From Event: SPIE Defense + Commercial Sensing, 2023
Recent years have seen the emergence of novel UAV swarm methodologies being developed for numerous applications within the Department of Defense. Such applications include, but are not limited to, search and rescue missions, intelligence, surveillance, and reconnaissance activities, and rapid disaster relief assessment. Herein, this article investigates an initial implementation of learning UAV swarm behaviors using reinforcement learning (RL). Specifically, we present a study implementing a leader-follower UAV swarm using RL-learned behaviors in a search-and-rescue task. Experiments are performed through simulations on synthetic data, specifically using a cross-platform flight simulator with Unreal Engine virtual environment. Performance is assessed by measuring key objective metrics, such as time to complete the mission, redundant actions, stagnation time, and goal success. This article seeks to provide an increased understanding and assessment of current reinforcement learning strategies being developed for controlling (or at a minimum suggesting) UAV swarm behaviors.
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Samantha S. Carley, Stanton R. Price, Xian Mae D. Hadia, Steven R. Price, and Samantha J. Butler, "Initial investigation of UAV swarm behaviors in a search-and-rescue scenario using reinforcement learning," Proc. SPIE 12549, Unmanned Systems Technology XXV, 125490F (Presented at SPIE Defense + Commercial Sensing: May 04, 2023; Published: 14 June 2023); https://doi.org/10.1117/12.2663629.