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集成 基于现场的宽度首次搜索流场路径查找
Jiongkun Yang1, Xiai Chen2, Mingze Dong1
1College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang, China.
Scientific reports
|November 18, 2025
概括
本研究介绍了一种基于整合现场的宽度首次搜索流场路径. 这种新的方法提高了路线规划效率和适应性,在动态环境中优于传统方法.
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
- 计算机科学 计算机科学
背景情况:
- 流场路径查找对于多代理导航至关重要.
- 像CFD这样的传统方法在计算上昂贵.
- 离散的流场往往会产生低于最佳的,曲的路径.
研究的目的:
- 开发一种更有效,更准确的流场路径查找方法.
- 为了提高路径长度和计算速度.
- 增强在动态环境中的路径规划适应能力.
主要方法:
- 提出了一个基于集成现场的宽度优先搜索算法.
- 利用波面并行实现更快的计算和更短的路径.
- 集成流域与深度强化学习,以提高适应能力.
主要成果:
- 与传统的离散流场相比,拟议的方法显著减少了路径长度.
- 通过波面并行实现更快的计算时间.
- 在未知的室内环境中,基于流场的深度强化学习框架的表现卓越.
结论:
- 基于集成现场的BFS提供了一种高效和有效的解决方案,用于流场路径.
- 深度强化学习的整合提高了在动态环境中的导航适应性.
- 该方法的实际适用性在现实环境中得到验证.
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