Executive Summary

In recent years the FAA has brought swift changes in the regulatory setup , leading to steady adoption of drones across several end use verticals .

According to the FAA there are nearly a million registered UAVs in the US alone which presents a sizable market opportunity .[1][2]

These UAVs have brought significant productivity gains and have enhanced the operational efficiency of businesses and brands. Today these UAVs are deployed in a range of operations ranging from law enforcement , rescue and search to industrial use cases such as in energy , mining and are even used for facilitating last mile deliveries.

Problem :

The steady adoption of drones has triggered the demand for skilled drone pilots . These pilots fulfill several operational needs such as flight piloting , surveillance & mapping , data collection and often work with the industries and law enforcement agencies for assisting in day to day operations .

Despite this the industry lacks a suitable simulation environment for testing the competency of the pilots and lacks any means to train the workforce/pilots for these diverse operational needs. Furthermore the pilots don’t have an adequate simulation environment to train themselves under different environment conditions or to benchmark their abilities.

Solution :

A drone simulator which models real world environments and payload constraints and helps pilots train against multiple terrain and climate conditions.

The integrated LMS monitors the flying patterns and builds a personalized training module for every pilot . Alternatively the pilot could manually tweak the environment variables and train against diverse climate conditions , wind speeds etc. They could also tweak payload parameters and get real time insights about their flying patterns.

The pilots can utilize these data points to track progress and benchmark their ability . The vendors could use these data points to significantly reduce the risks of operating human-piloted UAVs.