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Study on Automatic Grasping Operation for Phalaenopsis Tissue Culture Plantlets
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In the manual transplanting of Phalaenopsis plantlets, the plantlet is usually grasped at the root or the stem since the leaf is fragile and is easily damaged by the gripper. To mimic the manual transplanting operation, this study developed an image-processing algorithm to segment and classify the components of Phalaenopsis tissue culture plantlets (PTCPs) and to determine a suitable grasping location to carry out an automatic grasping process.
In the proposed approach, the nodes of the plantlet's skeleton are automatically located and used to generate cutting lines with which to separate the plantlet into its constituent leaves and roots. The sequential forward floating selection (SFFS) algorithm was employed to establish the optimal combination of color and shape features. A Bayes classifier based on the optimal combination was then applied to classify the individual components of the plantlet as either leaf or root segments. The root segment with the highest Bayes decision value was selected, and the midpoint of its skeleton was then specified as a suitable grasping point for the automatic grasping operation.
Implementing the optimal set of features, the proposed classifier achieved a successful rate of 94.9% in identifying suitable grasping points on complete PTCP plantlets. An automatic grasping mechanism was constructed in which the grasping point selection scheme was integrated with a binocular stereo vision system and a robotic gripper. It is shown that the automatic grasping system has a success rate of 78.2% in grasping the plantlets in an appropriate position.
本文利用苗影像的骨架分叉點來產生分割線，將苗體分割成一段一段的莖葉元件，然後使用SFFS (Sequential Forward Floating Selection) 特徵選擇演算法就擷取的顏色與形狀特徵參數中，選出一組辨識率最佳的特徵組合。再根據最佳的特徵組合建立一個Bayes 分類器，將被分割的元件區分為葉或根兩種類別。然後在被分類為根的元件選定具有最高之根類Bayes決策值者作為夾持目標，並取該元件之骨架中點作為自動夾持作業選定的夾持點。
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