Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom.
Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
PLoS One. 2020 Aug 11;15(8):e0237379. doi: 10.1371/journal.pone.0237379. eCollection 2020.
This paper provides a solution for fast haptic information gain during soft tissue palpation using a Variable Lever Mechanism (VLM) probe. More specifically, we investigate the impact of stiffness variation of the probe to condition likelihood functions of the kinesthetic force and tactile sensors measurements during a palpation task for two sweeping directions. Using knowledge obtained from past probing trials or Finite Element (FE) simulations, we implemented this likelihood conditioning in an autonomous palpation control strategy. Based on a recursive Bayesian inferencing framework, this new control strategy adapts the sweeping direction and the stiffness of the probe to detect abnormal stiff inclusions in soft tissues. This original control strategy for compliant palpation probes shows a sub-millimeter accuracy for the 3D localization of the nodules in a soft tissue phantom as well as a 100% reliability detecting the existence of nodules in a soft phantom.
本文提出了一种使用变杠杆机构(VLM)探头在软组织触诊中快速获取触觉信息的解决方案。更具体地说,我们研究了探头刚度变化对两种扫掠方向触诊任务中运动觉力和触觉传感器测量的条件似然函数的影响。利用从过去探测试验或有限元(FE)模拟中获得的知识,我们在自主触诊控制策略中实现了这种似然条件。基于递归贝叶斯推理框架,这种新的控制策略自适应地调整探头的扫掠方向和刚度,以检测软组织中异常硬的夹杂。这种用于顺应性触诊探头的原始控制策略在软组织体模中对结节的 3D 定位具有亚毫米级的精度,并且在软体模中检测结节的存在具有 100%的可靠性。