一种新的混合增强学习与人工潜力场方法用于无人机目标搜索目标搜索
Fang Jin1, Zhihao Ye1, Mengxue Li2
1College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究介绍了一种混合框架,将强化学习 (RL) 和人工潜力场 (APF) 结合起来,用于无人机 (UAV) 导航. 新方法显著提高了目标搜索效率和在复杂环境中避免障碍.
科学领域:
- 机器人和自动化机器人与自动化
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 无人驾驶飞行器 (UAV) 对于搜救和监视等任务至关重要.
- 强化学习 (RL) 对无人机导航具有前景,但在大规模环境中难以实现缓慢的融合和效率.
- 现有的RL方法在复杂,动态的环境中面临目标搜索和障碍回避的局限性.
研究的目的:
- 开发一种改进的无人机目标搜索算法,采用混合方法.
- 为了提高基于RL的无人机导航的效率和计算性能.
- 在大型环境中解决独立RL的局限性,以避免障碍.
主要方法:
- 开发了一个混合框架,将强化学习 (RL) 与人工潜力场 (APF) 整合在一起.
- 构建了一个特定于无人机目标搜索的任务场景和培训环境.
- 混合RL-APF框架被训练并与独立RL算法进行比较.
主要成果:
- 拟议的混合框架与独立的RL算法相比,表现出更高的性能.
- 在目标搜索效率和避障能力方面都观察到显著的改进.
- 软行动者-关键-人工潜力场 (SAC-APF) 模型在障碍场景中实现了161%的成功率增加 (0.282到0.736).
结论:
- 混合RL-APF框架有效地克服了无人机导航传统RL方法的局限性.
- 这种方法为自主搜索目标和避开障碍提供了更有效和更强大的解决方案.
- 这些发现表明,在复杂的操作环境中推进无人机能力是一个有希望的方向.
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