SwarmBox: A Plug-and-Play Drone Swarm Framework for Streamlined Development and Comprehensive Analysis

Drone swarms are emerging as paradigm-shifting technology with the potential to redefine traditional robot missions such as logistics, surveillance, and disaster response, through their ability to coordinate large numbers of autonomous drones. Yet, progress in swarm research and development is constrained by a fragmented development ecosystem; every new algorithm must be validated on a bespoke testbed, introducing significant and redundant engineering overhead, while producing results that are difficult to reproduce or compare. As a remedy, we present SwarmBox, an open-source testbed framework that provides a shared foundation for swarm robotics. SwarmBox streamlines swarm research and development by providing three key capabilities: (1) a plug-and-play software architecture that decouples high-level swarm logic from low-level flight control and platform dependencies; (2) a swarm-level integrated analyzer that exposes emergent behaviors across drones and system layers to facilitate debugging and analysis; and (3) a configurable experimentation environment that supports diverse missions and communication topologies to promote fair and reproducible benchmarking. Our evaluation demonstrates these benefits in practice. SwarmBox reduces software engineering effort by eliminating boilerplate code, abstracting low-level system details into coherent APIs, and simplifying swarm coordination into a uniform process. It improves fault diagnosis efficiency and uniquely exposes inter-agent interaction failures that traditional debugging methods cannot capture. It enables reproducible benchmarking by allowing systematic comparison of different swarm algorithms. It proves its generality and scalability by supporting diverse mission scenarios ranging from centralized coordination to fully decentralized swarms. Finally, its hardware abstraction layer minimizes the simulation-to-real gap, enabling unmodified application code to be seamlessly deployed on both simulation and physical drones. Together, these capabilities establish SwarmBox as a practical foundation for reproducible, community-driven swarm robotics research.

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2026-06-30
DOI
https://doi.org/10.1145/3808100
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

SwarmBox: A Plug-and-Play Drone Swarm Framework for Streamlined Development and Comprehensive Analysis

Seulbae Kim, Minki Lee, Seojin Lee
Proceedings of the ACM on software engineering.
UAV Applications and Optimization
article

SwarmBox: A Plug-and-Play Drone Swarm Framework for Streamlined Development and Comprehensive Analysis

Seulbae Kim, Minki Lee, Seojin Lee
article en

Abstract

Drone swarms are emerging as paradigm-shifting technology with the potential to redefine traditional robot missions such as logistics, surveillance, and disaster response, through their ability to coordinate large numbers of autonomous drones. Yet, progress in swarm research and development is constrained by a fragmented development ecosystem; every new algorithm must be validated on a bespoke testbed, introducing significant and redundant engineering overhead, while producing results that are difficult to reproduce or compare. As a remedy, we present SwarmBox, an open-source testbed framework that provides a shared foundation for swarm robotics. SwarmBox streamlines swarm research and development by providing three key capabilities: (1) a plug-and-play software architecture that decouples high-level swarm logic from low-level flight control and platform dependencies; (2) a swarm-level integrated analyzer that exposes emergent behaviors across drones and system layers to facilitate debugging and analysis; and (3) a configurable experimentation environment that supports diverse missions and communication topologies to promote fair and reproducible benchmarking. Our evaluation demonstrates these benefits in practice. SwarmBox reduces software engineering effort by eliminating boilerplate code, abstracting low-level system details into coherent APIs, and simplifying swarm coordination into a uniform process. It improves fault diagnosis efficiency and uniquely exposes inter-agent interaction failures that traditional debugging methods cannot capture. It enables reproducible benchmarking by allowing systematic comparison of different swarm algorithms. It proves its generality and scalability by supporting diverse mission scenarios ranging from centralized coordination to fully decentralized swarms. Finally, its hardware abstraction layer minimizes the simulation-to-real gap, enabling unmodified application code to be seamlessly deployed on both simulation and physical drones. Together, these capabilities establish SwarmBox as a practical foundation for reproducible, community-driven swarm robotics research.

Proceedings of the ACM on software engineering.Vol. 3(FSE)
Pohang University of Science and Technology (KR), Daegu Gyeongbuk Institute of Science and Technology (KR)
National Research Foundation of Korea
Climate action
Openalex Percentile: Top 4%
UAV Applications and Optimization
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