The U.S. Army ordered development of the system of charging of a swarm of drones

On October 7, 2020 it became known that the U.S. Army aims to create the autonomous system of charging capable to service hundreds of drones. The U.S. Army financed the four-year research project which ultimate goal consists in start of charging stations which will maintain a huge number of drones.

According to Engadget, the University of Illinois in Chicago signed the contract for $8 million with research laboratory of U.S. Army which specializes in development of fighting opportunities. Researchers will work on a system which will allow small drones to define location of the next charging station, to reach independently there and to be loaded before returning to the mission. The university works on algorithms which will help drones to define the best route to charging port.

The U.S. Army ordered development of the system of charging of a swarm of drones, the Illinoyssky university participates in the project

The project manager Mike Kveon noted:

Imagine that in the future the army will unroll a swarm from hundreds or thousands of the pilotless aviation systems. Each of these systems has only about 26 minutes with the current technologies of accumulators to carry out flight and to come back home before they lose battery power that means that all of them can return at the same time to replace the batteries.

The researchers financed by army will also develop miniature fuel level sensors for larger drones. According to DroneDJ, it will allow future drones which can partially use gasoline, define when they finish fuel. Thanks to it devices will be able to return on base for refueling or recharge before they finish fuel.

This research is crucial not only for aircraft, but also for land vehicles, especially for army missions, - Kveon told. - The fuel level sensor tells the operator what type of fuel moves from the fuel tank in the engine. This input signal can be used for the intellectual instruction to the engine to adjust parameters of management according to fuel type to avoid any failures. These data can also be used for search of basic reasons of failures if any component of the engine prematurely failed.[1]

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