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改进的人工潜力场算法,辅助多源数据用于AUV路径规划.

Tianyu Xing1, Xiaohao Wang1, Kaiyang Ding1

  • 1Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
概括

本研究介绍了一种改进的人工潜力场 (IAPF) 方法,与深度强化学习 (DRL) 结合,用于自动水下车辆 (AUV) 路径规划. 该方法提高了在复杂的水下环境中避免障碍物和提高了导航效率.

关键词:
自动水下车辆 (AUV) 是一种自动驾驶水下车辆.深度强化学习 (DRL) 是一种深度强化学习.提高人工潜力 提高现场潜力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
  • 海洋工程 海洋工程
  • 人工智能的人工智能

背景情况:

  • 海洋勘探在复杂环境中的任务中严重依赖自主水下车辆 (AUV).
  • 有效和安全的路径规划对于AUV来说至关重要,以迅速和顺利地到达目标点.
  • 现有的路径规划算法经常将深度强化学习 (DRL) 与其他避难障碍和路径缩短方法相结合.

研究的目的:

  • 为解决AUV路径规划人工潜力场 (APF) 方法中的局部最小问题.
  • 为了提高AUV导航在具有挑战性的水下条件的效率和安全性.
  • 通过改进APF并将其与DRL集成来开发一种新的路径规划方法.

主要方法:

  • 提出了一种改进的人工潜力场 (IAPF) 方法,通过引入引力来克服局部最小值.
  • 整合了IAPF方法与深度强化学习 (DRL) 进行自主水下车辆路径规划.
  • 优化了DRL算法中的奖励函数,并利用生成的路径来改进未来的导航.

主要成果:

  • 拟议的IAPF-DRL方法在AUV路径规划方面显示出积极的影响和优势.
  • 实验性比较显示,开发的方法比现有的算法性能优越.
  • 这种方法被证明是有效的,使AUV能够逃避局部最小值并实现高效的路径规划.

结论:

  • 开发的IAPF-DRL方法为AUV路径规划提供了高效和安全的解决方案.
  • 这种方法显示了改善水下导航设备的巨大潜力.
  • 该研究为提高AUV在复杂海洋环境中的任务成功提供了有价值的方法.