Collision Avoidance Mechanism for Swarms of Drones

Area of Science:

• Robotics
• Artificial Intelligence
• Computer Science

Background:

• Large-scale drone swarms require robust collision avoidance.
• Dynamic environments pose significant challenges for autonomous navigation.
• Existing methods may lack scalability or be computationally intensive.

Purpose of the Study:

• To present a novel, distributed collision avoidance mechanism for drone swarms.
• To enable autonomous cooperation and safe distance maintenance among drones.
• To develop a scalable and computationally efficient solution.

Main Methods:

• Utilizes distributed communication for drones to share positional and trajectory data.
• Employs repulsion vectors based on proximity to obstacles and other drones.
• Simulates swarm behavior in complex scenarios with over 20 drones.

Main Results:

• Demonstrates effective collision avoidance and maintenance of safe distances.
• Confirms the algorithm's scalability with large drone swarms (up to 25 drones).
• Shows adaptability to changing environmental conditions and formations.

Conclusions:

• The proposed mechanism offers a robust and scalable solution for drone swarm coordination.
• Its simplicity and low computational cost make it suitable for resource-constrained drones.
• Represents a significant advancement for real-world swarm applications.