Jeon Seokhee, Harders Matthias
IEEE Trans Haptics. 2014 Oct-Dec;7(4):477-85. doi: 10.1109/TOH.2014.2330300.
We currently explore the application of haptic augmentation in the context of palpation training systems. The key idea is to modify real touch sensations with computed haptic feedback. In earlier work, we have introduced an algorithmic framework for determining appropriate augmentation forces during interaction at one contact point. In this paper, we present an extension of the approach to deal with manipulations at more than one contact location. At the heart of our method is the data-driven estimation of Hunt-Crossley model parameters in a pre-computation step. Feeding the parameters into a contact dynamics model allows us to approximate the feedback behavior of various physical tissue mock-ups. Further, we combine the parameter estimation with the tracking of the position of a stiffer inclusion in the mock-up. These data are employed to create a model of movement due to external forces. The combination of these models then allows us to represent and render the mutual effects at multiple contact points. Several experiments have been carried out on a setup with two haptic devices. Comparisons of recorded with simulated interaction data demonstrate the performance and potential of our method.
我们目前正在探索触觉增强在触诊训练系统中的应用。其关键思想是利用计算出的触觉反馈来修改真实的触感。在早期工作中，我们引入了一个算法框架，用于在一个接触点的交互过程中确定合适的增强力。在本文中，我们将该方法扩展到处理多个接触位置的操作。我们方法的核心是在预计算步骤中对亨特 - 克罗斯利模型参数进行数据驱动的估计。将这些参数输入到接触动力学模型中，使我们能够近似各种物理组织模型的反馈行为。此外，我们将参数估计与模型中较硬内含物位置的跟踪相结合。这些数据用于创建外力作用下的运动模型。这些模型的结合使我们能够表示和呈现多个接触点处的相互作用。我们在一个配备两个触觉设备的装置上进行了多项实验。记录数据与模拟交互数据的比较证明了我们方法的性能和潜力。
