混合群体智能和人类启发的优化用于城市无人机路径规划
Yidao Ji1, Qiqi Liu2, Cheng Zhou1
1School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Biomimetics (Basel, Switzerland)
|March 26, 2025
概括
这项研究提高了城市无人机路径规划,使用由集体智能启发的优化粒子群优化算法. 新方法确保在复杂的城市环境中更安全,更流,更节能地进行无人机导航.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
- 计算优化计算优化
背景情况:
- 城市无人机操作需要先进的路径规划,以确保安全和高效的导航.
- 现有的算法经常与复杂的城市环境作斗争,导致非最佳路线和潜在的安全问题.
研究的目的:
- 为城市无人机开发一个增强的路径规划算法.
- 在复杂的城市环境中提高航行安全,效率和轨道平滑性.
主要方法:
- 将层次结构和群体交互行为集成到粒子集群优化 (PSO) 中.
- 对竞争和支持行为进行数学建模,以增强PSO的学习和全球搜索.
- 引入一种突变机制,以防止局部最佳趋同并改善多样性.
- 路径细分,优先更新和立方B-spline曲线算法的结合用于路径生成.
主要成果:
- 与标准方法相比,拟议的方法产生了更顺的无人机轨迹.
- 实时性能有所改善,能耗和运行时间大大减少.
- 通过复杂的城市环境中的比较模拟来验证有效性.
结论:
- 这种新的方法显著提升了城市无人机路径规划能力.
- 该方法确保了安全,最佳和高效的无人机导航,扩大了在各种城市场景中的适用性.
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