We investigated the ability of two fingers to discriminate stiffness with stochastic resonance. It is known that the haptic perception at the fingertip improves when vibrotactile noise propagates to the fingertip, which is a phenomenon called the stochastic resonance. The improvement in the haptic sensation of a fingertip depends on the intensity of the noise propagating to the fingertip. An improvement in the haptic sensation of multiple fingertips does not require multiple noise sources, such as vibrators, to be attached to multiple fingertips; i.e., even a single vibrator can propagate noise to multiple fingers. In this study, we focus on stiffness discrimination as a task using multiple fingers, in which the thumb and index finger are used to touch an object and perceive its stiffness. Subsequently, we demonstrate that the stiffness perception is improved by propagating sufficiently intense noise to the thumb and index finger using only a single vibrator. The findings indicate the possibility of improving the haptic sensation at multiple fingertips using one vibrator.

In the recently growing field of virtual reality, the virtual grasping sensation remains in its infancy, and the discrepancy between real and virtual grasping has become a problem. This article develops a new haptic glove that presents the sensation of different types of grasping (e.g., power/precision/intermediate grasping) a compliant object. To this end, we develop an exoskeleton that fixes the movements of fingers to present a braking force and a flexible pad that reproduces a wide range of stiffness. By applying the “string jamming mechanism,” our exoskeleton has functions that existing devices do not. In that, it constrains the motion of finger extension/flexion and adduction/abduction with only one actuator and presents the braking force to all surfaces of the finger. In addition, we propose a lightweight and compact variable-stiffness pad that reproduces an extensive stiffness range based on the layer jamming technique. We conducted experiments to evaluate the mechanical performance of the prototype integrating the exoskeleton and the variable-stiffness pad and demonstrated the usefulness of the proposed glove.

To learn motor skills using a finger in many haptic training systems, a user places his or her finger in a holder to communicate with a haptic interface. It is known that the force-detection capability at the fingertip is reduced when the user's finger is enclosed in a holder. The learning performance might therefore be impaired in training. Stochastic resonance is known to improve sensitivity at the fingertip. We thus propose a fingertip force learning method using stochastic resonance. We first examine the effect of stochastic resonance on the user haptic performance in a two-dimensional task when the fingertip is within the finger holder. The outcomes indicate that the user fingertip sensitivity increases even when the finger is placed within a holder. We next perform force learning tasks in one- and two-dimensional space. Our proposed method that combines haptic feedback and stochastic resonance is compared with the method in which only haptic feedback is provided to the user in force leaning tasks. Results obtained using the proposed method indicate that the performance outcome of the proposed method is higher than that of the comparison method. This study demonstrates the potential of the proposed method for motor learning tasks.

In this paper, we address the stability analysis of swarm robots with heterogeneous abilities for velocity and acceleration. Each robot also has a unique sensing region with a limited angle of field of view. We previously proposed a decentralized navigation method for such swarm robots. With the method, connectivity maintenance is achieved while satisfying physical limitations which are unique to each robot. Here, we focus on the shape of the whole swarm and mathematically prove that the shape and orientations of all robots eventually converge to the equilibrium state. Furthermore, we show experimental results to confirm the convergence.

This paper addresses the adaptive synchronization problem of networked mechanical systems in task space with time-varying communication delays, where both kinematic and dynamic uncertainties are considered and the information flow in the networks is represented by a directed graph. Based on a novel coordination auxiliary system, we first extend existing feedback architecture to achieve synchronization of networked mechanical systems in task space with slow-varying delays. Given that abrupt turns arise for the delays sometimes, we then propose a delay-independent adaptive synchronization control scheme which removes the requirement of the slow-varying condition. Both of the two control schemes are established with time-domain approaches by using Lyapunov-Krasovskii functions. Simulation results are provided to demonstrate the effectiveness of the proposed control schemes.

When we touch a human body, the flesh yields to our touch and we feel a sensation of softness. In virtual training systems for medical procedures such as palpation, the display of softness at the fingertips is essential. This paper proposes a softness-display device using a flexible sheet, and we present the concept of softness display at multiple fingers by combining the developed softness-display device and a multi-fingered haptic interface robot consisting of a five-fingered hand and an arm. Further, we carried out several experiments, the results of which show the validity of the proposed system and its great potential.

To display three-directional forces at a user's multiple fingertips and to allow the user to grasp a small virtual object, we previously developed a side-faced-type multi-fingered haptic interface. This haptic interface consists of a five-fingered haptic hand and an arm, and the haptic interface is located on the side of the user's body. Therefore, there is a risk that the haptic hand and the user's hand will collide while the user manipulates the haptic interface. In this paper, we realized a control system for collision avoidance for a side-faced-type multi-fingered haptic interface, and present experimental results demonstrating its validity.

This paper presents the design of tool-type devices for a multifingered Haptic Interface Robot (HIRO), and summarizes the experimental results. HIRO consists of a robot arm with a five-fingered hand to which a variety of tools can be attached. The system is able to present the force sensation of many tool-type devices. In the medical field, manufacturing industry, and other fields, there are tools of a variety of shapes with a range of uses, and a haptic interface that can present the force sensation for many tools will be important for virtual training systems. HIRO has five fingers, and thus, we must clarify how many fingers need to be connected to the tool-type device and which fingers should be used for the connection. Solving these problems is important with regard to presenting an operator the force feeling through the tool-type device. To solve these problems, we propose an optimal connection method from the mobility and singularity points of view, and we have developed the tool-type devices for HIRO based on the proposed method. We describe here several experiments that were carried out to investigate the performance of the developed devices.

This letter introduces the averaged learning subspace method (ALSM) that can be applied directly to original hyperspectral data for the purpose of classifying land cover. The ALSM algorithm of classification consists of the following iterative steps: (1) generate the initial appropriate feature subspace for each class in training datasets using the class-featuring information compression method, and (2) update the subspaces according to the maximum projection principle. We compare ALSM with the support vector machine classifier. By conducting experiments on two hyperspectral datasets (48 bands and 191 bands, respectively), we demonstrate that the ALSM can make dimensional reduction and classification simultaneously. When compared with the SVM classifier, it appears that the ALSM can achieve a higher accuracy on classification in some cases.