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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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研究多无人机自主避障算法,集成改进的动态窗口方法和ORCA.

Xucheng Chang1, Jingyu Wang2, Kang Li3

  • 1School of Automation, Zhengzhou University of Aeronautics, Zhengzhou, 450046, China.

Scientific reports
|April 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种改进的动态窗口方法 (DWA) 融合算法,用于在复杂环境中高效地避免无人机障碍. 改进的算法显著减少了多无人机系统的飞行路径长度,任务时间和代.

关键词:
自主避开障碍的自主方法改进了 DWA 算法.这就是ORCA算法.无人机无人驾驶飞行器 (UAV) 是一种无人机.

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科学领域:

  • 机器人技术和自主系统
  • 人工智能的人工智能
  • 控制理论 控制理论

背景情况:

  • 传统的动态窗口方法 (DWA) 算法在未知和复杂的环境中表现出低效率.
  • 现有的DWA方法往往缺乏全球视角,并努力平衡计算速度与准确性.
  • 环境适应性差,无人机间避障能力有限,阻碍了传统算法的性能.

研究的目的:

  • 开发一个改进的DWA聚变算法,以提高无人机在复杂,未知的环境中避开障碍.
  • 解决传统DWA的局限性,包括缺乏全球视角,计算效率挑战和环境适应性差.
  • 将改进的DWA与最佳相互避免碰撞 (ORCA) 集成在一起,以有效地避免多个无人机的协作障碍.

主要方法:

  • 引入了双向搜索策略,以增强计划轨迹的全球视角.
  • 设计了一个动态时间步调整机制,以平衡计算效率和准确性.
  • 开发了一个带有可变权重的轨迹评估函数,以提高环境适应性.
  • 集成了改进的DWA算法与最佳相互避免碰撞 (ORCA) 方法,用于多无人机协作.

主要成果:

  • 与传统的DWA算法相比,无人机飞行路径长度减少了27.90%.
  • 任务完成时间减少了17.01%.
  • 代数量减少了21.5%,表明计算效率有所提高.
  • 模拟实验验证了提议的改进的融合算法的有效性.

结论:

  • 改进的DWA融合算法在复杂和未知的环境中在无人机避难障碍方面提供了卓越的性能.
  • 与ORCA的整合增强了多个无人机的协作避障能力.
  • 该算法在自主多UAV系统中的工程应用中具有显著的实际价值.