基于深度神经网络的多指灵巧手抓取手势优化  

Grasping Gesture Optimization of Multi-fingered Dexterous Hands Based on Deep Neural Networks

作　　者：何浩源 尚伟伟[1] 张飞 丛爽[1] HE Haoyuan;SHANG Weiwei;ZHANG Fei;CONG Shuang(Department of Automation,University of Science and Technology of China,Hefei 230027,China)

机构地区：[1]中国科学技术大学自动化系,安徽合肥230027

出　　处：《机器人》2023年第1期38-47,共10页Robot

基　　金：国家自然科学基金(62173316,51675501);安徽省杰出青年科学基金(2108085J32)。

摘　　要：基于深度神经网络模型,提出了一种适用于多指灵巧手的抓取手势优化方法。首先,在仿真环境下构建了一个抓取数据集,并在此基础上训练了一个卷积神经网络,依据目标物体单目视觉信息和多指灵巧手抓取位形来预测抓取质量函数,由此可以将多指灵巧手的抓取规划问题转化为使抓取质量最大化的优化问题,进一步,基于深度神经网络中的反向传播和梯度上升算法实现多指灵巧手抓取手势的迭代与优化。在仿真环境中,比较该网络和仿真平台对同一抓取位形的抓取质量评估结果,再利用所提出的优化方法对随机搜索到的初始手势进行优化,比较优化前后手势的力封闭指标。最后,在实际机器人平台上验证本文方法的优化效果,结果表明,本文方法对未知物体的抓取成功率在80%以上,对于失败的抓取,优化后成功的比例达到90%。Based on deep neural network model, a grasping gesture optimization method is proposed for multi-fingered dexterous hands. Firstly, a grasp dataset is constructed in simulation environment, and then a convolutional neural network is trained on this basis to predict the grasp quality function from the monocular visual information of the target object and the grasp configuration of the multi-fingered dexterous hand. Therefore, the grasp planning problem of the multi-fingered dexterous hands is transformed into an optimization problem about maximizing the grasping quality. Further the backpropagation and gradient ascent algorithm in deep learning is used to iterate and optimize the grasping gestures of the multi-fingered dexterous hands. In simulation, the evaluation results of the grasping quality, separately computed by the proposed network and the simulation platform for the same grasp configuration, are compared. Then the proposed method is implemented to optimize the initial gestures searched randomly, and the force closure metrics of the gestures before and after optimization are compared. Finally, the optimization performance of the proposed method is validated on the actual robot platform. The results show that the grasping success rate of the proposed method for the unknown objects is more than 80%, and for the failed grasps, the success rate after optimization reaches 90%.

关 键 词：多指灵巧手 卷积神经网络 抓取规划 未知物体 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]