外观
论文arXiv 2609.113821 个月前
SwarmNxt：面向快速敏捷空中集群的开源软硬件平台
原文链接：SwarmNxt: Open-source Software-Hardware Platform for Fast and Agile Aerial Swarms（arXiv 2609.11382 · 发布日期：2026-09-10）
SwarmNxt: Open-source Software-Hardware Platform for Fast and Agile Aerial Swarms
针对敏捷视觉集群飞行平台受限问题，SwarmNxt基于OmniNxt和ROS 2提供开源工具链，并在室内真机多无人机实验中验证。
核心要点
- 空中机器人集群有望提升救援等时效任务，但自主集群导航和现有平台仍受限制。
- SwarmNxt基于开源OmniNxt硬件，提供装配教程、并行部署和群体更新工具。
- 其ROS 2多智能体框架集成控制、规划和深度估计，支持机载自主导航研究。
- 真机实验展示六机高速避碰分散规划，以及四机在障碍环境中凭机载深度估计集体飞行。
验证范围与局限：验证仅限室内真机实验，使用外部动作捕捉提供全局位置，包括六机和四机两类飞行任务。
英文摘要 (Abstract)
Aerial robot swarms have the potential to transform time-critical safety, security, and search-and-rescue operations. By coordinating multiple robots, they can rapidly survey disaster sites, map collapsed or GPS-denied environments, and search cluttered areas faster than a single robot, reducing response times and minimizing risks to first responders. Realizing this potential, however, requires robust autonomous swarm navigation, which remains an active research challenge. Progress is further constrained by existing platforms, as commercial drones are often closed-source or lack the onboard computational resources needed for agile, vision-based collective flight. Moreover, developing, deploying, and maintaining software across multiple aerial robots requires significant engineering effort. To address these challenges, we present SwarmNxt, an open-source software platform built on the open-source OmniNxt drone hardware. SwarmNxt provides an end-to-end toolkit, including detailed hardware assembly instructions with a video tutorial, automation tools for parallel software deployment and swarm-wide updates, and a ROS 2-based framework for autonomous navigation. The platform integrates state-of-the-art control, planning, and depth estimation into a single ROS 2 multi-agent system, providing an open research infrastructure for physical swarm experimentation. We validate SwarmNxt through two real-world experiments: a six-drone swarm performing decentralized planning with high-speed inter-drone collision avoidance, and a four-drone swarm executing collective flight with onboard depth estimation in an obstacle-filled environment. Both experiments were run indoors with global position from external motion capture; perception, planning, and control run onboard.
作者：Charbel Toumieh, Niel Mistry, Benjamin Jarvis, Simon Luis Jeger, Peize Liu, Shaojie Shen, Dario Floreano
来源
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