[1]CHENG Lei,ZHOU Mingda,WU Huaiyu,et al.Cooperative multi-robot localization based on particle swarm optimization in the environment of wireless sensor[J].CAAI Transactions on Intelligent Systems,2015,10(1):138-142.[doi:10.3969/j.issn.1673-4785.201310067]
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Cooperative multi-robot localization based on particle swarm optimization in the environment of wireless sensor

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 1 Page number: 138-142 Column: 学术论文—智能系统 Public date: 2015-03-25
Title:
Cooperative multi-robot localization based on particle swarm optimization in the environment of wireless sensor
Author(s):
CHENG Lei1; 2;  ZHOU Mingda1;  WU Huaiyu1;  LI Jie1;  WANG Yongji3
1. Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China;
2. College of Engineering, Peking University, Beijing 100871, China;
3. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Keywords:
particle swarm optimization; multi-robot; cooperative localization; wireless sensor network; resampling; inertia weight; multiple information fusion; fitness function
CLC:
TP31
DOI:
10.3969/j.issn.1673-4785.201310067
Abstract:
Cooperative localization is an important technique of multi-robot’s autonomous behavior. In this paper, the multi-robot cooperative localization algorithm based on the optimization of particle swarm optimization under wireless sensor network environment is described. The resampling algorithm is introduced to solve the problem of particle depletion, enlarge the scope of solution space and guarantee the diversity of population. The introduction of inertia weight provides a solution for the particle degradation. Simulation results showed that by using the particle swarm optimization algorithm, which is supported by wireless sensor network to assist navigation and integrating robots’ observation information, the spatial dimensions of the problem can be reduced. In addition, the accuracy of robot localization can be improved effectively under the background of Gaussian noise.
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