一个光敏度增强的植物生长算法用于无人机路径规划
Renjie Yang1, Pan Huang2,3, Hui Gao1
1School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.
Biomimetics (Basel, Switzerland)
|April 26, 2024
概括
一种新的光敏感度增强植物生长算法 (PEPG) 改善了无人机低空透路径的规划. 与传统算法相比,这种方法提高了安全性,并将路径长度减少了超过8.2%.
科学领域:
- 机器人技术和自主系统
- 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:
- 无人机对于国家安全至关重要,需要安全的低空透能力.
- 现有的路径规划算法与复杂的海洋环境和无人机动力学约束作斗争.
研究的目的:
- 为在复杂的海洋环境中低海拔运行的无人机开发先进的路径规划算法.
- 提高无人机飞行路径的安全性,效率和顺性.
主要方法:
- 使用S-57电子图表数据构建了一个海上战斗环境.
- 开发了一种光敏度增强的植物生长算法 (PEPG),改进了植物生长路径规划算法 (PGPP).
- 集成无人机动力学约束,包括转角度,进入路径规划过程.
主要成果:
- 与最初的PGPP相比,PEPG算法产生了更光滑的路径,长度减少了至少8.2%.
- 模拟测试表明,PEPG在路径长度和质量方面优于A*,RRT和GA算法.
- 自主跟踪飞行测试验证了计划路径的可行性和安全性.
结论:
- PEPG算法在无人机低空透路径规划方面取得了重大进展.
- 在复杂的环境中,PEPG为无人机提供了更高效,更安全,更顺的飞行路径解决方案.
- 算法的有效性通过模拟和现实世界飞行测试来验证.
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