基于多策略的无人驾驶飞行器的路径规划 改进的鱼优化算法
Shaoming Qiu1, Jikun Dai1, Dongsheng Zhao2
1Key Laboratory of Network and Communications, Dalian University, Dalian 116622, China.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
本研究介绍了一种改进的路径优化算法 (POA),用于无人机 (UAV) 路径规划. 改进的算法显著提高了城市环境中的路径效率和安全性.
科学领域:
- 机器人和自动化机器人与自动化
- 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:
- 有效的无人机路线规划对于优化复杂城市环境中的任务至关重要.
- 现有的算法经常与多目标优化,平衡路径长度,转角和避免碰撞作斗争.
研究的目的:
- 开发一个先进的无人机路径规划算法,以提高效率和安全.
- 为了解决无人机导航中的多约束优化挑战.
主要方法:
- 开发了一个多策略改进的路径优化算法 (IPOA).
- IPOA整合了混乱映射,折射反向学习,非线性惯性权重,勒维飞行和自适应的t分布变化.
- 该算法将路径规划转化为考虑路径长度,转向角度和避免碰撞的多约束优化问题.
主要成果:
- 在69.4%的CEC2022测试函数中,IPOA表现优于其他算法.
- 在现实世界的模拟中,IPOA与原来的POA相比,改善了8.44%的路径长度,5.82%的转角度,4.07%的避障,9.36%的飞行时间.
- 与MPOA相比,IPOA显示了4.09%的路径长度,0.76%的转角度,1.85%的避难障碍和4.21%的飞行时间的改善.
结论:
- 拟议的IPOA算法显著提高了无人机路径规划的效率和准确性.
- 对于复杂的城市导航任务,IPOA提供了一个强大的解决方案,其性能优于现有的方法.
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