Distributed UAV Swarm Formation and Collision Avoidance Strategies Over Fixed and Switching Topologies

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Aerospace Engineering

Background:

• Unmanned Aerial Vehicles (UAVs) offer collective benefits like reduced energy consumption through formation flight.
• Traditional formation strategies significantly restrict individual UAV freedom and formation shape.
• Swarm strategies offer greater individual UAV freedom but require careful rule-based coordination.

Purpose of the Study:

• To propose a novel controlling framework for multiple UAVs integrating formation flight and swarm deployment modes.
• To enable seamless switching between formation and swarm strategies based on environmental factors.
• To address challenges posed by poor network conditions and unknown, mobile obstacles.

Main Methods:

• Development of a distributed formation controller for guiding UAVs into desired formations from unordered states.
• Implementation of a distributed collision avoidance controller inspired by biological systems to handle unknown and mobile obstacles.
• Analysis of controller stability across fixed and switching network topologies.

Main Results:

• The proposed framework effectively integrates formation flight and swarm deployment strategies for multiple UAVs.
• The distributed controllers demonstrated stability and effectiveness in managing UAVs under various conditions.
• Experimental validation confirmed the framework's capability to handle dynamic environments and ensure collision avoidance.

Conclusions:

• The integrated framework provides a flexible and robust solution for controlling multiple UAVs in complex environments.
• The study highlights the potential of bio-inspired control strategies for enhancing UAV autonomy and safety.
• The developed controllers are effective for both static and dynamic network topologies, paving the way for advanced multi-UAV applications.