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数字双胞胎导航和剪刀机切割的运动规划方法
Bing Miao1, Shirong Ge1,2, Yunwang Li1,2,3
1School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究介绍了一种数字双胞胎系统,用于自主剪刀导航切割,增强煤矿采矿智能. 在这种先进的数字双胞胎环境中,DQN-NAF算法在运动规划方面表现出了卓越的优势.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 煤矿面临的挑战是,自主运营需要增强的智能.
- 剪刀导航切割对于高效和安全的采矿至关重要.
- 数字双胞胎技术为优化采矿过程提供了潜力.
研究的目的:
- 提出和开发一个剪刀数字双导航切割运动规划系统.
- 为了实现自主导出,学习和优化剪刀切割路径.
- 评估用于剪切运动规划的深度强化学习算法.
主要方法:
- 建立了一个基于数字双胞胎理论的剪刀数字双胞胎导航切割运动规划系统.
- 集成的物理感知,综合数据和数字模型融合分析层.
- 在数字双胞胎环境中利用深度强化学习算法 (DQN-NAF和DDPG).
主要成果:
- 开发的系统支持关键功能:剪刀切割数字双子,动态导航地图数字双子,强化学习环境构建和运动规划.
- 对比分析显示,DQN-NAF算法在剪切运动规划任务中表现优于DDPG.
- DQN-NAF算法证明了性能和稳定性的提高.
结论:
- 拟议的剪刀数字双胞胎系统有效地支持自主导航切割运动规划.
- 深度强化学习,特别是DQN-NAF,是优化剪刀机操作的可行和有效方法.
- 这项研究通过数字双胞胎和人工智能集成来推进智能煤矿开采.
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