一个基于风险的无人驾驶飞行器路径规划方案,用于复杂的空地环境
Kai Zhou1, Kai Wang1, Yuhao Wang1
1School of Vehicle and Mobility, Tsinghua University, Beijing, China.
Risk analysis : an official publication of the Society for Risk Analysis
|December 22, 2024
概括
本研究引入了无人飞行器 (UAV) 的新风险评估模型,该模型平衡了操作第三方风险 (TPR) 和经济成本. A*路径规划算法优化无人机路线,最大限度地降低风险,最大限度地提高商业化效益.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 风险管理 风险管理
背景情况:
- 无人驾驶飞行器 (UAV) 有多种应用,但潜在的第三方风险 (TPR) 阻碍了商业化.
- 当前的风险评估模型往往忽略了无人机毁的可能性.
- 优化与TPR相比的运营成本对于广泛采用无人机至关重要.
研究的目的:
- 为无人机运营开发一个改进的风险评估模型.
- 使用路径规划算法,优化运营TPR成本和经济成本之间的权衡.
- 提高商业无人机部署的安全性和经济可行性.
主要方法:
- 将无人机机概率纳入TPR评估模型.
- 应用A*路径规划算法来优化飞行路径.
- 对最佳先搜索和Dijkstra的算法进行比较分析.
- 在中国主要城市使用真实数据模拟无人机路径网络.
主要成果:
- 与其他算法相比,A*算法在平衡TPR和经济成本方面表现优越.
- 优化路径最初显示距离略微增加以显著减少TPR,后来转向距离减少以最小增加TPR.
- 模拟的无人机网络具有分层的单向路径,通常延伸到沿海地区,以尽量减少TPR.
结论:
- 拟议的A*路线规划方法有效地减轻无人机操作中的第三方风险.
- 该模型提供了一种可行的策略,以最大限度地提高商业无人机应用中的整体效益.
- 网络配置建议进行战略路线规划,特别是在沿海地区,以减少运营风险.
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