基于最佳容量和效益的煤炭异物分类机器人的多任务分配模型
Wu Xudong1, Cao Xiangang2, Ding Wentao1
1School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi Province, China.
Scientific reports
|August 11, 2025
概括
这项研究引入了一种新的机器人对煤炭异物进行分类系统,提高了煤炭的质量和安全性. 与现有方法相比,拟议的遗传算法策略显著提高了分类效率.
科学领域:
- 机器人和自动化 机器人和自动化
- 材料科学 材料科学 材料科学
- 环境工程 环境工程
背景情况:
- 现有的煤分类方法与复杂的随机异物作斗争.
- 目前的方法无法同时优化煤炭质量,人机安全和环境影响.
研究的目的:
- 为煤炭异物物品分类机器人 (CFoSR) 开发一个智能多任务分配模型.
- 通过优化分类来提高煤炭质量,提高人机安全,减少环境污染.
主要方法:
- 为CFoSR建立了一个基于最佳容量和效益的多任务分配模型.
- 设计了一种多重评估指数健身功能,考虑质量和各种计数.
- 基于遗传算法 (GACRS) 的综合规则策略被提出,结合了FIFO,BF和SPT规则.
主要成果:
- 在模拟中,GACRS实现了比贪算法高24.73%的最佳健身值.
- 在CFoSR平台上的实验结果显示,GACRS在最佳健身方面表现优于贪算法14.02%.
- 分析揭示了调度规则,操纵器,皮带速度和煤炭流量对分类性能的影响.
结论:
- 拟议的GACRS为智能煤炭异物分类提供了有效的解决方案.
- 开发的模型和方法在排序效率和整体系统性能方面提供了显著的改进.
- 这项研究有助于推进采矿行业的自动化,以改善资源管理和安全.
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