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Method of spatial countering an intruder in a swarm of unmanned aerial vehicles

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The development of decentralized control technologies for swarms of Unmanned Aerial Vehicles requires the development of new methods for ensuring their resilience to internal threats. The emergence of an intruder agent within a swarm creates threats of energy or information attacks. The situation is especially critical when the intruder agent is located at the center of the swarm, with its influence on its neighbors being greatest. Existing research has focused primarily on detecting intruder agents, while countermeasures, particularly spatial exclusion of the intruder agent from the group, remain poorly understood. This study develops and analyzes a method for spatial countermeasures against the intruder agent that does not require its explicit detection or information exchange between agents. The proposed method is based on the original idea of analogizing the control of a swarm of unmanned aerial vehicles (agents) with the processes occurring in a semiconductor crystal. Countermeasures against the intruder agent are achieved through the temporary modification of certain swarm interaction parameters by the agents. As a result, the spatial structure of the swarm changes, and the intruder, who does not change its interaction parameters, begins to move relative to the other agents, ending up at the edge of the group. Three implementation options for the proposed countermeasure method, based on compression, expansion, and sequential restructuring of the swarm structure, are investigated. Simulation modeling of the behavior of a swarm of unmanned aerial vehicles in the presence of an intruder was performed. The success of the method was measured by the probability of the intruder agent having fewer than five neighboring unmanned aerial vehicles, i.e., being at the edge of the group. The best performance (probability close to 1.0) was demonstrated by the swarm compression option. The swarm expansion option showed lower performance (non-guaranteed option). The sequential restructuring option proved ineffective. It is shown that the degree of change in the distance between agents makes the main contribution to the effectiveness of the method. The proposed method implemented in the swarm compression mode demonstrated effectiveness for swarm numbers ranging from 19 to 91. The proposed method reduces the likelihood of destructive intruder influence in a swarm of unmanned aerial vehicles, relying solely on the agents local navigation data, without resorting to intruder detection. This makes the method applicable to systems with limited communication capabilities. Some increase in energy consumption can be mitigated by optimizing the intrusion duration and maintaining the swarm structure.

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