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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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DBO-AWOA:一种适应性鱼优化算法,用于全球优化和无人机3D路径规划.

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  • 1School of Automation, Shenyang Aerospace University, Shenyang 110136, China.

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概括
此摘要是机器生成的。

本研究介绍了一种适应性鱼优化算法 (DBO-AWOA),用于增强无人机 (UAV) 3D路径规划. 在复杂的环境中,DBO-AWOA提高了障碍回避和计算效率.

关键词:
这就是DBO-AWOA.无人机无人机无人机是什么?适应性惯性机制的适应性惯性机制混乱的地图绘制.甲虫优化器 甲虫优化器非线性收因子是指非线性收因子.路径规划路径规划路径规划

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 优化算法 优化算法

背景情况:

  • 无人机 (UAV) 应用正在迅速扩展到复杂的环境中.
  • 传统的3D路径规划方法在动态避障和计算效率方面扎.

研究的目的:

  • 为无人机在复杂环境中开发一个改进的3D路径规划算法.
  • 提高UAV路径规划中的动态避障和计算效率.

主要方法:

  • 介绍了适应性鱼优化算法 (DBO-AWOA).
  • 纳入混乱映射 (ICMIC),非线性收因子 (基于共数),自适应惯性和虫优化器启发的行为.
  • 评估CEC2017基准套件和山区环境中的3D路径规划模拟.

主要成果:

  • 在CEC2017基准套件上,DBO-AWOA表现出卓越的融合精度和稳定性,在72%的测试函数中达到最佳值.
  • 在3D路径规划中,DBO-AWOA产生了更光滑,更短,更安全的轨迹,与现有方法相比,减低了5-25%的健身值.
  • 该算法在高度动态的混合函数中显示了轻微的不稳定性,但整体上改善了全球优化.

结论:

  • DBO-AWOA为无人机3D路径规划的全球优化技术提供了显著的进步.
  • 该算法有效地解决了动态避障和计算效率的局限性.
  • 进一步的研究可能将重点放在提高高度动态场景中的稳定性上.