摘要
针对出现机器人"绑架"现象时,由于在后验密度分布取值较大区域中的采样数较少,利用蒙特卡罗定位方法(MonteCarloLocalization)进行定位需要大量的采样才能取得较好效果的问题,提出了一种遗传蒙特卡罗定位方法(GeneticMonteCarloLocalization).GMCL将进化计算中的交叉与变异操作引入到MCL中,对采样进行优化,使采样朝后验密度分布取值较大的区域移动,从而更好地表达系统的后验密度分布.实验结果表明:GMCL可以显著减少所需的采样数,具有更高的精度和更好的鲁棒性.
A large sample size is needed for Monte Carlo localization (MCL) in multi -robot dynamic environment due to the frequent robot kidnap phenomenon making less samples locate in the regions where the value of desired posterior density function (PDF) is large. A modified localization method named genetic Monte Carlo localization (GMCL) was proposed. The crossover and mutation operations in evolutionary computation were introduced into MCL to make samples move towards regions with large value of PDF, so the sample set of GMCL can represent the desired PDF better. Experimental results show that GMCL needs fewer samples and is more precise and robust in the dynamic environment.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2003年第9期1047-1049,共3页
Journal of Harbin Institute of Technology
基金
国家高技术研究发展计划资助项目(863-2001AA422270).
