Modeling and optimization of multiple unmanned aerial vehicles system architecture alternatives

Area of Science:

• Robotics and Control Systems
• Computer Science
• Aerospace Engineering

Background:

• Unmanned aerial vehicle (UAV) systems are increasingly adopted for civilian applications.
• Coordinating multiple UAVs presents significant system architecture challenges for diverse tasks.
• Existing methods for multi-UAV system design require optimization.

Purpose of the Study:

• To define and address the multi-UAV system architecture problem.
• To propose an optimization approach for designing multi-UAV systems.
• To validate the proposed method's effectiveness in realistic scenarios.

Main Methods:

• Formulated the multi-UAV system architecture challenge as an optimization problem.
• Developed an efficient genetic algorithm with a refined crossover operator (GA-RX).
• Conducted two simulations across different scenarios to test the GA-RX algorithm.

Main Results:

• The proposed GA-RX algorithm effectively addresses the multi-UAV system architecture problem.
• Simulations demonstrated the availability and effectiveness of the developed method.
• The iterative architecting process using GA-RX proved successful.

Conclusions:

• The GA-RX algorithm offers a viable solution for optimizing multi-UAV system architecture.
• This approach enhances the coordination and deployment of UAV systems in civilian tasks.
• The validated method contributes to the advancement of autonomous multi-UAV operations.