考虑含矸率时变性的多臂协同策略优化方法

曹现刚; 乔欢乐; 吴旭东; 王鹏; 范智海

doi:10.13433/j.cnki.1003-8728.20220159

考虑含矸率时变性的多臂协同策略优化方法

doi: 10.13433/j.cnki.1003-8728.20220159

曹现刚^{1, 2,},
乔欢乐^{1, 2,},
吴旭东^{1, 2},
王鹏^{1, 2},
范智海³

1.
西安科技大学机械工程学院，西安　710054
2.
陕西省矿山机电装备智能检测重点实验室，西安　710054
3.
陕西铜川矿业有限公司，陕西铜川　727000

基金项目: 国家自然科学基金面上项目（51975468）、陕西省重点研发计划项目（2018GY-160）及陕西省教育厅科学研究计划项目（18JC022）

详细信息

作者简介:
曹现刚（1970−），教授，博士生导师，博士，研究方向为设备健康维护与管理、机器人技术，172833610@qq.com

中图分类号: TP273
计量
- 文章访问数: 70
- HTML全文浏览量: 51
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-02
- 刊出日期: 2023-11-30

A Multi-manipulator Cooperative Strategy Optimization Method with Time-varying Gangue Rate Considered

CAO Xiangang^{1, 2
,},
QIAO Huanle^{1, 2
,},
WU Xudong^{1, 2},
WANG Peng^{1, 2},
FAN Zhihai³

1.
School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
2.
Shaanxi Provincial Key Laboratory of Intelligent Testing of Mine Mechanical and Electrical Equipment, Xi'an 710054, China
3.
Shaanxi Tongchuan Mining Co., Ltd., Tongchuan 727000, Shaanxi, China

摘要

摘要: 针对煤炭生产过程中，原煤含矸率时变性引起分拣效率低的问题，本文综合考虑矸石质量、矸石识别置信度、机械臂分拣时间等因素，通过一种多因素融合效益矩阵的方法，优化时变含矸率条件下的多机械臂煤矸分拣策略。提出了一种基于效益矩阵的多机械臂协同策略模型,以分拣效益和拣矸率共同作为策略的评价标准。通过采用相似性原理，构建小样本矸石流模型；利用熵权法量化效益矩阵的元素参量；最后，采用FCFS策略方法及本文策略方法进行多组对比实验，实验结果表明，通过效益矩阵优化的多臂协同策略能够有效解决过矸量大时的矸石任务分配问题，相对FCFS策略方法，提高拣矸率的同时，有效提高了选煤质量。
- 煤矸分选 /
- 多机械臂协同 /
- 多任务分配 /
- 效益矩阵
Abstract: In the process of coal production, the time varying of raw coal gangue rate causes low sorting efficiency. With the factors such as gangue quality, gangue recognition and manipulator sorting time considered, this paper uses the multi-factor fusion benefit matrix to optimize the multi-manipulator gangue sorting strategy under the condition of time-varying gangue rate. A multi-manipulator cooperative strategy model based on the benefit matrix was proposed. The sorting efficiency and the sorting gangue rate were used as evaluation criteria. A small sample gangue flow model is constructed by using the similarity principle. The element parameters of the benefit matrix are quantified with the entropy weight method. The experimental results show that the multi-manipulator cooperative strategy optimization method can effectively solve the gangue task allocation problem when the number of gangues is large. The gangue sorting rate and the sorted coal quality are effectively improved.
- coal gangue sorting /
- multi-manipulator coordination /
- multitask allocation /
- benefit matrix

HTML全文

图 1 多机械臂协同煤矸分拣机器人系统架构

Figure 1. System architecture of multi-manipulator cooperative coal-gangue sorting robot

下载: 全尺寸图片幻灯片

图 2 煤矸分拣过程描述

Figure 2. Description of coal-gangue sorting processes

下载: 全尺寸图片幻灯片

图 3 煤矸分拣机器人平台

Figure 3. Coal-gangue sorting robot platform

下载: 全尺寸图片幻灯片

图 4 多机械臂协同煤矸分拣策略流程图

Figure 4. Flow chart of multi-manipulator cooperative coal-gangue sorting strategy

下载: 全尺寸图片幻灯片

图 5 不同含矸率煤矸流任务分配结果

Figure 5. Task allocation results of coal gangue flow with different gangue contents

下载: 全尺寸图片幻灯片

图 6 基于效益矩阵的分配策略

Figure 6. Allocation strategy based on benefit matrix

下载: 全尺寸图片幻灯片

图 7 基于FCFS的分配策略

Figure 7. Allocation strategy based on FCFS

下载: 全尺寸图片幻灯片

表 1 随机矸石流的求解结果

Table 1. The result of solving random gangue flow

含矸率/ %	组	总个数	分拣个数	漏拣个数	拣矸率/%	分拣质量/ kg
10	1	106	96	10	90.6	1028.28
	1	106	101	5	95.2	1052.20
	2	117	96	21	82.0	859.15
	2	117	101	14	86.0	942.66
	3	74	62	12	83.7	670.66
	3	74	68	6	91.9	689.59
	4	91	79	12	86.8	716.55
	4	91	80	11	87.9	744.02
	5	110	90	20	81.8	875.27
	5	110	90	20	81.8	965.80
20	1	194	126	68	64.9	1342.34
	1	194	143	51	73.7	1646.10
	2	200	140	60	70.0	1523.70
	2	200	153	45	76.5	1909.60
	3	195	147	48	75.4	1587.79
	3	195	159	36	81.5	1886.10
	4	163	105	58	64.4	1041.56
	4	163	113	50	69.3	1317.50
	5	268	174	94	64.9	1679.06
	5	268	195	73	72.8	2342.00
30	1	352	160	192	45.5	1546.92
	1	352	170	180	48.0	2158.60
	2	233	117	116	50.0	1285.00
	2	233	138	112	59.2	2035.20
	3	231	116	115	50.2	1134.12
	3	231	130	101	56.3	1710.70
	4	251	114	137	45.0	1118.19
	4	251	123	128	49.0	1668.20
	5	242	127	115	52.5	1254.65
	5	242	135	107	55.8	1735.70
40	1	372	157	215	42.2	1784.47
	1	372	162	210	43.5	3107.90
	2	327	134	193	41.0	1484.83
	2	327	148	177	45.3	2158.90
	3	351	147	204	41.9	1731.05
	3	351	163	188	46.4	2837.80
	4	374	159	243	42.0	1856.00
	4	374	172	202	45.9	3195.90
	5	393	150	243	38.0	1551.52
	5	393	159	234	40.5	2740.30

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