Enhancing Drone Light Shows Performances: Optimal Allocation and Trajectories for Swarm Drone Formations
Yunes Alqudsi

TL;DR
This paper presents a scalable, real-time framework for optimal drone assignment and collision-free trajectory generation, significantly advancing large-scale drone light show coordination.
Contribution
The paper introduces the UATG framework that simultaneously solves drone assignment and trajectory planning efficiently for large swarms, enabling real-time aerial displays.
Findings
Computes optimal assignment and trajectories for 1008 drones in about one second.
Demonstrates complex formations with high precision and smoothness.
Validates the framework through extensive realistic simulations.
Abstract
Drone light shows (DLShows) represent a rapidly growing application of swarm robotics, creating captivating aerial displays through the synchronized flight of hundreds or thousands of unmanned aerial vehicles (UAVs) as environmentally friendly and reusable alternatives to traditional pyrotechnics. This domain presents unique challenges in optimally assigning drones to visual waypoints and generating smooth, collision-free trajectories at a very large scale. This article introduces the Unified Assignment and Trajectory Generation (UATG) framework. The proposed approach concurrently solves two core problems: the optimal assignment of drones to designated goal locations and the generation of dynamically feasible, collision-free, time-parameterized trajectories. The UATG framework is specifically designed for DLShows, ensuring minimal transition times between formations and guaranteeing…
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Taxonomy
TopicsRobotic Path Planning Algorithms · UAV Applications and Optimization · Distributed Control Multi-Agent Systems
