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The ability to grasp a wider range of objects in size and shape directly relates to the performance of robotic grippers. Adapting to complex geometries of objects requires large degrees of freedom to allow complex configurations. However, complexity in controlling many individual joints leads to introduction of underactuated mechanisms, in which traditional finger designs composed of revolute joints allow only flexion/extension motions. In this article, we propose a length-adjustable linkage mechanism in the underactuated finger controlled by an antagonistic tendon pair. The resulting gripper can elongate the fingers for an increased task space or shorten them for a finer spatial resolution. For tactile sensing, hyperelastic soft sensors are used to stretch with finger elongation. Contact pressures measured by the soft sensors are used in force-feedback control for which either the joint angles or the link lengths are adjusted. Lastly, a multimodal control scheme that combines elongation and flexion modes is demonstrated with tasks of dexterous manipulation. [ABSTRACT FROM AUTHOR]

Electromyography (EMG) is one of the most common methods to detect muscle activities and intentions. However, it has been difficult to estimate accurate hand motions represented by the finger joint angles using EMG signals. We propose an encoder-decoder network with an attention mechanism, an explainable deep learning model that estimates 14 finger joint angles from forearm EMG signals. This study demonstrates that the model trained by the single-finger motion data can be generalized to estimate complex motions of random fingers. The color map result of the after-training attention matrix shows that the proposed attention algorithm enables the model to learn the nonlinear relationship between the EMG signals and the finger joint angles, which is explainable. The highly activated entries in the color map of the attention matrix derived from model training are consistent with the experimental observations in which certain EMG sensors are highly activated when a particular finger moves. In summary, this study proposes an explainable deep learning model that estimates finger joint angles based on EMG signals of the forearm using the attention mechanism. [ABSTRACT FROM AUTHOR]

State estimation is one of the key requirements in robot control, which has been achieved by kinematic and dynamic models combined with motion sensors in traditional robotics. However, it is challenging to acquire accurate proprioceptive information in soft robots due to relatively high noise levels and hysteretic responses of soft actuators and sensors. In this article, we propose a method of estimating real-time states of soft robots by filtering noisy output signals and including hysteresis in the models using a Bayesian network. This approach is useful in constructing a state observer for soft robot control when both the kinematic model of the actuator and the model of the sensor are used. In our method, we regard a hysteresis function as a conditional random process model. We then introduce a dynamic Bayesian network composed of the actuator and the sensor models of the target system using distribution hysteresis mapping. Finally, we show that solving a Bayesian filtering problem is equivalent to suboptimal state estimation of the soft system. This article describes two ways for defining modeling and filtering; one is by Gaussian process regression combined with an extended Kalman filter, and the other is based on variational inference with a particle filter. While the first approach relaxes the uncertainty level in modeling to Gaussian, the second approach illustrates a general probability distribution. We experimentally validate the proposed methods through real-time state estimation of a sensor-integrated soft robotic gripper. The result shows significant improvement in state estimation compared to conventional estimation methods. [ABSTRACT FROM AUTHOR]

The performance of inflatable artificial muscles depends greatly on their designs and the output shapes resulting from the geometric constraints. Although there have been attempts to apply physical constraints on the air chamber to achieve larger stroke lengths and increased force–length ratios, it has been difficult to achieve the above two goals while maintaining a compact form factor. In this article, we propose flat inflatable artificial muscles that have large contraction ratios (up to 0.5) and show increased forces in wider ranges of contractions by adding an internal geometric constraint. Addition of an external constraint, such as rigid plates, further increased the maximum contraction ratio (up to 0.553) through a synergistic effect. We show that various force–length relations can be achieved by adjusting the height of the plates. Theoretical models based on the geometry and the principle of virtual work are experimentally validated using actuator prototypes made of heat-sealable plastic sheets. Also, compact capacitive sensors are integrated in design for proprioceptive feedback of the proposed actuators, and their feasibility and effectiveness are experimentally evaluated through closed-loop control. [ABSTRACT FROM AUTHOR]

Soft and integrated design can enable wearable haptic devices to augment natural human taction. This paper proposes a novel, soft, haptic finger-worn wearable device based on compliant and adhesive silicone skin and lightweight twisted and coiled polymer (TCP) actuators using ultra high molecular weight polyethylene (UHMWPE) fibers to provide lateral skin stretch sensations. Recently, silicone elastomers have been used in wearable sensors and in haptic applications for their high compliance or adhesion. TCP actuators have also demonstrated high power to weight ratios, large stroke length, simple mechanism, and inherent softness. Lateral skin stretch is sensitive to small motions and has been used for intuitive proprioceptive feedback applications. We combined these characteristics to design and manufacture a wearable, functional haptic prototype. Prototype performance was evaluated using an optical tracking system, a force gauge test bench, and compared to vibrotactile haptic feedback in a experiment with 14 healthy participants. Results showed that participant mean reaction times were comparable to those of a vibrotactile feedback system, though task completion times were longer. This paper is the first to employ TCP actuators for haptic stimulation and could serve as a foundation for future applications involving soft wearable haptics in gaming, health, and virtual reality. [ABSTRACT FROM AUTHOR]

Multi-Fingered haptics is imperative for truly immersive virtual reality experience, as many real-world tasks involve finger manipulation. One of the key lacking aspect for this is the absence of technologically and economically viable wearable haptic interfaces that can simultaneously track the finger/hand motions and display multi-degree-of-freedom (DOF) contact forces. In this paper, we propose a novel wearable cutaneous haptic interface (WCHI), which consists of 1) finger tracking modules (FTMs) to estimate complex multi-DOF finger and hand motion; and 2) cutaneous haptic modules (CHMs) to convey three-DOF contact force at the finger-tip. By opportunistically utilizing such different types of sensors as inertial measurement units, force sensitive resistor sensors, and soft sensors, the WCHI can track complex anatomically consistent multi-DOF finger motion while avoiding FTM-CHM electromagnetic interference possibly stemming from their collocation in the small form-factor interface; while also providing the direction and magnitude of three-DOF finger-tip contact force, the feedback of which can significantly enhance the precision of contact force generation against variability among users via their closed-loop control. Human subject study is performed with a virtual peg insertion task to show the importance of both the multi-DOF finger tracking and the three-DOF cutaneous haptic feedback for dexterous manipulation in virtual environment. [ABSTRACT FROM AUTHOR]

Soft sensors are becoming more popular in wearables as a means of tracking human body motions due to their high stretchability and easy wearability. However, previous research not only was limited to only certain body parts, but also showed problems in both calibration and processing of the sensor signals, which are caused by the high nonlinearity and hysteresis of the soft materials and also by the misplacement and displacement of the sensors during motion. Although this problem can be alleviated through redundancy by employing an increased number of sensors, it will lay another burden of heavy processing and power consumption. Moreover, complete full-body motion tracking has not been achieved yet. Therefore, we propose use of deep learning for full-body motion sensing, which significantly increases efficiency in calibration of the soft sensor and estimation of the body motions. The sensing suit is made of stretchable fabric and contains 20 soft strain sensors distributed on both the upper and the lower extremities. Three athletic motions were tested with a human subject, and the proposed learning-based calibration and mapping method showed a higher accuracy than traditional methods that are mainly based on mathematical estimation, such as linear regression. [ABSTRACT FROM AUTHOR]

Each year 35 000 cardiac ablation procedures are performed to treat atrial fibrillation through the use of catheter systems. The success rate of this treatment is highly dependent on the force which the catheter applies on the heart wall. If the magnitude of the applied force is much higher than a certain threshold the tissue perforates, whereas if the force is lower than this threshold the lesion size may be too large and is inconsistent. Furthermore, studies have shown large variability in the applied force from trained physicians during treatment, suggesting that although there might be patient-specific differences, physicians are unable to manually regulate the levels of the force at the site of treatment. Current catheter systems do not provide the physicians with active means for contact force control and are only at most aided by visual feedback of the forces measured in situ. This paper discusses a novel design of a robotic end-effector that integrates mechanisms of sensing and actively controlling of the applied forces into a miniaturized compact form. The required specifications for design and integration were derived from the current application under investigation. An off-the-shelf miniature piezoelectric motor was chosen for actuation, and a force sensing solution was developed to meet the specifications. Experimental characterization of the actuator and the force sensor within the integrated setup show compliance with the specifications and pave the way for future experimentation where closed-loop control of the system can be implemented according to the contact force control strategies for the application. [ABSTRACT FROM PUBLISHER]

This study demonstrates real-time scan plane control dependent on three-dimensional needle bending, as measured from magnetic resonance imaging (MRI)-compatible optical strain sensors. A biopsy needle with embedded fiber Bragg grating (FBG) sensors to measure surface strains is used to estimate its full 3-D shape and control the imaging plane of an MR scanner in real-time, based on the needle's estimated profile. The needle and scanner coordinate frames are registered to each other via miniature radio-frequency (RF) tracking coils, and the scan planes autonomously track the needle as it is deflected, keeping its tip in view. A 3-D needle annotation is superimposed over MR-images presented in a 3-D environment with the scanner's frame of reference. Scan planes calculated based on the FBG sensors successfully follow the tip of the needle. Experiments using the FBG sensors and RF coils to track the needle shape and location in real-time had an average root mean square error of 4.2 mm when comparing the estimated shape to the needle profile as seen in high resolution MR images. This positional variance is less than the image artifact caused by the needle in high resolution SPGR (spoiled gradient recalled) images. Optical fiber strain sensors can estimate a needle's profile in real-time and be used for MRI scan plane control to potentially enable faster and more accurate physician response. [ABSTRACT FROM AUTHOR]

A novel soft strain sensor capable of withstanding strains of up to 100% is described. The sensor is made of a hyperelastic silicone elastomer that contains embedded microchannels filled with conductive liquids. This is an effort of improving the previously reported soft sensors that uses a single liquid conductor. The proposed sensor employs a hybrid approach involving two liquid conductors: an ionic solution and an eutectic gallium-indium alloy. This hybrid method reduces the sensitivity to noise that may be caused by variations in electrical resistance of the wire interface and undesired stress applied to signal routing areas. The bridge between these two liquids is made conductive by doping the elastomer locally with nickel nanoparticles. The design, fabrication, and characterization of the sensor are presented. [ABSTRACT FROM PUBLISHER]

We describe a MRI-compatible biopsy needle instrumented with optical fiber Bragg gratings for measuring bending deflections of the needle as it is inserted into tissues. During procedures, such as diagnostic biopsies and localized treatments, it is useful to track any tool deviation from the planned trajectory to minimize positioning errors and procedural complications. The goal is to display tool deflections in real time, with greater bandwidth and accuracy than when viewing the tool in MR images. A standard 18 ga \times 15 cm inner needle is prepared using a fixture, and 350-\mum-deep grooves are created along its length. Optical fibers are embedded in the grooves. Two sets of sensors, located at different points along the needle, provide an estimate of the bent profile, as well as temperature compensation. Tests of the needle in a water bath showed that it produced no adverse imaging artifacts when used with the MR scanner. [ABSTRACT FROM PUBLISHER]