The long-term goal of this research is to design cooperative-control algorithms for autonomous vehicles inspired by the collective behaviors in animal groups. The specific research objectives of this dissertation are twofold: (1) to analyze and model the swarming and pursuit behaviors observed in the mating swarms of mosquitoes, and (2) to design mosquito-inspired control algorithms to perform swarming and pursuit with autonomous rotorcraft.

The first part of this dissertation analyzes the reconstructed flight data of the malarial mosquito Anopheles gambiae to characterize the velocity-alignment interaction between male mosquitoes, who aggregate to form mating swarms and subsequently pursue a female mosquito. Both swarming and pursuit behaviors are represented using self-propelled particle models. The model is used together with tools from control theory to investigate the connection between velocity-alignment behavior and success in pursuit. The results of this research have a potential impact on vector-control methods for malaria, and are also utilized in the second part of this dissertation.

The second part of this dissertation studies two types of pursuit problems inspired by the collective behavior in mosquito swarms. The first problem considers the strategy for a single pursuer chasing a single target. This problem has been studied extensively for the application to missile guidance and navigation. Here, we tailor the assumptions on the dynamics of the agents as well as the design criteria for the application to small and agile rotorcraft. The second pursuit problem incorporates the swarming behavior by considering a scenario in which multiple guardian vehicles are deployed to protect an area against fast intruders. We derive necessary and sufficient conditions for capturing the intruder. We also present swarming strategies to maximize the performance of the guardians, inspired by the random-oscillatory motion and the velocity-alignment behavior of male mosquitoes.