基于对象识别和跟踪的无人机自主着陆系统的模拟和现实实施,用于在不确定的环境中安全着陆
1Kpro System, Namyangju-si, Republic of Korea.
Frontiers in robotics and AI
|November 12, 2024
概括
本研究介绍了一种强大的四旋翼无人机自主着陆系统,可以在遇到障碍或缺少视觉标记的情况下安全着陆. 该系统确保在具有挑战性的环境中可靠的自主飞行操作.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 越来越多的自主性需要可靠的系统来完成关键任务阶段,如着陆.
- 自主降落能力对于无人机任务的成功至关重要,尤其是在面对环境不确定性时,如障碍物或缺少视觉线索.
研究的目的:
- 为四旋翼无人机开发和验证一种自主着陆系统,能够在恶劣条件下顺利着陆.
- 通过解决障碍回避和没有指定着陆标记等挑战,提高无人机着陆的稳定性.
主要方法:
- 集成的你只看一次版本5 (YOLOv5) 对象检测和深度SORT对象跟踪.
- 使用欧几里德距离变换在检测到的障碍物中识别安全着陆区域.
- 采用比例整合衍生 (PID) 控制器,以基于视觉反的准确无人机运动控制.
主要成果:
- 该系统成功地识别了着陆标记和常见障碍物 (人,汽车,树木).
- 证明了即使视觉标记不存在或模糊,也能够定位安全着陆区域的能力.
- 通过广泛的软件模拟和现实世界的硬件测试在各种场景中验证了系统性能.
结论:
- 开发的基于视觉的自主着陆系统显著提高了无人机的操作安全性和可靠性.
- YOLOv5,DeepSORT,欧几里德距离变换和PID控制的综合方法为复杂环境中的自主无人机着陆提供了强大的解决方案.
- 该系统的有效性已经通过严格的测试来验证,为在自主任务中实际部署铺平了道路.
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