A Distributed Algorithm for Real-Time Multi-Drone Collision-Free Trajectory Replanning.

Area of Science:

• Robotics
• Control Systems
• Computational Geometry

Background:

• Multi-agent systems, particularly quadrotor swarms, require sophisticated trajectory planning for safe operation in shared environments.
• Existing methods often struggle with scalability and computational efficiency when dealing with a large number of agents.

Purpose of the Study:

• To develop a distributed algorithm for generating collision-free trajectories for multiple quadrotors.
• To enhance the robustness and efficiency of multi-quadrotor path planning.

Main Methods:

• A distributed, receding-horizon optimization approach is employed, where each quadrotor replans its trajectory using local variables.
• Voronoi partitioning of space is utilized to establish local collision avoidance constraints.
• Vehicle orientation and Bézier curves are incorporated to ensure feasibility and efficient, discretization-free constraint evaluation.

Main Results:

• The algorithm successfully generates collision-free trajectories for up to 100 quadrotors in simulations.
• The proposed method demonstrates a higher success rate compared to other Voronoi-based approaches for large-scale drone groups.
• The incorporation of orientation and Bézier curves improves the feasibility and efficiency of collision avoidance.

Conclusions:

• The developed distributed algorithm offers an effective solution for collision-free trajectory generation in multi-quadrotor systems.
• The approach is scalable and computationally efficient, outperforming existing methods in complex scenarios.
• This work contributes to safer and more reliable autonomous operation of drone swarms.