Gen-Swarms: Adapting Deep Generative Models to Swarms of Drones

2024-08-28

arXiv.org

AI Summary

Existing 3D point cloud generation models are ill-suited for drone swarm formation control due to non-smooth trajectory outputs and the absence of explicit collision-avoidance modeling. This paper introduces the first joint 3D shape and motion generation framework tailored for drone light shows: it adapts Flow Matching—a continuous normalizing flow technique—to 3D point cloud space and integrates a real-time reactive navigation module into the sampling process. The framework enables text-conditioned (e.g., “Airplane”) generation of high-fidelity 3D swarm configurations alongside collision-free, smooth, and dynamically feasible trajectories in a unified manner. Evaluated in full-scale realistic simulations, our method demonstrates both computational efficiency and operational safety, significantly enhancing the practicality and execution readiness of generated trajectories compared to prior approaches.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Motion and Path Planning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Gen-Swarms is an innovative method that leverages and combines the capabilities of deep generative models with reactive navigation algorithms to automate the creation of drone shows. Advancements in deep generative models, particularly diffusion models, have demonstrated remarkable effectiveness in generating high-quality 2D images. Building on this success, various works have extended diffusion models to 3D point cloud generation. In contrast, alternative generative models such as flow matching have been proposed, offering a simple and intuitive transition from noise to meaningful outputs. However, the application of flow matching models to 3D point cloud generation remains largely unexplored. Gen-Swarms adapts these models to automatically generate drone shows. Existing 3D point cloud generative models create point trajectories which are impractical for drone swarms. In contrast, our method not only generates accurate 3D shapes but also guides the swarm motion, producing smooth trajectories and accounting for potential collisions through a reactive navigation algorithm incorporated into the sampling process. For example, when given a text category like Airplane, Gen-Swarms can rapidly and continuously generate numerous variations of 3D airplane shapes. Our experiments demonstrate that this approach is particularly well-suited for drone shows, providing feasible trajectories, creating representative final shapes, and significantly enhancing the overall performance of drone show generation.

Problem

• Adapts generative models for automated drone show creation
• Generates feasible 3D shapes and swarm motion trajectories
• Integrates collision avoidance in drone swarm navigation

Innovation

• Combines deep generative models with reactive navigation
• Adapts flow matching for 3D drone swarm trajectories
• Generates collision-free shapes and smooth motions