多智能体博弈的纺织车间搬运机器人任务分配

doi:10.13475/j.fzxb.20190800210

纺织学报 ›› 2020, Vol. 41 ›› Issue (07): 78-87.doi: 10.13475/j.fzxb.20190800210

多智能体博弈的纺织车间搬运机器人任务分配

李珣¹(), 南恺恺¹, 赵征凡², 王晓华¹, 景军锋¹

1.西安工程大学电子信息学院, 陕西西安 710048
2.工业和信息化部电子第五研究所, 广东广州 510610

收稿日期:2019-08-02 修回日期:2020-01-21 出版日期:2020-07-15 发布日期:2020-07-23
作者简介:李珣(1981—),男,副教授,博士。主要研究方向为基于深度学习的车辆目标检测与跟踪以及多运动机器人协同控制技术。E-mail: leonlee527@163.com。
基金资助:
国家自然科学基金资助项目(51607133);陕西省自然科学基础研究计划项目(2019JM567);中国纺织工业联合会科技指导性项目(2018094);大学生创新创业训练项目(201910709019)

Task allocation of handling robot in textile workshop based on multi-agent game theory

LI Xun¹(), NAN Kaikai¹, ZHAO Zhengfan², WANG Xiaohua¹, JING Junfeng¹

1. School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
2. The Fifth Electronics Research Institute, Ministry of Industry and Information Technology, Guangzhou, Guangdong 510610, China

Received:2019-08-02 Revised:2020-01-21 Online:2020-07-15 Published:2020-07-23

摘要/Abstract

摘要：

针对当前纺织品智能化生产加工过程中搬运机器人所面临的大规模复杂动态任务分配问题,提出了一种基于智能体博弈理论的分布式自主决策框架。根据纺织品实际生产过程任务环境建立任务模型,在考虑到任务的距离和时间优先级等因素下建立目标函数,以智能体的目标效用函数最优为策略选择依据,引入博弈论中的Nash均衡理论对问题求解,并对该决策框架进行了实验验证。实验结果表明:在该决策框架下任务的分配相比同类分布式任务分配算法能够得到全局最优解,具有较强的可扩展性、良好的鲁棒性、收敛性能,同时对动态任务分配同样具有良好结果表现。

关键词: 多智能体, 搬运机器人, 任务分配, 纺织车间, 博弈论

Abstract:

A distributed autonomous decision-making framework was proposed based on multi-agent game theory. The framework is used to solve the problems of handling robot in the process of intelligent textile production and processing, which are large-scale and complex dynamic task allocation problems. To start with, the task model was established according to the actual task environment of textile production. Taking into account of the task distance and time priority, the target utility function of the agent was then used as the policy selection basis, and the equilibrium theory of the game was introduced to solve the problem. Eventually, the decision framework was verified by experiments. The experimental results show that the global optimal solution of the task allocation in this decision framework can be better achieved in comparison to the similar distributed task allocation algorithms. In summary, the proposed algorithm has high scalability, good robustness, and convergence performance. Furthermore, the proposed algorithm has excellent performance for dynamic task allocation.

Key words: multi-agent, handling robot, task allocation, textile workshop, game theory

中图分类号:

TP24

李珣, 南恺恺, 赵征凡, 王晓华, 景军锋. 多智能体博弈的纺织车间搬运机器人任务分配[J]. 纺织学报, 2020, 41(07): 78-87.

LI Xun, NAN Kaikai, ZHAO Zhengfan, WANG Xiaohua, JING Junfeng. Task allocation of handling robot in textile workshop based on multi-agent game theory[J]. Journal of Textile Research, 2020, 41(07): 78-87.

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m	迭代次数
m	n=4	n=5	n=6	n=7	n=8
10	14	13	14	12	14
15	23	26	25	23	24
20	38	41	38	38	43
25	55	57	53	49	46
30	68	70	63	72	67
35	73	88	67	85	79
40	89	97	99	94	83
45	107	104	103	98	109
50	112	122	111	122	117
55	124	125	141	134	129
60	158	146	147	150	135
65	166	149	167	164	148
70	166	175	174	165	158
75	168	163	175	180	180
80	178	183	206	189	173
85	185	203	216	207	197
90	216	224	224	214	221
95	223	245	232	221	232
100	223	231	232	243	245
105	233	276	251	258	273
110	251	261	277	263	280
115	236	292	301	284	303
120	247	285	307	302	319
125	·	295	309	306	342
130	·	331	311	314	305
135	·	·	329	334	328

智能体数	100次完成任务平均时间/s
智能体数	PSO	市场法	博弈论
20 25 30 35 40	2.35 2.43 2.68 2.76 3.05	1.53 1.59 1.67 1.73 1.85	0.72 0.85 0.91 0.94 0.96

智能体数	100次完成任务平均时间/s
智能体数	PSO	市场法	博弈论
20 25 30 35 40	2.73 2.86 2.98 3.18 3.27	2.63 3.07 3.15 3.34 3.58	0.98 1.04 1.16 1.24 1.26

多智能体博弈的纺织车间搬运机器人任务分配

Task allocation of handling robot in textile workshop based on multi-agent game theory

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参考文献 17

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m	迭代次数
m	n=4	n=5	n=6	n=7	n=8
10	14	13	14	12	14
15	23	26	25	23	24
20	38	41	38	38	43
25	55	57	53	49	46
30	68	70	63	72	67
35	73	88	67	85	79
40	89	97	99	94	83
45	107	104	103	98	109
50	112	122	111	122	117
55	124	125	141	134	129
60	158	146	147	150	135
65	166	149	167	164	148
70	166	175	174	165	158
75	168	163	175	180	180
80	178	183	206	189	173
85	185	203	216	207	197
90	216	224	224	214	221
95	223	245	232	221	232
100	223	231	232	243	245
105	233	276	251	258	273
110	251	261	277	263	280
115	236	292	301	284	303
120	247	285	307	302	319
125	·	295	309	306	342
130	·	331	311	314	305
135	·	·	329	334	328

m	迭代次数
m	n=4	n=5	n=6	n=7	n=8
10	14	13	14	12	14
15	23	26	25	23	24
20	38	41	38	38	43
25	55	57	53	49	46
30	68	70	63	72	67
35	73	88	67	85	79
40	89	97	99	94	83
45	107	104	103	98	109
50	112	122	111	122	117
55	124	125	141	134	129
60	158	146	147	150	135
65	166	149	167	164	148
70	166	175	174	165	158
75	168	163	175	180	180
80	178	183	206	189	173
85	185	203	216	207	197
90	216	224	224	214	221
95	223	245	232	221	232
100	223	231	232	243	245
105	233	276	251	258	273
110	251	261	277	263	280
115	236	292	301	284	303
120	247	285	307	302	319
125	·	295	309	306	342
130	·	331	311	314	305
135	·	·	329	334	328

m	迭代次数
m	n=4	n=5	n=6	n=7	n=8
10	14	13	14	12	14
15	23	26	25	23	24
20	38	41	38	38	43
25	55	57	53	49	46
30	68	70	63	72	67
35	73	88	67	85	79
40	89	97	99	94	83
45	107	104	103	98	109
50	112	122	111	122	117
55	124	125	141	134	129
60	158	146	147	150	135
65	166	149	167	164	148
70	166	175	174	165	158
75	168	163	175	180	180
80	178	183	206	189	173
85	185	203	216	207	197
90	216	224	224	214	221
95	223	245	232	221	232
100	223	231	232	243	245
105	233	276	251	258	273
110	251	261	277	263	280
115	236	292	301	284	303
120	247	285	307	302	319
125	·	295	309	306	342
130	·	331	311	314	305
135	·	·	329	334	328