The fingertips are one of the most important and sensitive parts of our body. They are the first stimulated areas of the hand when we interact with our environment. Providing haptic feedback to the fingertips in virtual reality could, thus, drastically improve perception and interaction with virtual environments. In this paper, we present a modular approach called HapTip to display such haptic sensations at the level of the fingertips. This approach relies on a wearable and compact haptic device able to simulate 2 Degree of Freedom (DoF) shear forces on the fingertip with a displacement range of ±2 mm. Several modules can be added and used jointly in order to address multi-finger and/or bimanual scenarios in virtual environments. For that purpose, we introduce several haptic rendering techniques to cover different cases of 3D interaction, such as touching a rough virtual surface, or feeling the inertia or weight of a virtual object. In order to illustrate the possibilities offered by HapTip, we provide four use cases focused on touching or grasping virtual objects. To validate the efficiency of our approach, we also conducted experiments to assess the tactile perception obtained with HapTip. Our results show that participants can successfully discriminate the directions of the 2 DoF stimulation of our haptic device. We found also that participants could well perceive different weights of virtual objects simulated using two HapTip devices. We believe that HapTip could be used in numerous applications in virtual reality for which 3D manipulation and tactile sensations are often crucial, such as in virtual prototyping or virtual training.
In advanced motor control systems, an accurate knowledge of motor parameters is essential in order to achieve good performances. Some of these parameters, such as stator resistances, are sometimes given by constructors, but they vary according to the operating conditions. This paper presents and compares two parameter identification methods of an actuator used in haptic interfaces which, in this case, is a Permanent Magnet Synchronous Machine (PMSM). Using these methods, the electrical parameters of the PMSM can be determined during different operating conditions. Physical parameters estimation of a dynamic 4-parameter model is performed on the one hand, using an Output Error identification method based on a Non Linear Programming algorithm and on the other hand, using an identification method based on Least-Squares techniques and inverse models.
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