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使用ARCog-NET实现单眼视觉SLAM,用于空中机器人群室内映射
Gabryel Silva Ramos1, Milena Faria Pinto1, Fabio A A Andrade2
1Control and Automation Laboratory (LACEA), Federal Center for Technological Education Celso Suckow da Fonseca (CEFET-RJ), Rio de Janeiro, 20271-110, Brazil.
Scientific reports
|December 22, 2025
概括
这项研究引入了一个实时分布式框架,用于在GPS被拒绝的地区无人机 (UAV) 群. 该系统使用视觉SLAM实现自主导航和协作3D绘图,增强群体智能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 在没有GPS的室内环境中运行无人机 (UAV) 群体带来了重大本地化和协调挑战.
- 现有的框架往往缺乏复杂的实时操作所需的去中心化决策和适应能力.
研究的目的:
- 为无人机群体在没有GPS的室内环境中提供实时分布式框架.
- 通过认知架构和视觉SLAM实现自主导航和协作3D绘图.
主要方法:
- 该框架使用空中机器人认知网络架构 (ARCog-NET),具有多层边缘-雾-云 (EFC) 层次结构.
- 单眼视觉同时定位和映射 (SLAM) 集成用于联合轨迹估计和环境重建.
- 强化学习基于覆盖度指标和历史数据动态调整导航路径.
主要成果:
- 一个由六个无人机组成的群体成功地在受控的实验室环境中展示了自主导航和协作3D绘图.
- 该系统实现了准确的轨迹估计和高保真度点云重建.
- 评估了包括轨迹错误,决策融合和知识重用率在内的关键绩效指标.
结论:
- 拟议的方法使可扩展,自主SLAM和规划用于现实世界无人机网络.
- 它强调了分布式空中机器人中的导航和感知之间的认知协同作用.
- 该框架为挑战性室内环境中的复杂任务提供了强大的解决方案.
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