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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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基于蛇优化算法的AUV搜索任务的层次规划方法

Zhiwen Wen1, Zhong Wang1, Xiangdong Wen2

  • 1Xi'an Precision Machinery Research Institute, Xi'an 710077, China.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种对自动水下车辆 (AUV) 进行分层规划的方法,以快速拦截动态目标. 该方法优化了AUV导航时间和轨迹,以便在复杂环境中高效执行搜索任务.

关键词:
自主水下车辆自主水下车辆层次化的编程方法是层次化的编程方法.搜索任务 搜索任务 搜索任务蛇优化算法 蛇优化算法

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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
  • 海洋工程 海洋工程

背景情况:

  • 自主水下车辆 (AUV) 在动态战场环境中面临挑战,以拦截目标.
  • 有效的全球自主规划对于AUV搜索任务的完成至关重要.

研究的目的:

  • 开发一个分层的AUV任务规划方法,以尽量减少与动态目标相遇的时间.
  • 处理初始目标信息可用的场景.

主要方法:

  • 一种三级层次的层次编程方法与蛇优化算法相结合.
  • 搜索任务的分解为外部 (遭遇时间),中间 (导航时间) 和内部 (轨迹优化) 层.
  • 优化考虑诸如威胁区域,路径长度和路径平滑等约束因素.

主要成果:

  • 拟议的方法通过对AUV搜索任务的模拟来验证.
  • 在各种约束条件下对AUV进行有效的任务规划.
  • 成功优化了AUV和动态目标之间的相遇时间.

结论:

  • 层次规划方法为AUV动态目标搜索提供了可行和实用的解决方案.
  • 该方法优化了AUV性能和碰撞时间,显示了重要的工程价值.
  • 为类似水下无人系统的任务规划提供了有价值的参考.