SwarmNxt：面向快速敏捷空中集群的开源软硬件平台

中文摘要 AI 辅助

空中机器人集群有潜力变革对时间要求严苛的安全、安保以及搜索与救援行动。通过协调多台机器人，它们能够比单台机器人更快地勘察灾难现场、绘制坍塌或GPS拒止环境的地图，并搜索杂乱区域，从而缩短响应时间并最大程度降低急救人员的风险。然而，要实现这一潜力，需要鲁棒的自主集群导航，而这仍是一个活跃的研究挑战。现有平台进一步制约了进展，因为商用无人机通常是闭源的，或缺乏敏捷的、基于视觉的集体飞行所需的机载计算资源。此外，在多台空中机器人上开发、部署和维护软件需要大量的工程投入。为应对这些挑战，我们提出了SwarmNxt，一个构建于开源OmniNxt无人机硬件之上的开源软件平台。SwarmNxt提供端到端工具包，包括带有视频教程的详细硬件组装说明、用于并行软件部署和集群范围更新的自动化工具，以及基于ROS 2的自主导航框架。该平台将最先进的控制、规划和深度估计集成到单个ROS 2多智能体系统中，为物理集群实验提供了开放的研究基础设施。我们通过两个真实世界实验验证了SwarmNxt：一个由六架无人机组成的集群执行分布式规划并具备高速无人机间碰撞避免，以及一个由四架无人机组成的集群在充满障碍物的环境中执行带有板载深度估计的集体飞行。两个实验均在室内进行，使用外部运动捕捉提供全局位置；感知、规划和控制均在机载运行。

英文摘要

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.

发表机构

• Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）
• Hong Kong University of Science and Technology（香港科技大学）

机构由 AI 辅助整理，请以论文原文为准。

相关深度报道

• 6架无人机高速互避，EPFL、港科大公开整套集群软硬件