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

• URL: http://arxiv.org/abs/2603.24401v1
• Date: Wed, 25 Mar 2026 15:16:51 GMT
• Title: Enhancing Drone Light Shows Performances: Optimal Allocation and Trajectories for Swarm Drone Formations
• Abstract summary: Drone light shows (DLShows) represent a rapidly growing application of swarm robotics.<n>This article introduces the Unified Assignment and Trajectory Generation framework.<n>The framework is specifically designed for DLShows, ensuring minimal transition times between formations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 inter-drone collision avoidance. A key innovation is its exceptional computational efficiency, enabling the coordination of large-scale in real-time; for instance, it computes the optimal assignment and trajectories for 1008 drones in approximately one second on a standard laptop. Extensive simulations in realistic environments validate the framework's performance, demonstrating its capability to orchestrate complex formations, from alphanumeric characters to intricate 3D shapes, with precision and visual smoothness. This work provides a critical advancement for the DLShow industry, offering a practical and scalable solution for generating complex aerial choreography and establishing a valuable benchmark for ground control station software designed for the efficient coordination of multiple UAVs. A supplemental animated simulation of this work is available at https://youtu.be/-Fjrhw03594.

Related papers

• Generative AI for Safe and Photorealistic Drone Light Shows [51.71264054256171]
  Drone light shows are redefining aerial entertainment, yet their widespread adoption is bottlenecked by labor-intensive, manual animation.<n>We introduce SWAN, an end-to-end pipeline that synthesizes photorealistic, large-scale, and collision-free drone choreographies directly from text prompts.<n>We demonstrate scalability by safely orchestrating simulated 2,000-drone formations and validate physical feasibility on a dense real-world swarm of 49 quadcopters.
  arXiv  Detail & Related papers  (2026-06-24T06:34:44Z)
• Precision Meets Art: Autonomous Multi-UAV System for Large Scale Mural Drawing [0.0]
  This paper presents the design, deployment, and testing of a novel multi-drone system for automated mural painting in outdoor settings.<n>New software that coordinates multiple drones simultaneously, utilizing state-machine algorithms for task execution.<n>A 100 square meters mural was created using the developed multi-drone system, validating the system's efficacy.
  arXiv  Detail & Related papers  (2026-01-10T10:00:23Z)
• Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
  Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
  arXiv  Detail & Related papers  (2025-10-28T10:05:53Z)
• NoTVLA: Narrowing of Dense Action Trajectories for Generalizable Robot Manipulation [54.87964060934928]
  Vision-Language-Action (VLA) models confront critical barriers to real-world deployment, most notably catastrophic forgetting.<n>We propose the Narrowing of Trajectory VLA framework: a novel approach that narrows its focus to sparse trajectories.<n>NoTVLA achieves superior performance and generalization compared to pi0 while operating under two critical constraints.
  arXiv  Detail & Related papers  (2025-10-04T18:26:55Z)
• SpectraSentinel: LightWeight Dual-Stream Real-Time Drone Detection, Tracking and Payload Identification [0.0903415485511869]
  The proliferation of drones in civilian airspace has raised urgent security concerns.<n>In response to the 2025 VIP Cup challenge tasks, we propose a dual-stream drone monitoring framework.<n>Our approach deploys independent You Only Look Once v11-nano (YOLOv11n) object detectors on parallel infrared (thermal) and visible (RGB) data streams.
  arXiv  Detail & Related papers  (2025-07-30T13:10:13Z)
• Open-World Drone Active Tracking with Goal-Centered Rewards [62.21394499788672]
  Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose DAT, the first open-world drone active air-to-ground tracking benchmark.<n>We also propose GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios.
  arXiv  Detail & Related papers  (2024-12-01T09:37:46Z)
• Learning Motion Blur Robust Vision Transformers for Real-Time UAV Tracking [14.382072224997074]
  Unmanned aerial vehicle (UAV) tracking is critical for applications like surveillance, search-and-rescue, and autonomous navigation.<n>The high-speed movement of UAVs and targets introduces unique challenges, including real-time processing demands and severe motion blur.<n>We propose an adaptive computation framework that dynamically exits Transformer blocks for real-time UAV tracking.
  arXiv  Detail & Related papers  (2024-07-07T14:10:04Z)
• Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach [57.15309977293297]
  The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses. We propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses.
  arXiv  Detail & Related papers  (2024-01-18T02:14:13Z)
• TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
  Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
  arXiv  Detail & Related papers  (2022-10-16T03:05:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.