schedule information independent of others. The number of drones exceeds hundreds of thousands. The software is tasked with the dual goal of assigning non-overlapping,  contention free and flow maximizing  flight manifests for each drone as well as keeping the costs low for the operation of a single drone deployed to serve a customer. Controllers for the drones are placed in multiple cities worldwide and operate their own fleet independent of others and cover a specific non-overlapping geographic region. The software differentiates from the one used to mange the fleet of robots in its warehouse in that the space to operate is not a grid but a hub-and-spoke model and the destinations are not internally managed but received from order processing service bearing address, volume, weight and delivery date information in a streaming manner. The software does not differentiate between fleet members and requires the ability to form the fleet dynamically by replacing , adding or removing members. When this proprietary software becomes general purpose use for fleets of varying sizes, operating loads, and quality of service, it becomes promising as a multitenant platform so that different companies do not need to reinvent the wheel. This platform must keep the use of cloud resources as efficient as possible to pass on the savings to businesses and streamline consumption of additional resources when any single parameter is stretched. Additionally, it must provide offloading of any interface of its components for customization by these businesses. For example, they can determine their own scheduling plug-in and are not bound to supplying values to predetermined scheduling templates. Volume and rate of drone command updates must cover the six sigma of normalizes  probability distribution of current and foreseeable future fleet sizes.

Case 2. A small and medium sized business is looking to improve the efficiency of  scheduling for its fleet and wants to download models that it can operate on its own premises or invoke remotely from APIs. This controller-offload-to-the cloud scenario demands world class performance and availability at affordable price. This business does not expect to grow its fleet or experience a lot of churns in its fleet but appreciates full service outsourcing of its fleet management so that it merely controls the inventory and the commands to the fleet members. It also expects to include a clause for liability of drone damages resulting from faulty instructions. It might want to change scheduling algorithms depending on changing business priorities that are not fully covered by the scheduling parameters.

Case 3. Many businesses outsource their fleet with a fleet provider that maintains a large inventory of hybrid models that are continually updated in their degrees of motion, capabilities and inputs. These businesses expect to have one interface with the fleet provider and another with the fleet management service such that they can choose to organize their fleet dynamically and pass the fleet details to their management service for commands that they can relay to their fleet members. They expect a detailed and comprehensive management dashboard or portal where they can monitor the key performance indicators aka KPI  for the fleet and drill down. They expect these programmability interfaces to be compatible in a way that requires little or no processing other than relaying the commands or updating the goals on the management dashboard.

References: https://1drv.ms/w/s!Ashlm-Nw-wnWhPA9saJLYQGA7q2Wiw?e=AONTxo for data architecture 

https://1drv.ms/w/s!Ashlm-Nw-wnWhO4OGADjCj0GVLyFTA?e=UGMEpB for software description.