Nicholas Wettels

Biomimetic Tactile Sensor Array

The performance of robotic and prosthetic hands in unstructured environments is severely limited by their having little or no tactile information compared to the rich tactile feedback of the human hand. We are developing a novel, robust tactile sensor array that mimics the mechanical properties and distributed touch receptors of the human fingertip. It consists of a rigid core surrounded by a weakly conductive fluid contained within an elastomeric skin. The sensor uses the deformable properties of the finger pad as part of the transduction process. Multiple electrodes are mounted on the surface of the rigid core and connected to impedance-measuring circuitry safely embedded within the core. External forces deform the fluid path around the electrodes, resulting in a distributed pattern of impedance changes containing information about those forces and the objects that applied them. Here we describe means to optimize the dynamic range of individual electrode sensors by texturing the inner surface of the silicone skin. Forces ranging from 0.1 to 30 N produced impedances ranging from 5 to 1000 kΩ. Spatial resolution (below 2 mm) and frequency response (above 50 Hz) appeared to be limited only by the viscoelastic properties of the silicone elastomeric skin.

Grip Control Using Biomimetic Tactile Sensing Systems

We present a proof-of-concept for controlling the grasp of an anthropomorphic mechatronic prosthetic hand by using a biomimetic tactile sensor, Bayesian inference, and simple algorithms for estimation and control. The sensor takes advantage of its compliant mechanics to provide a triaxial force sensing end-effector for grasp control. By calculating normal and shear forces at the fingertip, the prosthetic hand is able to maintain perturbed objects within the force cone to prevent slip. A Kalman filter is used as a noise-robust method to calculate tangential forces. Biologically inspired algorithms and heuristics are presented that can be implemented online to support rapid, reflexive adjustments of grip.

Signal processing and fabrication of a biomimetic tactile sensor array with thermal, force and microvibration modalities

We have developed a finger-shaped sensor array that provides simultaneous information about the contact forces, microvibrations and thermal fluxes induced by contact with external objects. In this paper, we describe a microprocessor-based signal conditioning and digitizing system for these sensing modalities and its embodiment on a flex-circuit that facilitates efficient assembly of the entire system via injection molding. Thermal energy from the embedded electronics is used to heat the finger above ambient temperature, similar to the biological finger. This enables the material properties of contacted objects to be inferred from thermal transients measured by a thermistor in the sensor array. Combining sensor modalities provides synergistic benefits. For example, the contact forces for exploratory movements can be calibrated so that thermal and microvibration data can be interpreted more definitively.

Biomimetic Tactile Sensor for Control of Grip

We are developing a novel, robust tactile sensor array that mimics the human fingertip and its distributed set of touch receptors. The mechanical components are similar to a fingertip, with a rigid core surrounded by a weakly conductive fluid contained within an elastomeric skin. It uses the deformable properties of the finger pad as part of the transduction process. Multiple electrodes are mounted on the surface of the rigid core and connected to impedance measuring circuitry within the core. External forces deform the fluid path around the electrodes, resulting in a distributed pattern of impedance changes containing information about those forces and the objects that applied them. Here we report preliminary results with prototypes of the sensor, and we propose strategies for extracting features related to the mechanical inputs and using this information for reflexive grip control.

Haptic feature extraction from a biomimetic tactile sensor: Force, contact location and curvature

The BioTac® is a biomimetic tactile sensor for grip control and object characterization. It has three sensing modalities: thermal flux, microvibration and force. In this paper, we discuss feature extraction and interpretation of the force modality data. The data produced by this force sensing modality during sensor-object interaction are monotonic but non-linear. Algorithms and machine learning techniques were developed and validated for extracting the radius of curvature (ROC), point of application of force (PAF) and force vector (FV). These features have varying degrees of usefulness in extracting object properties using only cutaneous information; most robots can also provide the equivalent of proprioceptive sensing. For example, PAF and ROC is useful for extracting contact points for grasp and object shape as the finger depresses and moves along an object; magnitude of FV is useful in evaluating compliance from reaction forces when a finger is pushed into an object at a given velocity while direction is important for maintaining stable grip.

Deformable skin design to enhance response of a biomimetic tactile sensor

Grasping of objects by robotic hands in unstructured environments demands a sensor surface that is durable, compliant, and responsive to various force and slip conditions. A compliant and robust skin can be as critical to grasping objects as the sensor it protects. In an effort to combine compliant mechanics and robust sensing, a biomimetic tactile sensor is being developed. Deformations of its skin can be detected by displacing a conductive fluid from the vicinity of electrodes on a rigid core. In this study, we used simplified finite element models to understand the effects of various textures for the inner surface of the skin and then produced the more promising textures by molding the elastomeric skin material against negatives made by stereolithography. The impedance vs. force relationships obtained with these molded skins had the predicted and desired wide dynamic range. By selecting the appropriate materials for the skin and fluid, previously described problems with hysteresis and diffusion losses have been greatly reduced.

Multimodal Tactile Sensor

We have developed a finger-shaped sensor array (BioTac®) that provides simultaneous information about contact forces, microvibrations and thermal fluxes, mimicking the full cutaneous sensory capabilities of the human finger. For many tasks, such as identifying objects or maintaining stable grasp, these sensory modalities are synergistic. For example, information about the material composition of an object can be inferred from the rate of heat transfer from a heated finger to the object, but only if the location and force of contact are well controlled. In this chapter we introduce the three sensing modalities of our sensor and consider how they can be used synergistically. Tactile sensing and signal processing is necessary for human dexterity and is likely to be required in mechatronic systems such as robotic and prosthetic limbs if they are to achieve similar dexterity.

Understanding haptics by evolving mechatronic systems.

Haptics can be defined as the characterization and identification of objects by voluntary exploration and somatosensory feedback. It requires multimodal sensing, motor dexterity, and high levels of cognitive integration with prior experience and fundamental concepts of self versus external world. Humans have unique haptic capabilities that enable tool use. Experimental animals have much poorer capabilities that are difficult to train and even more difficult to study because they involve rapid, subtle, and variable movements. Robots can now be constructed with biomimetic sensing and dexterity, so they may provide a suitable platform on which to test theories of haptics. Robots will need to embody such theories if they are ever going to realize the long-standing dream of working alongside humans using the same tools and objects.

Integrated dynamic and static tactile sensor: focus on static force sensing

Object grasping by robotic hands in unstructured environments demands a sensor that is durable, compliant, and responsive to static and dynamic force conditions. In order for a tactile sensor to be useful for grasp control in these, it should have the following properties: tri-axial force sensing (two shear plus normal component), dynamic event sensing across slip frequencies, compliant surface for grip, wide dynamic range (depending on application), insensitivity to environmental conditions, ability to withstand abuse and good sensing behavior (e.g. low hysteresis, high repeatability). These features can be combined in a novel multimodal tactile sensor. This sensor combines commercial-off-the-shelf MEMS technology with two proprietary force sensors: a high bandwidth device based on PZT technology and low bandwidth device based on elastomers and optics. In this study, we focus on the latter transduction mechanism and the proposed architecture of the completed device. In this study, an embedded LED was utilized to produce a constant light source throughout a layer of silicon rubber which covered a plastic mandrel containing a set of sensitive phototransistors. Features about the contacted object such as center of pressure and force vectors can be extracted from the information in the changing patterns of light. The voltage versus force relationship obtained with this molded humanlike finger had a wide dynamic range that coincided with forces relevant for most human grip tasks.

Large-scale self-tuning solid-state kinetic energy harvester

In recent years there has been a strong emphasis on kinetic (vibration) energy harvesting using smart structure technology. This emphasis has been driven in large part by industry demand for powering sensors and wireless telemetry of sensor data in places into which running power and data cables is difficult or impossible. Common examples are helicopter drive shafts and other rotating equipment. In many instances, available space in these locations is highly limited, resulting in a trend for miniaturization of kinetic energy harvesters. While in some cases size limitations are dominant, in other cases large and even very large harvesters are possible and even desirable since they may produce significantly more power. Examples of large-scale energy harvesting include geomatics, which is the discipline of gathering, storing, processing, and delivering spatially referenced information on vast scales. Geomatics relies on suites of various sensors and imaging devices such as meteorological sensors, seismographs, high-resolution cameras, and LiDARs. These devices may be stationed for prolonged periods of time in remote and poorly accessible areas and are required to operate continuously over prolonged periods of time. In other cases, sensing and imaging equipment may be mounted on land, sea, or airborne platforms and expected to operate for many hours on its own power. Providing power to this equipment constitutes a technological challenge. Other cases may include commercial buildings, unmanned powered gliders and more. Large scale kinetic energy harvesting thus constitutes a paradigm shift in the approach to kinetic energy harvesting as a whole and as often happens it poses its own unique technological challenges. Primarily these challenges fall into two categories: the cost-effective manufacturing of large and very large scale transducing elements based on smart structure technology and the continuous optimization (tuning) of these transducers for various operating conditions. Current research proposes the simultaneous solution of both of the aforementioned challenges via the use of specialized technology for the incorporation of large numbers of piezoelectric transducers into standard printed circuit boards and the continuous control of structural resonance via the application of adaptive compressive stress. Used together, these technologies allow for fully scalable and tunable kinetic energy harvesting. Since the design is modular in nature and a typical size of a single module can easily reach dimensions of 60 by 40 centimeters, there is virtually no upper limit on the size of the harvester other than the limits that derive from its specific applications and placement. The use of compressive forces rather than the commonly used non-structural mass for the tuning of the harvester frequency to the disturbing frequency allows for continuous adaptive tuning while at the same time avoiding the undesirable vibration damping effects of non-structural mass. A proof of concept large-scale harvester capable of manual compressive force tuning was built as part of the current study and preliminary tests were conducted. The tests validate the proposed approach showing power generation on the order of 10 mW at disturbing frequencies between 10 and 100 Hz, with RMS voltages reaching over 20 volts and RMS currents over 2 mA, with proven potential for 50 mW with over 100 VAC and 10 mA for a transducing panel 20 by 10 cm. The results also validate the tuning via compressive force approach, showing strong dependence of energy harvesting efficiency on the compressive force applied to the transducing panel.