一个混合APF-DQN框架与基于变压器的当前预测,用于在动态海洋环境中的USV路径规划
Nanjie Zhang1, Yuquan Chen2, Yunshan Wu3
1College of Artificial Intelligence and Automation, Hohai University, Changzhou, 213000, China.
Scientific reports
|December 30, 2025
概括
本研究介绍了一种人工潜力场导向深度Q网络 (APF-DQN) 用于无人驾驶地面车辆 (USV) 路径规划. 这种新的方法通过整合海洋电流预测和提高能源效率来增强复杂海洋环境中的航行能力.
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:
- 无人驾驶地面车辆 (USV) 的路径规划是复杂的,因为海流和障碍物.
- 现有的方法缺乏强大的时间预测和基于物理和基于学习的方法的有效整合.
研究的目的:
- 开发一种新的路径规划框架,用于在具有挑战性的海洋环境中使用USV.
- 增强时间预测能力,并协同基于物理和基于学习的方法.
主要方法:
- 一个多尺度的变压器架构,用于高精度的海洋电流场预测.
- 一个增强的自适应人工潜力场 (APF),具有动态的电流诱导力和局部最小的逃逸.
- 一个基于中位数Q值的探索机制和APF的损失函数,用于深度Q网络 (DQN) 集成.
主要成果:
- 拟议的APF-DQN框架在模拟中实现了100%的路径成功率.
- 与基线方法相比,证明轨迹长度减少了14.7%.
- 在能源消耗方面实现了37.7%的下降.
结论:
- 在复杂的海洋环境中,APF-DQN框架为USV路径规划提供了强大的和高效的解决方案.
- 集成基于变压器的电流预测和增强的APF显著提高了导航性能.
- 该方法为自主海洋导航系统提供了一个有希望的方向.
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