基于多策略混合人工魚群算法的移動(dòng)機(jī)器人路徑規(guī)劃
發(fā)布時(shí)間:2018-07-21 16:18
【摘要】:針對移動(dòng)機(jī)器人的路徑規(guī)劃問題,提出了一種基于多策略混合人工魚群算法的路徑規(guī)劃方法(MH-AFSA).為了提高傳統(tǒng)人工魚群算法(AFSA)的收斂速度和全局搜索能力,引入多策略混合機(jī)制,利用加權(quán)平均距離策略,擴(kuò)大了人工魚的視野范圍.采用對數(shù)函數(shù)作為步長的移動(dòng)因子,克服了傳統(tǒng)固定步長的缺陷.進(jìn)一步利用高斯變異策略擴(kuò)大了種群的多樣性.通過經(jīng)典函數(shù)優(yōu)化和旅行商問題(TSP)測試了算法的性能.最后,建立移動(dòng)機(jī)器人的環(huán)境模型,給出了基于多策略混合人工魚群算法的移動(dòng)機(jī)器人路徑規(guī)劃步驟.通過數(shù)值仿真說明了所提算法的優(yōu)越性和有效性.
[Abstract]:A path planning method (MH-AFSA) based on multi-strategy hybrid artificial fish swarm algorithm (MH-AFSA) is proposed for path planning of mobile robots. In order to improve the convergence speed and global search ability of the traditional artificial fish swarm algorithm (AFSA), a multi-strategy hybrid mechanism was introduced, and the weighted average distance strategy was used to enlarge the field of vision of artificial fish. The logarithmic function is used as the moving factor of step size, which overcomes the defect of traditional fixed step size. Further use of Gao Si mutation strategy to expand the diversity of the population. The performance of the algorithm is tested by classical function optimization and traveling salesman problem (tsp). Finally, the environment model of mobile robot is established, and the path planning steps of mobile robot based on multi-strategy hybrid artificial fish swarm algorithm are presented. Numerical simulation shows the superiority and effectiveness of the proposed algorithm.
【作者單位】: 安徽工程大學(xué)電氣工程學(xué)院;安徽省檢測技術(shù)與節(jié)能裝置省級重點(diǎn)實(shí)驗(yàn)室;
【基金】:國家自然科學(xué)基金資助項(xiàng)目(61304127) 安徽省自然科學(xué)基金資助項(xiàng)目(1408085QF132) 安徽工程大學(xué)中青年拔尖人才資助項(xiàng)目(2016BJRC004)
【分類號】:TP18;TP242
[Abstract]:A path planning method (MH-AFSA) based on multi-strategy hybrid artificial fish swarm algorithm (MH-AFSA) is proposed for path planning of mobile robots. In order to improve the convergence speed and global search ability of the traditional artificial fish swarm algorithm (AFSA), a multi-strategy hybrid mechanism was introduced, and the weighted average distance strategy was used to enlarge the field of vision of artificial fish. The logarithmic function is used as the moving factor of step size, which overcomes the defect of traditional fixed step size. Further use of Gao Si mutation strategy to expand the diversity of the population. The performance of the algorithm is tested by classical function optimization and traveling salesman problem (tsp). Finally, the environment model of mobile robot is established, and the path planning steps of mobile robot based on multi-strategy hybrid artificial fish swarm algorithm are presented. Numerical simulation shows the superiority and effectiveness of the proposed algorithm.
【作者單位】: 安徽工程大學(xué)電氣工程學(xué)院;安徽省檢測技術(shù)與節(jié)能裝置省級重點(diǎn)實(shí)驗(yàn)室;
【基金】:國家自然科學(xué)基金資助項(xiàng)目(61304127) 安徽省自然科學(xué)基金資助項(xiàng)目(1408085QF132) 安徽工程大學(xué)中青年拔尖人才資助項(xiàng)目(2016BJRC004)
【分類號】:TP18;TP242
【參考文獻(xiàn)】
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