Task Assignment for Multivehicle Systems Based on Collaborative Neurodynamic Optimization

被引：34

作者：

Wang, Jiasen ^{[1
,2
]}

Wang, Jun ^{[1
,2
]}

Che, Hangjun ^{[1
,2
]}

机构：

[1] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China

[2] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2020年 / 31卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Task analysis; Optimization; Neurodynamics; Collaboration; Linear programming; Neural networks; Lagrangian functions; Multivehicle systems; neurodynamic optimization; task assignment (TA); PARTICLE SWARM OPTIMIZATION; RECURRENT NEURAL-NETWORK; ALLOCATION; ALGORITHM; ROBOTS;

D O I：

10.1109/TNNLS.2019.2918984

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper addresses task assignment (TA) for multivehicle systems. Multivehicle TA problems are formulated as a combinatorial optimization problem and further as a global optimization problem. To fulfill heterogeneous tasks, cooperation among heterogeneous vehicles is incorporated in the problem formulations. A collaborative neurodynamic optimization approach is developed for solving the TA problems. Experimental results on four types of TA problems are discussed to substantiate the efficacy of the approach.

引用

页码：1145 / 1154

页数：10

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