Gant électronique | EPFL Graph Search

vignette|alt=Un gant de données|Un gant de données. Un gant électronique (aussi appelé gant numérique ou gant de réalité virtuelle, ou encore, plus rarement, gant de données) est un gant comportant des capteurs, qui permet à un utilisateur de saisir presque naturellement un objet virtuel et de le manipuler, en numérisant en temps réel les mouvements de la main. Il est utilisé pour l'interface homme-machine dans la réalité virtuelle. Remarque : un simple capteur de localisation peut en général suffire à la prise et à la manipulation d’un objet. Cela explique que peu d’applications exploitent les gants numériques et que, par conséquent, leur développement commercial est lent. Certains gants électroniques sont dotés d'un dispositif à retour tactile qui permet aux utilisateur de ressentir ce qu'il touchent. Ce type de gant électronique est alors dit gant haptique ou gant tactile, ou encore, plus rarement, gant sensitif. Gants à fibres optiques Ce fut le premier principe utili

Haptic glove for virtual reality using magneto-rheological fluid actuators

Kevin Jamolli

Le projet a pour but de concevoir un gant à retour de force. Ce gant devra agir sur le bout des doigts de l'utilisateur afin de recréer la sensation de saisie d'objet entre ses doigts. Dans une première approche, nous nous focaliserons sur un seul doigt: l'index. Et nous développerons un prototype capable d'appliquer une force au bout de celui-ci. La force sera générée à l'aide d'un frein unidirectionnel à fluide magnéto-rhéologique; il faudra donc que le frein puisse être fixé sur le gant. Ce type de frein a commencé à être développé au sein du laboratoire dans des projets précédents Nous pouvons donc reprendre ce qui a déjà été réalisé dans ce domaine pour l'appliquer à notre gant à retour de force.

2010

Grasping for the Task

Ravin Luis De Souza

The significant advances made in the design and construction of anthropomorphic robot hands, endow them with prehensile abilities reaching that of humans. However, using these powerful hands with the same level of expertise that humans display is a big challenge for robots. Traditional approaches use finger-tip (precision) or enveloping (power) methods to generate the best force closure grasps. However, this ignores the variety of prehensile postures available to the hand and also the larger context of arm action. This thesis explores a paradigm for grasp formation based on generating oppositional pressure within the hand, which has been proposed as a functional basis for grasping in humans (MacKenzie and Iberall, 1994). A set of opposition primitives encapsulates the hand's ability to generate oppositional forces. The oppositional intention encoded in a primitive serves as a guide to match the hand to the object, quantify its functional ability and relate this to the arm. In this thesis we leverage the properties of opposition primitives to both interpret grasps formed by humans and to construct grasps for a robot considering the larger context of arm action. In the first part of the thesis we examine the hypothesis that hand representation schemes based on opposition are correlated with hand function. We propose hand-parameters describing oppositional intention and compare these with commonly used methods such as joint angles, joint synergies and shape features. We expect that opposition-based parameterizations, which take an interaction-based perspective of a grasp, are able to discriminate between grasps that are similar in shape but different in functional intent. We test this hypothesis using qualitative assessment of precision and power capabilities found in existing grasp taxonomies. The next part of the thesis presents a general method to recognize oppositional intention manifested in human grasp demonstrations. A data glove instrumented with tactile sensors is used to provide the raw information regarding hand configuration and interaction force. For a grasp combining several cooperating oppositional intentions, hand surfaces can be simultaneously involved in multiple oppositional roles. We characterize the low-level interactions between different surfaces of the hand based on captured interaction force and reconstructed hand surface geometry. This is subsequently used to separate out and prioritize multiple and possibly overlapping oppositional intentions present in the demonstrated grasp. We evaluate our method on several human subjects across a wide range of hand functions. The last part of the thesis applies the properties encoded in opposition primitives to optimize task performance of the arm, for tasks where the arm assumes the dominant role. For these tasks, choosing the strongest power grasp available (from a force-closure sense) may constrain the arm to a sub-optimal configuration. Weaker grasp components impose fewer constraints on the hand, and can therefore explore a wider region of the object relative pose space. We take advantage of this to find the good arm configurations from a task perspective. The final hand-arm configuration is obtained by trading of overall robustness in the grasp with ability of the arm to perform the task. We validate our approach, using the tasks of cutting, hammering, screw-driving and opening a bottle-cap, for both human and robotic hand-arm systems.

EPFL2016

Hand-worn Haptic Interface for Drone Teleoperation

Dario Floreano, Matteo Macchini, Fabrizio Schiano, Antoine Weber

Drone teleoperation is usually accomplished using remote radio controllers, devices that can be hard to master for inexperienced users. Moreover, the limited amount of information fed back to the user about the robot's state, often limited to vision, can represent a bottleneck for operation in several conditions. In this work, we present a wearable interface for drone teleoperation and its evaluation through a user study. The two main features of the proposed system are a data glove to allow the user to control the drone trajectory by hand motion and a haptic system used to augment their awareness of the environment surrounding the robot. This interface can be employed for the operation of robotic systems in line of sight (LoS) by inexperienced operators and allows them to safely perform tasks common in inspection and search-and-rescue missions such as approaching walls and crossing narrow passages with limited visibility conditions. In addition to the design and implementation of the wearable interface, we performed a systematic study to assess the effectiveness of the system through three user studies (n= 36) to evaluate the users' learning path and their ability to perform tasks with limited visibility. We validated our ideas in both a simulated and a real-world environment. Our results demonstrate that the proposed system can improve teleoperation performance in different cases compared to standard remote controllers, making it a viable alternative to standard Human-Robot Interfaces.

2020

Grasping for the Task

Ravin Luis De Souza

EPFL2016