A versatile biomimetic controller for contact tooling and haptic exploration

International audienceThis article presents a versatile controller that enables various contact tooling tasks with minimal prior knowledge of the tooled surface. The controller is derived from results of neuroscience studies that investigated the neural mechanisms utilized by humans to control and learn complex interactions with the environment. We demonstrate here the versatility of this controller in simulations of cutting, drilling and surface exploration tasks, which would normally require different control paradigms. We also present results on the exploration of an unknown surface with a 7-DOF manipulator, where the robot builds a 3D surface map of the surface profile and texture while applying constant force during motion. Our controller provides a unified control framework encompassing behaviors expected from the different specialized control paradigms like position control, force control and impedance control

Toward wearable pneumatic haptic devices for microscale force feedback applications

The addition of haptic feedback to systems and devices allows a human user to gain a more complete understanding of the remote environment they are working in. Several applications, such as robotic minimally invasive surgery (MIS) and virtual reality gaming, have drawn interests for integrating haptic or tactile feedback onto remote operating tools. While both research studies and commercial products have clearly demonstrated the benefits of adding haptic feedback, many open questions remain for reaching the full potential of haptic feedback. This research focuses on investigating how light-weight, low-cost pneumatic haptic devices can be deployed on human hands, possibly in multiple locations, to enhance user comprehension of force feedback. The aim is to enhance the understanding of microscale pneumatic devices in their potential and limitations as a wearable haptic feedback system. This work investigates the design, construction, and testing of a binary pneumatic tactile display. Pneumatically actuated devices are chosen because they are light-weight, low-cost, and less-invasive in nature. Arrays of pneumatic balloons of different sizes were designed and constructed by taking into consideration the ease of the fabrication process and the effectiveness of feedback when placed on human hands. Human perception experiments were performed to test the pneumatic balloon arrays to determine the potential of providing binary haptic feedback. The results showed differences in sensitivity due to the location where the balloon array is placed as well as the size of the device. In addition, it appears that the use of multiple balloon arrays placed in different parts of a human hand can improve the overall effectiveness of the feedback, even if they are not placed on the most sensitive areas. Lastly, the experiments demonstrated the potential of using multiple pneumatic balloon arrays to produce identifiable binary patterns

Touching on elements for a non-invasive sensory feedback system for use in a prosthetic hand

Hand amputation results in the loss of motor and sensory functions, impacting activities of daily life and quality of life. Commercially available prosthetic hands restore the motor function but lack sensory feedback, which is crucial to receive information about the prosthesis state in real-time when interacting with the external environment. As a supplement to the missing sensory feedback, the amputee needs to rely on visual and audio cues to operate the prosthetic hand, which can be mentally demanding. This thesis revolves around finding potential solutions to contribute to an intuitive non-invasive sensory feedback system that could be cognitively less burdensome and enhance the sense of embodiment (the feeling that an artificial limb belongs to one’s own body), increasing acceptance of wearing a prosthesis.A sensory feedback system contains sensors to detect signals applied to the prosthetics. The signals are encoded via signal processing to resemble the detected sensation delivered by actuators on the skin. There is a challenge in implementing commercial sensors in a prosthetic finger. Due to the prosthetic finger’s curvature and the fact that some prosthetic hands use a covering rubber glove, the sensor response would be inaccurate. This thesis shows that a pneumatic touch sensor integrated into a rubber glove eliminates these errors. This sensor provides a consistent reading independent of the incident angle of stimulus, has a sensitivity of 0.82 kPa/N, a hysteresis error of 2.39±0.17%, and a linearity error of 2.95±0.40%.For intuitive tactile stimulation, it has been suggested that the feedback stimulus should be modality-matched with the intention to provide a sensation that can be easily associated with the real touch on the prosthetic hand, e.g., pressure on the prosthetic finger should provide pressure on the residual limb. A stimulus should also be spatially matched (e.g., position, size, and shape). Electrotactile stimulation has the ability to provide various sensations due to it having several adjustable parameters. Therefore, this type of stimulus is a good candidate for discrimination of textures. A microphone can detect texture-elicited vibrations to be processed, and by varying, e.g., the median frequency of the electrical stimulation, the signal can be presented on the skin. Participants in a study using electrotactile feedback showed a median accuracy of 85% in differentiating between four textures.During active exploration, electrotactile and vibrotactile feedback provide spatially matched modality stimulations, providing continuous feedback and providing a displaced sensation or a sensation dispatched on a larger area. Evaluating commonly used stimulation modalities using the Rubber Hand Illusion, modalities which resemble the intended sensation provide a more vivid illusion of ownership for the rubber hand.For a potentially more intuitive sensory feedback, the stimulation can be somatotopically matched, where the stimulus is experienced as being applied on a site corresponding to their missing hand. This is possible for amputees who experience referred sensation on their residual stump. However, not all amputees experience referred sensations. Nonetheless, after a structured training period, it is possible to learn to associate touch with specific fingers, and the effect persisted after two weeks. This effect was evaluated on participants with intact limbs, so it remains to evaluate this effect for amputees.In conclusion, this thesis proposes suggestions on sensory feedback systems that could be helpful in future prosthetic hands to (1) reduce their complexity and (2) enhance the sense of body ownership to enhance the overall sense of embodiment as an addition to an intuitive control system

Discrimination de textures et quantification de rugosité par algorithme d'apprentissage

Alors que la recherche en robotique progresse sur la manipulation d’objets, nous nous sommes intéressés à trouver des informations qui pourraient permettre d’améliorer la dextérité du robot si ce dernier en avait connaissance avant la manipulation. Nous supposons que lorsqu’une personne observe un objet, elle intègre en même temps un cetain nombre de ses caractéristiques (forme, texture, rugosité, fragilité, déformabilité...). Ces informations conditionnent son mouvement et lui permettent de mieux manipuler l’objet. En allant dans ce sens, nous cherchons à donner au toucher un moyen d’acquérir ce type d’informations. Pour cela, l’objectif de notre étude est de proposer un algorithme léger et utilisant des mouvements d’acquisition rapides pour permettre à un robot de distinguer des textures et d’estimer la rugostié. Cet algorithme a pour objectif de pouvoir être utilisé en temps réel. Dans un premier temps, nous sommes repartis de différentes études sur la discrimination de textures. Nous avons réalisé trois études visant à reconnaître différentes textures malgré des vitesses, des forces ou une orientation d’acquisition différentes. Nous avons aussi fait une étude sur 10 textures fines visant à différencier des textures proches. Pour chaque simulation (RNA ou SVM), optimisée par algorithme génétique, la reconnaissance dépasse 90%. Nous avons noté l’interêt d’utiliser des algorithmes génétiques pour optimiser la simulation. Néanmoins, ces algorithmes sont limités car il est nécessaire d’apprendre une texture avant de pouvoir la reconnaitre. Dans une seconde étude, nous avons cherché à estimer la rugosité d’un matériau en se basant sur une échelle humaine : nous avons demandé à 30 personnes de donner un indice de rugosité allant de 1 à 10 pour 25 textures. Ensuite, nous avons créé un algorithme cherchant à estimer la rugosité en étalonant notre échelle avec les résultats de nos participants. Nous avons pu voir que l’homme a du mal à définir de manière précise la rugosité sur une échelle de 1 à 10. Malgré quatre propositions d’architecture et différents traitements, l’algorithme a aussi eu des difficultés à généraliser les résultats avec uniquement 25 textures. Une étude avec plus de textures serait nécessaire

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Development of highly sensitive multimodal tactile sensor

The sense of touch is crucial for interpreting exteroceptive stimuli, and for moderating physical interactions with one’s environment during object grasping and manipulation tasks. For years, tactile researchers have sought a method that will allow robots to achieve the same tactile sensing capabilities as humans, but the solution has remained elusive. This is a problem for people in the medical and robotics communities, as prosthetic and robotic limbs provide little or no force feedback during contact with objects. During object manipulation tasks, the inability to control the force (applied by the prosthetic or robotic hand to the object) frequently results in damage to the object. Moreover, amputees must compensate for the lack of tactility by paying continuous visual attention to the task at hand, making even the simplest task a frustrating and time-consuming endeavor. We believe that these challenges of object manipulation might best be addressed by a closed feedback loop with a tactile sensory system that is capable of detecting multiple stimuli. To this end, the goal of our research is the development of a tactile sensor that mimics the human sensory apparatus as closely as possible. Thus far, tactile sensors have been unable to match the human sensory apparatus in terms of simultaneous multimodality, high resolution, and broad sensitivity. In particular, previous sensors have typically been able to sense either a wide range of forces, or very low forces, but never both at the same time; and they are designed for either static or dynamic sensing, rather than multimodality. These restrictions have left them unsuited to the needs of robotic applications. Capacitance-based sensors represent the most promising approach, but they too must overcome many limitations. Although recent innovations in the touch screen industry have resolved the issue of processing complexity, through the replacement of clunky processing circuits with new integrated circuits (ICs), most capacitive sensors still remain limited by hysteresis and narrow ranges of sensitivity, due to the properties of their dielectrics. In this thesis, we present the design of a new capacitive tactile sensor that is capable of making highly accurate measurements at low force levels, while also being sensitive to a wide range of forces. Our sensor is not limited to the detection of either low forces or broad sensitivity, because the improved soft dielectric that we have constructed allows it to do both at the same time. To construct the base of the dielectric, we used a geometrically modified silicone material. To create this material, we used a soft-lithography process to construct microfeatures that enhance the silicone’s compressibility under pressure. Moreover, the silicone was doped with high-permittivity ceramic nanoparticles, thereby enhancing the capacitive response of the sensor. Our dielectric features a two-stage microstructure, which makes it very sensitive to low forces, while still able to measure a wide range of forces. Despite these steps, and the complexity of the dielectric’s structure, we were still able to fabricate the dielectric using a relatively simple process. In addition, our sensor is not limited to either static or dynamic sensing; unlike previous sensors, it is capable of doing both simultaneously. This multimodality allows our sensor to detect fluctuating forces, even at very low force levels. Whereas past researchers have used separate technologies for static and dynamic sensing, our dynamic sensing unit is formed with same capacitive technology as the static one. This was possible because of the high sensitivity of our dielectric. We used the entire surface area effectively, by integrating the single dynamic sensing taxel on the same layer as the static sensing taxels. Essentially, the dynamic taxel takes the shape of the lines of a grid, filling in the spaces between the individual static taxels. For further optimization, the geometry of the dynamic taxel has been redesigned by fringing miniature traces of the dynamic taxel within the static taxels. In this way, the entire surface of the sensor is sensitive to both dynamic and static events. While this design slightly reduces the area that is covered by the static taxels, the trade-off is justified, as the capacitive behavior is boosted by the edge effect of the capacitor. The fusion of an innovative dielectric with a capacitive sensing IC has produced a highly sensitive tactile sensor that meets our goals regarding resolution, noise immunity, and overall performance. It is sensitive to forces ranging from 1 mN to 15 N. We verified the functionality of our sensor by mounting it on several of the most popular mechanical hands. Our grasp assessment experiments delivered promising results, and showed how our sensor might be further refined so that it can be used to accurately estimate the outcome of an attempted grasp. In future, we believe that combining an advanced robotic hand with the sensor we have developed will allow us to meet the demand for human-like tactile sensing abilities