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• MACHINE LEARNING MODEL OF SWARM EVASION FROM THE INFLUENCE OF ANTAGONISTIC ENVIRONMENT

  V. К. Abrosimov, G.А. Dolgov, Е. S. Mikhailova

  6-19

  2025-04-27

  Abstract ▼

  One of the priority areas of group control theory for the near future is swarm control of groups of
  small unmanned aerial vehicles - micro-, mini- and nano-classes, performing a collective task under enemy
  influence. Here, two antagonistic strategies collide - minimization of losses from the point of view of
  the attacking swarm and maximization of such losses from the point of view of the defense system. Research objective: development of an approach to solving a practical problem - penetration of a swarm of
  unmanned aerial vehicles into an object protected by a defense system. The objectives of the study were to
  analyze the characteristics of the factors influencing the processes of detection, tracking, recognition of
  swarm intentions by the defense system and the development of a machine learning model for creating
  spatio-temporal formations that minimize the number of swarm elements affected by the defense system.
  The main parameters of the defense system are the detection range and duration of swarm recognition, the
  time to make a decision on the actions of the swarm, the size of the zone of destruction of defense means.
  The method of machine learning on convolutional neural networks with reinforcement was chosen as the
  research method. The counteraction effect against the defense system is created due to the swarm's dynamics;
  it can actively maneuver, creating spatio-temporal maneuvers during the mission. To simulate the
  "Swarm vs. Defense System" situation, a swarm agent (a neural network with a transformer architecture
  that initiates swarm formations) and a defense system agent are introduced that recognizes the swarm and
  attacks it, creating a zone of destruction in the conventional center of mass of the swarm. The swarm is
  guided by a stochastic rule, asking the defense system (environment) to react to its maneuver. The environment
  responds by attacking the swarm, creating a damaging factor at the point where the swarm or the
  main part of the swarm is expected to be. The reward of the swarm strategy is the number of undestroyed
  objects under the conditions of constraints; for the defense system, this "reward" acts as a "punishment".
  An interesting phenomenon was established in the process of machine learning: each swarm element,
  remaining within a given space and implementing the biological principles of swarm control without a
  Leader, independently evades the area of destruction, which together creates a random spatio-temporal
  formation for defense means with minimal losses of swarm elements. Thus, using the method of machine
  learning with reinforcement, a model was created that allows varying the behavior of the swarm and synthesizing
  spatio-temporal formations that complicate detection, tracking, recognition of intentions and
  decision-making on the impact of the defense system on a swarm of attacking small unmanned aerial vehicles,
  as well as significantly reducing their losses.