EMPLOYMENT OF INTELLIGENCE, SURVEILLANCE, AND RECONNAISSANCE DRONE SWARMS TO ENHANCE GROUND COMBAT OPERATIONS

Abstract

On August 27, 2015, the Naval Postgraduate School’s (NPS) Advanced Robotic Systems Engineering Laboratory flew 50 autonomous drones simultaneously. This demonstration proved that autonomous drone swarm technology is evolving at a daunting pace and drone deployment and control can now be done en mass. As academia, industry, and defense sectors continue to miniaturize sensors and enhance swarm operating systems, the transition from demonstrations to tactical employment will occur quickly. Doing so efficiently requires dedicated efforts to determine swarm sensor requirements and employment tactics, techniques, and procedures. This thesis uses agent-based simulation, cutting-edge design of experiments, and parallel computing to thoroughly explore drone swarm employment in support of a Marine infantry company. The scenario is a deliberate clearing mission, based on real events, in which an infantry company fights a peer enemy in restricted terrain. Analysis of the data obtained from 30,000 simulated missions reveals that, on average, the drone swarms enable the fire support team to target and engage twice as many enemy combatants when compared to the current ISR drone available at the company level. For the hierarchical swarm, this results in up to 50% fewer U.S. casualties. Data analysis and visual study of the emergent swarm also shows that the volume of the swarm, coupled with inherent sensor overlap, results in the largest reduction in sensor requirements.