A Time-efficient Prioritised Scheduling Algorithm to Optimise Initial Flock Formation of Drones

TL;DR

The paper tackles efficient initial flock formation for drone swarms under collision constraints by introducing Time-efficient Prioritised Scheduling (TPS), which imposes calculated start delays on straight-line paths from $S^i_\text{str}$ to $S^i_\text{tgt}$ to guarantee collision-free trajectories while minimising flocking time. TPS constructs a hierarchy of drones using collision matrices and employs a two-stage process: (i) hierarchy determination via the Extended Collision (ECL) framework, and (ii) delay calculation across three collision scenarios, with delays found through binary search where appropriate. The approach demonstrates scalability to up to 5,000 drones and outperforms the coupling-degree-based heuristic prioritized planning (CDH-PP) in simulations, while maintaining fixed straight-line paths to reduce computation. The work provides a practical, scalable method for rapid, reliable drone swarm formation in obstacle-free settings and lays groundwork for extending to obstacle-rich and dynamic environments. These findings have implications for real-world applications like large-scale drone shows, environmental monitoring, and distributed sensing where fast, collision-free formation is essential.

Abstract

Drone applications continue to expand across various domains, with flocking offering enhanced cooperative capabilities but introducing significant challenges during initial formation. Existing flocking algorithms often struggle with efficiency and scalability, particularly when potential collisions force drones into suboptimal trajectories. This paper presents a time-efficient prioritised scheduling algorithm that improves the initial formation process of drone flocks. The method assigns each drone a priority based on its number of potential collisions and its likelihood of reaching its target position without permanently obstructing other drones. Using this hierarchy, each drone computes an appropriate delay to ensure a collision-free path. Simulation results show that the proposed algorithm successfully generates collision-free trajectories for flocks of up to 5000 drones and outperforms the coupling-degree-based heuristic prioritised planning method (CDH-PP) in both performance and computational efficiency.

Figures (12)

• Figure 1:
• Figure 2: Velocity vs. time graph for a drone travelling in a straight line
• Figure 3: Velocity vs. time graph for a drone travelling in a straight line with a start delay and end waiting time
• Figure 4: Displacement vs. time graph for a drone travelling in a straight line with a start delay and end waiting time
• Figure 5: Example of the shortest distance between any two line segments.