无人机路径规划的新技术基于邻近龙算法.
Sameer Agrawal1, Bhumeshwar K Patle1, Sudarshan Sanap1
1Department of Mechanical Engineering, MIT Art, Design & Technology University, Pune 412201, India.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
邻居龙算法 (NDA) 增强了室内应用的自主空中无人机导航. 这项研究验证了有效路径规划的NDA,显示与其他方法相比,路径长度节省了5%以上.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 自主空中无人机导航对于室内应用,如监视和搜索和救援至关重要.
- 现有的路径规划方法面临着动态环境和最佳路径发现的挑战.
研究的目的:
- 实施和验证社区龙算法 (NDA),用于在复杂的室内环境中自主无人机路径规划.
- 评估的协作行为,以有效解决空间探索和无人机导航的更快融合.
主要方法:
- 开发和实时模拟带有固定和移动障碍物的室内环境.
- 实施邻近龙算法 (NDA) 用于单个和多个无人机路径的规划.
- 对NDA与IACO和PRM等现有算法进行比较分析.
主要成果:
- NDA方法表现出强大的性能,实现了模拟和实时实验之间的路径长度和导航时间差异小于5.7%.
- NDA的表现优于IACO和PRM,在平滑路径规划和路径长度优化方面取得了显著改善,节约超过5%.
结论:
- 社区龙算法 (NDA) 为室内环境中的无人机自主导航提供了一种一致,可行和高效的解决方案.
- NDA为开发能够处理复杂场景的先进和可靠无人机导航系统提供了坚实的基础.
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