双半球胶囊机器人自监督姿态快速矫正方法

Posture rapid correction for a double hemisphere capsule robot through self-supervised learning

为了解决胃肠道粘弹性阻尼和胶囊质心偏移等非线性因素会使胶囊机器人姿态偏离控制目标,胶囊运动过程中大视差和运动图像模糊所导致的姿态估计误差等问题,提出一种自监督姿态快速矫正方法。在胶囊初始姿态标定方面,消除了初始自转角的影响;在胶囊姿态估计方面,基于注意力机制SAB和TAM,使用深度可分离卷积替代部分标准卷积,设计出一种时空注意力机制TSAM并嵌入到PoseNet中得到注意力姿态估计网络APEN,加强模型特征提取能力。实验表明,相较于现有胶囊姿态估计方法Endo-SfM,APEN可在保持推理速度几乎不变的同时,相对姿态估计精度提高52%,并且该方法能实时、有效的对胶囊姿态进行矫正,姿态控制精度提高38.8%,为实现胃肠道高效动态诊疗奠定了基础。

To solve the problem that the posture of the double hemisphere capsule robot(DHCR)was likely to deviate from the targeted orientation due to nonlinear factors such as viscoelastic damping of the gastrointestinal(GI)tract and DHCR centroid deviation,as well as posture estimation error caused by large visual disparity and motion blur,a selfsupervised learning-based rapid posture correction method was proposed.In terms of calibration of the capsule′s initial posture,the influence of the initial rotation angle was eliminated;in terms of estimation of the capsule′s attitude,based on spatial attention block(SAB)and temporal attention module(TAM),a spatial-temporal attention mechanism(TSAM)was designed by replacing a component of the standard convolution with the depthwise separable convolution and embedding it into PoseNet to generate an attention posture estimation network(APEN),which enhanced the model′s ability to extract features.The experimental results show that APEN can increase relative posture estimate accuracy by 52%while keeping inference speed almost unchanged when compared to the current capsule posture estimation method,Endo-SfM.Moreover,the accuracy of posture control is increased by 38.8%with this method,and it can correct the capsule posture in real time,laying the foundation for successful dynamic GI tract diagnosis and therapy.