A new US Air Force (USAF) study indicates that Chinese researchers are looking to address the challenges related to creating drone swarms for future combat missions.

The paper, written by USAF Major Emilie Stuart and published by the China Aerospace Studies Institute, defines swarms as several unmanned air vehicles collaborating to achieve missions.


Source: Boevaya Mashina/Wikimedia Commons

China has been developing its own loitering munitions, including the FH-901

Stuart stresses that such a swarm has yet to be tested in combat. While large numbers of drones have been used in conflicts, sometimes simultaneously, the systems are still individually controlled. Moreover, China’s leadership has given little public indication about how it views the potential for drone swarms, and how they may fit in with People’s Liberation Army doctrine. 

True swarms include a degree of autonomous control generated using artificial intelligence/machine learning. Also, communications between drones allow them to function as a team absent from human input. Drone swarms must also possess accurate positioning and navigation, as well as a sufficient power supply.

Examining patent applications in China, Stuart observes that Chinese developers see many potential benefits from drone swarms, including image acquisition from multiple vantage points, reconnaissance, electronic countermeasures, and precision strikes.

Swarms can also be greater than the sum of their parts if each drone has a specific mission and decisions require “very little human intervention”.

Nonetheless, Chinese developers see challenges to deploying swarms, such as the balance between payload size and range, spectrum congestion, the failure of individual drones, and the impact of high-speed flight on connectivity. Communications delays will also occur as the size and range of swarms grows.

One topic developers grapple with is operations in challenging mission areas, such as in mountainous or urban terrain.

“The earliest drone swarm inventions focused on [command and control] and collaboration followed a few years later by those focused on navigation and path finding,” writes Stuart.

“This is logical because to create an efficient swarm, one would have to figure out the collaboration piece first as a foundation, then add mission sets and additional complexity, including path finding.”

Curiously, only one invention reviewed by Stuart addressed multi-mission swarms.

“However, this invention speaks more to the [command and control] structure of such a construct and does not speak to the specific missions set this structure would address, not the specific tasks,” writes Stuart.

“Most of these inventions likely exist at the classified level.”