在未知的复杂环境中进行全面覆盖路径规划的能源意识Q学习框架
Yao Xue1, Chee Keong Tan2,3, Wai Peng Wong2
1School of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, 47500, Malaysia. yao.xue@monash.edu.
Scientific reports
|December 30, 2025
概括
我们开发了PERM-QN,这是机器人智能路径规划算法. 这种方法通过确保完整的区域覆盖和能源意识,提高了在不可预测的灾区的搜索和救援效率.
科学领域:
- 机器人和智能系统 机器人和智能系统
- 人工智能的人工智能
- 搜索和救援行动 搜索和救援行动
背景情况:
- 传统的覆盖路径规划 (CPP) 算法在动态,不可预测的灾后环境中面临局限性.
- 机器人路径规划需要适应能力,以便在不确定的地形中进行高效的搜救行动.
研究的目的:
- 引入 PERM-QN (具有优先经验重复和记忆网络的Q学习),一个智能路径规划算法.
- 为了使机器人能够在复杂的,充满障碍的灾后环境中实现能源意识的,完整的区域覆盖.
主要方法:
- PERM-QN集成了动态重量奖励功能,用于适应性平衡覆盖范围,路径长度和能耗.
- 优先经验重播机制通过优先考虑高价值的过去经验来加速学习的融合.
- 记忆网络可以在类似的地形区域加快路线规划,减少冗余的探索.
主要成果:
- 与模拟灾后环境中的传统方法相比,PERM-QN证明了更有效和更全面的区域勘探.
- 该算法在不同的环境复杂性中保持了强大的性能.
- 在不确定的地形上,PERM-QN实现了有效的能源意识覆盖.
结论:
- 在复杂,动态的灾后场景中,PERM-QN为机器人路径规划提供了有效的解决方案.
- 智能算法增强了机器人在搜救任务中的能力.
- 这种方法提高了勘探效率和完整性,同时管理了能源消耗.
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