自主飞行控制方法 避开在有限环境中的无人机的障碍
Tiantian Dong1,2, Yonghong Zhang1, Qianyu Xiao3
1School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了用于多旋转机无人机的增强的3D-向量场直方图 (3D-VFH) 算法,改善了在狭窄空间中的自主飞行和避障. 该方法确保了稳定的飞行和精确避免动态障碍.
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:
- 在狭窄的环境中,多旋转机无人机 (UAV) 的自主导航存在重大挑战.
- 有效地避开局部障碍物对于安全和高效的无人机操作至关重要.
- 现有的算法可能在复杂的机动中难以实时处理和保持飞行稳定.
研究的目的:
- 提出一个改进的3D-向量场基底图 (3D-VFH) 算法,用于增强多旋转无人机的自主飞行和避开障碍.
- 优化对点云数据的利用,以实时进行本地障碍物检测.
- 确保在避障机动中保持稳定的姿态和准确的轨迹.
主要方法:
- 利用基于极地坐标的目标点坐标系统来处理点云数据,专注于相关的本地信息.
- 实现了一个滑动窗口算法来估计最佳的飞行路径,并执行避障控制.
- 在动态避障过程中保持无人机立场稳定的综合方法.
主要成果:
- 在避障飞行期间,无人机表现出良好的姿态稳定性.
- 展示了无人机自主遵循其预期轨迹的能力.
- 验证了在封闭的环境中精确避免动态障碍.
结论:
- 改进的3D-VFH算法有效地提高了多旋翼无人机的自主飞行和局部避难障碍.
- 该方法提高了实时性能,并确保了稳定的飞行动态.
- 该算法实现了精确的导航,并避免了复杂环境中的静态和动态障碍.
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