Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (07): 78-87.doi: 10.13475/j.fzxb.20190800210

• Textile Engineering • Previous Articles     Next Articles

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. 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

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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

CLC Number: 

  • TP24

Fig.1

Process flow chart of combing workshop"

Fig.2

Abstract description and simplified schematic of textile workshop production task. (a)Combing workshop; (b) Machine distribution in combing workshop; (c) Simplified task abstraction diagram of workshop"

Fig.3

Algorithm flow chart"

Fig.4

Iterative completion process of task allocation. (a) Iteration 79th; (b) Iteration 139th;(c) Iteration 189th; (d) Iteration 331th;(e) Iteration 597th; (f) Iteration 682th;(g) Iteration 771th; (h) Iteration 804th"

Fig.5

Results of the same quantity of agents in different task quantities"

Tab.1

Iterative performance results of algorithms under different order of magnitude m and n"

m 迭代次数
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

Fig.6

Task allocation income of three different methods"

Fig.7

System benefits in dynamic task change"

Fig.8

Task allocation results under different communication network strength. (a) Strong communication;(b) Weak communication"

Tab.2

Experimental results of strong communication network connection"

智能体数 100次完成任务平均时间/s
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

Tab.3

Test results of weak communication network connection with 30% communication failure"

智能体数 100次完成任务平均时间/s
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

Fig.9

Task allocation experiments under different conditions. (a) Allocation of efficiency in different situations;(b) Isometric map for allocation efficiency"

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