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

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.

Figures (8)

• Figure 1: Drone light show system architecture illustrating the integrated components: centralized control software, drone fleet with positioning and illumination systems, communication infrastructure, GCS, and redundancy mechanisms for reliable operation.
• Figure 2: Workflow of a DLShow performance illustrating the sequential phases. The sequence begins with synchronized takeoff and formation initialization, followed by smooth transitions between formations at $t=\{t_1, t_2, t_3, t_4\}$ with LED illumination patterns. Pre-programmed trajectory guidance ensures formation dynamics, concluding with a controlled landing.
• Figure 3: UATG Problem Visualization showing initial drone positions ($r_i$), target goal locations ($G_i$), and generated 3D trajectories. The illustration highlights collision-free path planning and optimal assignment in obstacle-free environments.
• Figure 4: Sequential numerical formations demonstrating the UATG algorithm's capability to transform a 16-drone swarm through digits 1, 2, and 3. The sequence illustrates smooth transitions between formations while maintaining optimal assignments and collision-free trajectories.
• Figure 5: Alphabetical character formations (T, S, A, G) generated by a 16-drone swarm, showcasing the algorithm's flexibility in creating complex letter-based patterns with efficient task allocation and synchronized movements.