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This study presents a new flocking model for autonomous drones navigating confined spaces. The model ensures stable collective motion and collision avoidance in large, high-velocity drone swarms, validated by real-world experiments.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Existing flocking models for aerial robots often fail on real hardware due to neglecting constraints.
  • Real-world multi-robot systems face challenges like limited communication, delays, and obstacles.

Purpose of the Study:

  • To develop and validate a robust flocking model for autonomous drones operating in confined environments.
  • To address limitations of current models by incorporating realistic constraints and an optimization framework.

Main Methods:

  • Proposed a novel flocking model for drones integrated with an evolutionary optimization framework.
  • Utilized carefully selected order parameters and fitness functions for model development.
  • Conducted numerical simulations and field experiments with a swarm of 30 drones.

Main Results:

  • The flocking model demonstrated stable collective motion in large drone swarms, even at high velocities and near obstacles.
  • The system exhibited coherent and realistic collective motion patterns under perturbed conditions.
  • Successfully validated the model on a real-world outdoor system of 30 self-organized drones without central control.

Conclusions:

  • The developed flocking model effectively handles realistic constraints for autonomous drone swarms.
  • This work represents the largest reported outdoor aerial system exhibiting flocking with collision and object avoidance.
  • The approach enables more efficient task management for large drone swarms in diverse applications.