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基于接觸狀態(tài)感知的羊胴體后腿自適應(yīng)分割控制方法
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2018YFD0700804)


Adaptive Segmentation Control Method of Sheep Carcass Hind Legs Based on Contact State Perception
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    摘要:

    針對(duì)羊胴體后腿骨肉邊界未知,、尺寸多變和可見性約束限制造成的機(jī)器人自主分割精確度低與易受阻卡住的問題,,提出一種羊胴體后腿自適應(yīng)分割控制方法,并開展羊胴體后腿分割試驗(yàn)進(jìn)行驗(yàn)證,。該方法以接觸狀態(tài)感知為核心,,有效提取接觸類型特征、接觸異常度特征和接觸方向特征,,通過構(gòu)建深度時(shí)空神經(jīng)網(wǎng)絡(luò)識(shí)別接觸類型,,構(gòu)建深度自編碼網(wǎng)絡(luò)估計(jì)接觸異常度,采用主成分分析方法檢測(cè)主要接觸方向,,實(shí)現(xiàn)接觸狀態(tài)多模態(tài)感知,,機(jī)器人通過動(dòng)態(tài)運(yùn)動(dòng)基元模仿學(xué)習(xí)人類操作技能,并結(jié)合接觸狀態(tài)感知信息實(shí)現(xiàn)關(guān)節(jié)運(yùn)動(dòng)的自適應(yīng)調(diào)節(jié),。試驗(yàn)結(jié)果表明:深度時(shí)空網(wǎng)絡(luò)模型在羊胴體后腿分割驗(yàn)證集上的識(shí)別準(zhǔn)確率為98.44%,;深度自編碼網(wǎng)絡(luò)模型能夠較好地估計(jì)驗(yàn)證集樣本的接觸異常度,區(qū)分不同的接觸狀態(tài),。機(jī)器人基于自適應(yīng)分割控制方法開展實(shí)際分割試驗(yàn),,與對(duì)照組相比,最大分割力下降幅度為29N,,最大力矩下降幅度為7N·m,,證明該方法的有效性;平均最大殘留肉厚度為3.6mm,,平均分割殘留率為4.9%,,分割殘留率與羊胴體質(zhì)量呈現(xiàn)負(fù)相關(guān),證明該方法具有良好的泛化性和準(zhǔn)確性,,并且整體分割效果較好,,滿足羊胴體后腿分割要求。

    Abstract:

    Due to unknown flesh and bone boundaries in the hind legs of sheep carcasses, variable size and visibility constraints, the robot autonomous segmentation accuracy is low and easy to be blocked. An adaptive segmentation control method was proposed for the hind legs of sheep carcasses, and the segmentation test of sheep carcass hind legs was carried out to verify it. The method was centred on contact state perception and effectively extracted contact type features, contact abnormality features and contact direction features. LSTM-FCN deep spatio-temporal neural network was constructed to identify contact types, constructing deep self-coding network to estimate contact anomalies, and using principal component analysis to detect the main contact directions to achieve multimodal sensing of contact states. The robot imitated and learned human manipulation skills through dynamic motion primitives, and incorporated contact state sensing information to achieve adaptive adjustment of joint motion. The experimental results showed that the recognition accuracy of LSTM-FCN model on the validation set of sheep carcass hind leg segmentation was 98.44%, with a high recognition accuracy. The DAE model can better estimate the contact anomalies of the validation set samples and distinguish different contact states. Robot conducted practical segmentation tests based on adaptive segmentation control method. Compared with the control group, the maximum segmentation force was decreased by 29N and the maximum torque was decreased by 7N·m, proving the effectiveness of the method. The average maximum residual meat thickness was 3.6mm, the average segmentation residual rate was 4.9%, and the segmentation residual rate showed a negative correlation with the quality of sheep carcasses. It proved that the method had good generalization and accuracy. And the overall segmentation effect was good, meeting the requirements of sheep carcass hind leg segmentation.

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謝斌,矯偉鵬,劉楷東,吳競(jìng),溫昌凱,陳仲舉.基于接觸狀態(tài)感知的羊胴體后腿自適應(yīng)分割控制方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2023,54(9):306-315. XIE Bin, JIAO Weipeng, LIU Kaidong, WU Jing, WEN Changkai, CHEN Zhongju. Adaptive Segmentation Control Method of Sheep Carcass Hind Legs Based on Contact State Perception[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(9):306-315.

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  • 收稿日期:2023-03-09
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  • 在線發(fā)布日期: 2023-09-10
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