| GMCL： a robust global localization method for mobile robot |
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| 引用本文： | 罗荣华 Hong Bingrong Min Huaqing. GMCL： a robust global localization method for mobile robot[J]. 高技术通讯(英文版), 2006, 12(4): 363-366 |
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| 作者姓名： | 罗荣华 Hong Bingrong Min Huaqing |
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| 作者单位： | [1]School of Computer Science and Engineering, South China University of Technology, Guangzhou 5106dO, P.R. China [2]School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, P.R. China |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | A large sample size is required for Monte Carlo localization （MCL） in multi-robot dynamic environ- ment, because of the ＂kidnapped robot＂ phenomenon, which will locate most of the samples in the regions with small value of desired posterior density. For this problem the crossover and mutation operators in evolutionary computation are introduced into MCL to make samples move towards the regions where the desired posterior density is large, so that the sample set can represent the density better. The proposed method is termed genetic Monte Carlo localization （GMCL）. Application in robot soccer system shows that GMCL can considerably reduce the required number of samples, and is more precise and robust in dynamic environment.
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| 关 键 词： | GMCL 移动机器人 机器人足球赛 全局定位 鲁棒性 进化计算 |
| 收稿时间： | 2003-09-08 |
GMCL: a robust global localization method for mobile robot |
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| Luo Ronghua,Hong Bingrong,Min Huaqing. GMCL: a robust global localization method for mobile robot[J]. High Technology Letters, 2006, 12(4): 363-366 |
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| Authors: | Luo Ronghua Hong Bingrong Min Huaqing |
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| Abstract: | A large sample size is required for Monte Carlo localization (MCL) in multi-robot dynamic environment, because of the "kidnapped robot" phenomenon, which will locate most of the samples in the regions with small value of desired posterior density. For this problem the crossover and mutation operators in evolutionary computation are introduced into MCL to make samples move towards the regions where the desired posterior density is large, so that the sample set can represent the density better. The proposed method is termed genetic Monte Carlo localization (GMCL). Application in robot soccer system shows that GMCL can considerably reduce the required number of samples, and is more precise and robust in dynamic environment. |
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| Keywords: | global localization Monte Carlo localization evolutionary computation robot soccer |
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