基于接触状态感知的羊胴体后腿自适应分割控制方法

针对羊胴体后腿骨肉边界未知、尺寸多变和可见性约束限制造成的机器人自主分割精确度低与易受阻卡住的问题,提出一种羊胴体后腿自适应分割控制方法,并开展羊胴体后腿分割试验进行验证。该方法以接触状态感知为核心,有效提取接触类型特征、接触异常度特征和接触方向特征,通过构建深度时空神经网络识别接触类型,构建深度自编码网络估计接触异常度,采用主成分分析方法检测主要接触方向,实现接触状态多模态感知,机器人通过动态运动基元模仿学习人类操作技能,并结合接触状态感知信息实现关节运动的自适应调节。试验结果表明：深度时空网络模型在羊胴体后腿分割验证集上的识别准确率为98.44%;深度自编码网络模型能够较好地估计验证集样本的接触异常度,区分不同的接触状态。机器人基于自适应分割控制方法开展实际分割试验,与对照组相比,最大分割力下降幅度为29 N,最大力矩下降幅度为7 N·m,证明该方法的有效性;平均最大残留肉厚度为3.6 mm,平均分割残留率为4.9%,分割残留率与羊胴体质量呈现负相关,证明该方法具有良好的泛化性和准确性,并且整体分割效果较好,满足羊胴体后腿分割要求。

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

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.