Initial investigation of UAV swarm behaviors in a search-and-rescue scenario using reinforcement learning

Samantha S. Carley; Stanton R. Price; Xian Mae D. Hadia; Steven R. Price; Samantha J. Butler

doi:10.1117/12.2663629

14 June 2023 Initial investigation of UAV swarm behaviors in a search-and-rescue scenario using reinforcement learning

Samantha S. Carley, Stanton R. Price, Xian Mae D. Hadia, Steven R. Price, Samantha J. Butler

Proceedings Volume 12549, Unmanned Systems Technology XXV; 125490F (2023) https://doi.org/10.1117/12.2663629
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

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 (14 June 2023); https://doi.org/10.1117/12.2663629

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KEYWORDS

Machine learning

Unmanned aerial vehicles

Simulations

Deep learning

Artificial neural networks

Collision avoidance

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