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Computer simulation experimentation examined the effectiveness of different Unmanned Aircraft System (UAS) swarm configurations for identification and localization of survivors after a natural disaster using the DroneLab application. Swarms differed in terms of total number of drones and ratio of entities programmed to perform one of three different “personalities”—Relay, Social, and Antisocial. Relay behavior puts a high priority on maintaining proximity to the centroid of the swarm while also maintaining a distance to closest neighbor drones equal to half of the maximum WiFi range. Antisocial drones prioritize an expanding behavior, increasing the spread of the swarm, while the Social behavior prioritizes a contractive behavior resulting in a tighter swarm formation. All drones performed a local waypoint-based search behavior while conducting a spiral-out search pattern upon detecting four or more survivors within a 10-meter radius. Swarm configurations with different ratios of these behaving entities were assessed for mission completion, defined as time to find 90% of the survivors. Mission completions were recorded for four simulation scenarios consisting of two terrains (urban/rural) with two different distributions of survivors (naturalistic/randomized). Ten replications of 98 different drone configurations were evaluated. Statistically significant differences between time to mission completion between the terrains, between the two distributions, and among the iterations were revealed. Qualitative comparisons revealed differences in configurations that performed the best in each terrain. A few configurations performed well in all four scenarios. Moreover, the minimum number of entities needed for well-performing swarms was indicated. The work demonstrates the utility of computer experimentation and statistical analyses for developing a framework for swarm design for operational effectiveness.
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