A method was developed for a swarm of hundreds of small battery-powered drones to autonomously return from military missions to unmanned ground vehicles (UGVs) for recharging. Algorithms are being developed to enable route planning for multiple teams of small unmanned air and ground vehicles. This will optimize the operational range extension and time on mission.
When deploying a swarm of hundreds or thousands of unmanned aerial systems (UAS), each of the systems has only about 26 minutes with current battery technologies to conduct a flight mission and return to their home before they lose battery power, which means all of them could conceivably return at the same time to have their batteries replaced.
Soldiers, for example, would need to carry a few thousand batteries on missions to facilitate this, which is logistically overwhelming. The use of fast recharging batteries and wireless power transfer technologies will allow multiple small UAS to hover around unmanned ground vehicles for wireless charging and this will not require soldier involvement.
For larger drones, research will explore the fundamental science needed to develop miniaturized fuel sensors for future multi-fuel hybrid electric propulsion systems. Fuel property sensors will help soldiers who operate fuel-based equipment to measure fuel property in real time for the Army's air and ground vehicles. This knowledge will allow Army personnel to prevent catastrophic failures of the systems and to increase its performance and reliability.
The fuel sensor tells the operator what type of fuel is being delivered from the fuel tank to the engine. This input signal can be used to intelligently tell the engine to adjust engine control parameters according to the fuel type to avoid any failures. This data can also be used to find root-cause failures if any engine component prematurely failed.
Current research in fuel sensor development examines the effects of fuel structure and chemistry on ignition in future multi-fuel drone engines so that real-time control can be implemented. This project further explores the underpinning science using advanced techniques, including spectroscopic diagnostics and data science analysis, to both enable and accelerate real-time control.