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This study presents a decentralized control strategy for unmanned aerial vehicle (UAV) swarms to maintain formation flight and navigate to a target area. The method effectively manages obstacles and ensures collision avoidance for robust swarm operations.

Keywords:
UAV swarmanti-collisiondecentralized controlformation flightobstacle avoidance

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Area of Science:

  • Robotics
  • Control Systems
  • Aerospace Engineering

Background:

  • Cooperative control of unmanned aerial vehicle (UAV) swarms is crucial for complex missions.
  • Maintaining formation topology and inter-vehicle distances presents significant challenges, especially in dynamic environments.

Purpose of the Study:

  • To design a decentralized guidance and control strategy for UAV swarms.
  • To ensure maintenance of a specified connection topology and mutual distances during flight to a target area.
  • To enable obstacle avoidance and collision prevention within the swarm.

Main Methods:

  • Utilized an extended Delaunay triangulation concept for formation control in obstacle-free environments.
  • Integrated Model Predictive Control (MPC) for adaptive sub-swarm formation and obstacle avoidance.
  • Developed a custom numerical simulator in Matlab/Simulink for validation.

Main Results:

  • The proposed strategy successfully maintained formation shapes and inter-vehicle distances.
  • MPC enabled effective obstacle avoidance and collision prevention by forming independent sub-swarms.
  • Simulations demonstrated the scheme's effectiveness in various 2D scenarios with diverse obstacles and swarm sizes.

Conclusions:

  • The decentralized guidance and control strategy is effective for UAV swarms operating in complex environments.
  • The approach ensures robust formation maintenance, obstacle avoidance, and collision prevention.
  • The developed simulator provides a valuable tool for testing and validating cooperative UAV control algorithms.