scholarly journals An Add-On Device to Perform Dexterous Grasping Tasks With a Haptic Feedback System

Author(s):  
Jean-Claude Leon ◽  
Thomas Dupeux ◽  
Jean-Rémy Chardonnet ◽  
Jérôme Perret

Achieving grasping tasks in real time with haptic feedback may require the control of a large number of degrees of freedom (DOFs) to model hand and finger movements. This is mandatory to grasp objects with dexterity. Here, a new device called HaptiHand is proposed that can be added to a haptic feedback arm and provide the user with enough DOFs so that he/she can intuitively and dexterously grasp an object, modify the virtual hand configuration and number of fingers with respect to the object while manipulating the object. Furthermore, this device is non-invasive and enables the user to apply forces on the fingers of the virtual hand. The HaptiHand lies inside the user’s hand so that the user can apply and release pressure on it in a natural manner that is transferred to the virtual hand using metaphors. The focus is placed on the description of the technology and structure of the HaptiHand to justify the choices and explain the behavior of the HaptiHand during object grasping and releasing tasks. This is combined with a short description of the models used.

Author(s):  
Jean-Claude Leon ◽  
Thomas Dupeux ◽  
Jean-Rémy Chardonnet ◽  
Jérôme Perret

The simulation of grasping operations in virtual reality (VR) is required for many applications, especially in the domain of industrial product design, but it is very difficult to achieve without any haptic feedback. Force feedback on the fingers can be provided by a hand exoskeleton, but such a device is very complex, invasive, and costly. In this paper, we present a new device, called HaptiHand, which provides position and force input as well as haptic output for four fingers in a noninvasive way, and is mounted on a standard force-feedback arm. The device incorporates four independent modules, one for each finger, inside an ergonomic shape, allowing the user to generate a wide range of virtual hand configurations to grasp naturally an object. It is also possible to reconfigure the virtual finger positions when holding an object. The paper explains how the device is used to control a virtual hand in order to perform dexterous grasping operations. The structure of the HaptiHand is described through the major technical solutions required and tests of key functions serve as validation process for some key requirements. Also, an effective grasping task illustrates some capabilities of the HaptiHand.


Author(s):  
Shiyu Zhang ◽  
Shuling Dai

To obtain real-time interactions in the virtual cockpit system (VCS), a real-time trajectory generation method based on dynamical nonlinear optimization and regression prediction for the haptic feedback manipulator (HFM) is presented in this paper. First, a haptic feedback system based on servoserial manipulator is constructed. Then, the trajectory planning problem for the HFM is formulated as a nonlinear optimization problem to balance the motion time and power consumption and ensure the safety of physical human–robot interactions (pHRI). Multiple optimization problems are solved to generate the optimal database off-line. Finally, the classified multivariate (CM) regression method is presented to learn the database and generate optimal trajectories with arbitrary initial and objective positions on-line. Results show that trajectories with rapidity, safety, and lower power consumption can be generated in real-time by this method, which lay a basis of haptic interactions in the VCS.


Author(s):  
Zhenhua Zhu ◽  
Shuming Gao ◽  
Huagen Wan ◽  
Yang Luo ◽  
Wenzhen Yang

The sense of touch is an important way for humans to feel the world. It is very important to provide realistic haptic feedback in virtual assembly applications as to enhancing immersion experience and improving efficiency. This paper presents a novel approach for grasp identification and multi-finger haptic feedback for virtual assembly. Firstly, the Voxmap-PointShell (VPS) algorithm is adapted and utilized to detect collisions between a dexterous virtual hand and a mechanical component or between two mechanical components, and collision detection results are used to guide the motion of a virtual hand. Then collision forces at collision points are calculated (using Hooke Law), classified and converted. Finally, forces received at fingertips of a virtual hand are used to identify whether or not a virtual hand can grasp a mechanical component, and are mapped to exert forces at user’s fingertips with a CyberGrasp force feedback system. Our approach has been incorporated and verified in a CAVE virtual environment.


2014 ◽  
Author(s):  
Rozaimi Ghazali ◽  
◽  
Asiah Mohd Pilus ◽  
Wan Mohd Bukhari Wan Daud ◽  
Mohd Juzaila Abd Latif ◽  
...  

Author(s):  
Schahrazad Soltane ◽  
Shahad Al-Mutabeq ◽  
Mona Masood ◽  
Rawan Al-Otaibi ◽  
Safiah Abdul Raouf ◽  
...  

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