Virtual Rheological Objects for Visual and Haptic Presentation

这项研究为流变学对象的视觉和触觉呈现奠定了基础。生物组织和食品等流变体在变形过程中表现出粘弹性和塑性两种性质。在本研究中，我们首先建立了基于粒子的流变学对象模型。在粒子模型中引入拓扑保持和体积效应，成功地实现了流变变形的稳定计算。其次，用现场可编程门阵列实现了流变变形计算的逻辑电路。在现场可编程门阵列(现场可编程门阵列)上实现了流变变形的计算，实现了比PC计算快近100倍的计算速度。第三，我们建立了一种基于粒子模型的模型参数识别方法。我们构建了一个实时测量流变体变形和压力分布的系统，以基于测量结果识别模型参数。接下来，我们研究了另一种基于连续介质力学的粒子建模方法。利用约束稳定化方法(CSM)对泊松比为0.5的不可压缩物体的变形进行了稳定计算，包括1)基于粒子模型的大变形计算，2)基于现场可编程门阵列的实时变形计算，3)激光测距仪和触觉传感器的实时变形测量，4)随机算法的模型参数辨识。基于这些结果，对压缩比为10%的变形进行了模拟，对含有1000个以上粒子的基于粒子的模型进行了视频帧速率计算，模型识别的误差在10%以内。

This research establishes fundamental technologies for visual and haptic presentation of rheological objects. Rheological objects such as biological tissues and foods show both viscoelastic and plastic nature in their deformation. In this research, we first established a particle-based modeling of rheological objects. Introducing topology maintaining and volumatic effects, we have succeeded a stable computation of rheological deformation in the particle-based modeling. Second, we realized a logical circuit to compute the rheological deformation using an FPGA. Implementing the computation of rheological deformation on an FPGA(Field Programmable Gate Array), we realized almost 100 times faster computation than PC computation. Thirdly, we established a calibration method to identify model parameters in a particle-based model. We constructed a system to measure realtime deformation of a rheological object and pressure distribution on it to identify the model parameters based on the measurements. Next, we investigated another particle-based modeling based on continua mechanics. We stably computed the deformation of uncompressive objects where Poisson's ratio is equal to 0.5 using the CSM (Constraint Stabilization Method).This research is summarized as 1) computation of large rheological deformation based on particle-based modeling, 2) realtime deformation computation using FPGA (Field Programmable Gate Array), 3) realtime deformation measurement using a laser range finder and a tactile sensor, and 4) identification of model parameters by randomized algorithm. Based on these results, simulation of deformation where compression ratio is 10%, video-frame rate computation of a particle-based model with over 1000 particles, and model identification with error within 10%.