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一种基于深度的混合方法,用于在无记忆规划中安全的飞行走廊生成
Thai Binh Nguyen1, Manzur Murshed2, Tanveer Choudhury1
1Institute of Innovation, Science and Sustainability, Federation University Australia, Churchill, VIC 3842, Australia.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种基于深度的新方法,用于生成安全的飞行走廊,这对于自主导航至关重要. 这种方法确保了无碰撞的路径与最小的重叠,提高无人机在复杂环境中的安全性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 自主系统需要在未知的环境中可靠的导航.
- 地方规划往往依赖于事先存在的地图,限制了实时的适应性.
- 创建安全的飞行走廊对于避免碰撞至关重要.
研究的目的:
- 开发一种基于深度的混合方法,用于生成安全的飞行走廊.
- 为了使没有内存的本地导航计划器能够有效地运行.
- 确保无碰撞的走廊与路径规划的最小重叠.
主要方法:
- 使用原始深度图像作为基于学习的对象检测引擎的直接输入.
- 使用物体检测网络来预测多面体安全走廊.
- 实施一个验证程序,以保证无碰撞的走廊.
- 尽量减少走廊重叠,实现整个欧盟 (IoU) 的平均交叉率低于2%.
主要成果:
- 成功集成到一个没有内存的规划器,使用直线路径规划算法.
- 在合成和现实世界障碍密集的环境中表现出高的成功率.
- 该方法有效地产生了独立的,无碰撞的安全走廊.
结论:
- 提出的基于深度的混合方法具有很高的能力,可以为无记忆的当地规划生成安全的走廊.
- 这种方法消除了对地图融合的需求,使实时导航成为可能.
- 这种技术在复杂场景中显著提高了自动飞行的安全性和效率.
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