A Self-Organized Reciprocal Decision Approach for Sensing Coverage with Multi-UAV Swarms

Area of Science:

• Robotics and Autonomous Systems
• Distributed Systems
• Sensor Networks

Background:

• Coordinated multi-UAV systems face challenges in maintaining stable trajectories and ensuring efficient sensing coverage.
• Traditional methods often optimize in configuration space, which can be complex and less adaptable for dynamic environments.

Purpose of the Study:

• To develop a novel, distributed approach for optimizing sensing coverage in multi-UAV systems.
• To enhance trajectory stability and collision avoidance through reciprocal interactions between neighboring UAVs.

Main Methods:

• A three-step, self-organized, distributed, and autonomous reciprocal decision approach is proposed.
• The sensing coverage problem is reformulated as an optimal reciprocal coverage velocity (ORCV) in velocity space.
• A random probability method is employed to determine the optimal velocity within the ORCV framework, ensuring collision avoidance.

Main Results:

• The proposed method achieves a high sensing coverage rate and rapid convergence.
• Simulations demonstrate low deadweight loss and excellent scalability for up to 1000 UAVs.
• The approach effectively ensures collision-avoiding assembly among UAVs.

Conclusions:

• The ORCV approach offers a concise and effective solution for multi-UAV sensing coverage optimization.
• The method provides robust scalability and collision avoidance capabilities for large-scale drone operations.
• This distributed, autonomous strategy significantly improves the performance of coordinated UAV missions.