全球导向的基于深度V网络的动作计划算法,用于固定翼无人机
Hang Du1, Ming You2, Xinyi Zhao1
1Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
这项研究引入了一种新的等级运动计划算法,用于固定翼无人机 (UAV). 该算法确保在复杂的环境中安全,无碰撞飞行,具有静态和动态障碍.
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
- 航空航天工程 航空航天工程
背景情况:
- 固定翼无人机为各种应用提供了巨大的潜力.
- 在具有静态和动态障碍的复杂环境中安全导航仍然是一个挑战.
研究的目的:
- 为固定翼无人机开发一个分层的双层运动规划算法.
- 为了实现安全,无碰撞和动力学上可行的飞行路径.
主要方法:
- 全球规划器生成参考指南,考虑动力学约束和静态障碍.
- 一个局部强化学习 (RL) 计划器处理传感范围内的动态障碍物.
- 为了模拟,采用了多阶段,多场景的培训策略.
主要成果:
- 拟议的算法在模拟中显示出与基线方法相比的优异性能.
- 实现了与静态和动态障碍物有效避免碰撞.
- 在整个规划过程中保持了动力学可行性.
结论:
- 层次的双层算法为固定翼无人机运动规划提供了有效的解决方案.
- 基于RL的地方规划提高了动态环境中的安全性和适应性.
- 拟议的方法显著提高了无人机的飞行安全和效率.
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