Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs

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.