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
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.
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.
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