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基于自適應(yīng)導(dǎo)航參數(shù)的智能車輛視覺導(dǎo)航
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國家自然科學(xué)基金資助項目(51075112)和安徽科技學(xué)院人才引進(穩(wěn)定)資助項目(ZRC2011302)


Navigation of Vision-guided Intelligent Vehicle Based on Adaptive Navigation Parameters
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    摘要:

    為提高導(dǎo)航路徑識別的魯棒性和實時性,采用了分區(qū)閾值二值化,、噪聲點搜索及濾波等圖像處理方法,,并對導(dǎo)航路徑進行分區(qū)逐段識別;在路徑跟蹤方面,,在獲取的導(dǎo)航路徑圖像中選取遠(yuǎn)端路徑和近端路徑,,以遠(yuǎn)端路徑和近端路徑的方位偏差量作為確定目標(biāo)路徑的依據(jù),使提取的導(dǎo)航參數(shù)能適應(yīng)導(dǎo)航路徑的變化,。根據(jù)四輪智能車輛模型進行路徑跟蹤仿真計算,。在此基礎(chǔ)上,采用兩塊數(shù)字信號處理器,,對基于路徑導(dǎo)航的視覺智能車輛進行了設(shè)計和試驗驗證,。試驗結(jié)果表明采用該方法設(shè)計的智能車輛具有較好的路徑識別和跟蹤控制效果。

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    In order to obtain better robustness and real-time processing of path recognition, the binary segmentation image, noise searching and filtering procedures were employed. The whole path was classified into far part and near part, and the difference between the two parts was used as a threshold for selecting target road. So the navigating parameters were adapted to the changing path. Based on a model of four-wheel intelligent vehicle, a path tracking simulation was performed. Then, the navigation system based on path tracking was designed by using two digital signal processors. The experiments showed the accuracy and robustness of the system.

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李進,陳無畏.基于自適應(yīng)導(dǎo)航參數(shù)的智能車輛視覺導(dǎo)航[J].農(nóng)業(yè)機械學(xué)報,2012,43(6):19-24,152. Li Jin, Chen Wuwei. Navigation of Vision-guided Intelligent Vehicle Based on Adaptive Navigation Parameters[J]. Transactions of the Chinese Society for Agricultural Machinery,2012,43(6):19-24,152.

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  • 在線發(fā)布日期: 2012-06-19
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