Xudong Wu, Xiangang Cao, Wentao Ding, Peng Wang, Ye Zhang, Jiahui Liu
School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi Province, China.
Sci Rep. 2025 Aug 11;15(1):29294. doi: 10.1038/s41598-025-14314-3.
Robot technology has important application value in coal mines, non-coal mines, and other fields. However, the existing coal gangue multi-task allocation methods cannot be used for the coal foreign object with multi kinds, complex features, and spatiotemporal randomness, and it is difficult to solve comprehensively improve coal quality, human-machine safety and environmental pollution. Firstly, this paper constructs a multi-task allocation model of coal foreign object sorting robot (CFoSR) based on optimal capacity and benefit. The matching function, optimal capacity function and benefit function were used to calculate the environmental state matrix, and the state transfer function was designed to update the environmental state, which described the multi-task allocation problem of coal foreign object sorting. Then, a multi-evaluation index fitness function was designed that comprehensively considered the total mass of gangue sorting and the total number of sundry sorting. According to the scheduling rules such as first-in-first-out (FIFO), benefit first (BF) and shortest process time (SPT), a combined rule strategy based on genetic algorithm (GACRS) is proposed. Finally, the CFoSR simulation environment was built. The action mechanism of scheduling rules, heterogeneous manipulators, belt speed and coal flow upper limit on the multi-task allocation results of CFoSR is deeply analyzed. The simulation results show that the optimal fitness value of GACRS is 24.73% higher than that of adaptive weights based on greedy algorithm(GAW). The comparison results based on CFoSR experimental platform show that the optimal fitness value of GACRS is 14.02% higher than that of GAW. This paper provides a multi-task allocation model and its solution method for intelligent coal foreign object sorting.
机器人技术在煤矿、非煤矿山等领域具有重要的应用价值。然而，现有的煤矸石多任务分配方法无法用于具有多种类型、复杂特征和时空随机性的煤杂物，难以全面解决提高煤炭质量、人机安全和环境污染等问题。首先，本文构建了基于最优能力和效益的煤杂物分选机器人（CFoSR）多任务分配模型。利用匹配函数、最优能力函数和效益函数计算环境状态矩阵，并设计状态转移函数更新环境状态，描述了煤杂物分选的多任务分配问题。然后，设计了一种综合考虑矸石分选总质量和杂物分选总数的多评价指标适应度函数。根据先进先出（FIFO）、效益优先（BF）和最短加工时间（SPT）等调度规则，提出了一种基于遗传算法的组合规则策略（GACRS）。最后，搭建了CFoSR仿真环境。深入分析了调度规则、异构机械手、皮带速度和煤流上限对CFoSR多任务分配结果的作用机制。仿真结果表明，GACRS的最优适应度值比基于贪婪算法的自适应权重（GAW）高24.73%。基于CFoSR实验平台的对比结果表明，GACRS的最优适应度值比GAW高14.02%。本文为智能煤杂物分选提供了一种多任务分配模型及其求解方法。
