一种高分辨力柔性触觉传感器

本文描述了一种基于全内反射原理，用透明橡胶材料作为波导板的柔性触觉传感器。该传感器除具有一般刚性波导板触觉传感器的高分辨力等特点外，还具有柔性好，力灵敏阈值低，不怕碰撞等特点，该传感器特别适合装在智能机器人手爪上，为机器人系统高效率地获取物体形状，位置和姿态信息，实现智能抓握物体，操作物体等功能提供了有效的手段。     

光纤式机器人触觉传感器静态特性的实验研究

介绍了一种光纤式机器人触觉传感器，分析了这种传感器的静态特性，并通过理论分析和实验研究提出了一种消除干扰的方法。实验结果证明，传感器的最大绝对误差为０．０５３ｋｇ，相对误差为０．６２％。     

Autonomous tactile perception: A combined improved sensing and Bayesian nonparametric approach

In recent years, autonomous robots have increasingly been deployed in unknown environments and required to manipulate or categorize unknown objects. In order to cope with these unfamiliar situations, improvements must be made both in sensing technologies and in the capability to autonomously train perception models. In this paper, we explore this problem in the context of tactile surface identification and categorization. Using a highly-discriminant tactile probe based upon large bandwidth, triple axis accelerometer that is sensitive to surface texture and material properties, we demonstrate that unsupervised learning for surface identification with this tactile probe is feasible. To this end, we derived a Bayesian nonparametric approach based on Pitman–Yor processes to model power-law distributions, an extension of our previous work using Dirichlet processes Dallaire et al. (2011). When tested against a large collection of surfaces and without providing the actual number of surfaces, the tactile probe combined with our proposed approach demonstrated near-perfect recognition in many cases and achieved perfect recognition given the right conditions. We consider that our combined improvements demonstrate the feasibility of effective autonomous tactile perception systems.     

Haptic two-finger contact with textiles

Real-time cloth simulation involves many computational challenges to be solved, particularly in the context of haptic applications, where high frame rates are necessary for obtaining a satisfying experience. In this paper, we present an interactive cloth simulation system that offers a compromise between a realistic physics-based simulation of fabrics and a haptic application meeting high requirements in terms of computation speed. Our system allows the user to interact with the fabric using two fingers. The required performance of the system is achieved by introducing an intermediate layer responsible for the simulation of the small part of the surface being in contact with the fingers. Additionally we separate the possible contact situations into different cases, each being individually handled by a specialised contact algorithm.

Towards robust grasping: An analysis of in-hand object motion with FBG optical fibers as force sensing technology

To enable or allow for advanced manipulation scenarios in the envisaged future in robotic manipulation, the introduction of tactile or force-sensing technology is essential. This work introduces a study on a novel integration of fiber sensing elements inside the fingers of robotics grippers that allow obtaining accurate interaction forces during object manipulation. The study focuses on in-hand object motion and analyzes the objects’ dynamic behaviors during grasping or contact phases. The proposed approach classifies the behavior of the gripper-object interaction in real-time by looking at the measured forces in the orthogonal direction of the gripper’s fingers after a filtering stage. The methodology is validated theoretically and evaluated with real experiments using objects presenting different shapes, sizes, and surface properties.     

Force and tactile feedback in preloaded cantilever snap-fits under manual assembly

The purpose of this study was to investigate the tactile feedback of snap-fit fasteners when used in manual assembly. An important aspect of this assembly process is the assembler’s ability to perceive the snap-fit’s engagement. This sensing of engagement yields a high level of confidence that assembly is both complete and secure. Force and tactile feedback are critical elements in this process. Many snap-fits are used in conjunction with sealing elements that produce a constant force that remains in effect after snap-fit assembly is completed. The effect of sealing element preload magnitude and stiffness was studied using a test station, force deflection measurements, and jury pool data. A low value of preload with low stiffness was determined to be most favorable in terms of force and tactile feedback, with no preload only slightly less favorable. In order to sense the engagement signal of the catch, some resistance to assembly was found to be beneficial. A dimensionless term called “engagement signal-to-hold-force ratio” is proposed as an additional way of rating the effect of assembly forces for snap-fits. It was found that higher signal-to-hold-force ratio, as well as higher values of engagement signal, corresponded to higher confidence of assembly among experimental subjects.

Relevance to Industry

In the automotive industry, force and tactile feedback are essential for sensing the full engagement of snap-fit parts during the assembly of critical components, such as electrical and fuel system interconnects. Both the design of the snap-fit and the presence or absence of preload will affect force and tactile feedback. Many of these applications require the compression of an elastic gasket with a preload needed for air, fuel, or electrical isolation. Preload and preload stiffness factors are examined in this paper with the aim of achieving a better understanding of these effects so that snap-fit confidence of assembly and assembly robustness can be enhanced in industrial settings.     

Direction coding using a tactile chair

This laboratory study examined the possibility of using a car seat instrumented with a tactile display to communicate directional information to a driver. A car seat fitted with an 8 × 8 matrix of vibrators embedded in the seat pan was used to code eight different directions. Localization response time and angular accuracy were examined as a function of stimulus direction, presence of a tactile attention cue, temporal pattern, stimulus layout, age, and gender. The mean absolute angular error was 23°, and both localization accuracy and response times were superior for the back left, backward, and back right directions. Of the various temporal pattern/attention cue combinations examined, results favored the relatively fast patterns consisting of vibration bursts of 125 or 250 ms without a centrally located attention cue over 500 ms bursts that were preceded by an attention cue. Observed age and gender effects were relatively modest, suggesting that using tactile cueing to communicate direction is effective across a wide range of users. In addition, the tactile stimulus was detected by more than 90% of the participants under surprise trial conditions. Overall, these results indicate that the tactile chair provides a promising and robust method of providing directional information.     

Contact type dependency of texture classification in a whiskered mobile robot

Actuated artificial whiskers modeled on rat macrovibrissae can provide effective tactile sensor systems for autonomous robots. This article focuses on texture classification using artificial whiskers and addresses a limitation of previous studies, namely, their use of whisker deflection signals obtained under relatively constrained experimental conditions. Here we consider the classification of signals obtained from a whiskered robot required to explore different surface textures from a range of orientations and distances. This procedure resulted in a variety of deflection signals for any given texture. Using a standard Gaussian classifier we show, using both hand-picked features and ones derived from studies of rat vibrissal processing, that a robust rough-smooth discrimination is achievable without any knowledge of how the whisker interacts with the investigated object. On the other hand, finer discriminations appear to require knowledge of the target’s relative position and/or of the manner in which the whisker contact its surface. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.

Iterative learning of grasp adaptation through human corrections

In the context of object interaction and manipulation, one characteristic of a robust grasp is its ability to comply with external perturbations applied to the grasped object while still maintaining the grasp. In this work, we introduce an approach for grasp adaptation which learns a statistical model to adapt hand posture solely based on the perceived contact between the object and fingers. Using a multi-step learning procedure, the model dataset is built by first demonstrating an initial hand posture, which is then physically corrected by a human teacher pressing on the fingertips, exploiting compliance in the robot hand. The learner then replays the resulting sequence of hand postures, to generate a dataset of posture-contact pairs that are not influenced by the touch of the teacher. A key feature of this work is that the learned model may be further refined by repeating the correction-replay steps. Alternatively, the model may be reused in the development of new models, characterized by the contact signatures of a different object. Our approach is empirically validated on the iCub robot. We demonstrate grasp adaptation in response to changes in contact, and show successful model reuse and improved adaptation with additional rounds of model refinement.     

The influence of combined visual and tactile information on finger and eye movements during shape tracing

Abstract

Adaptation experiments in shape tracing were conducted to investigate finger and eye movements in various conditions of visual and tactile information. Maximum velocity, mean velocity, maximum acceleration and reacceleration point were calculated from finger movements. Number of eye fixations and lead time of eye fixation to finger position were calculated from eye movements. The results showed that for the finger movement the values of the indices studied were higher in the combined visual and tactile condition than in the visual only condition. The number of eye fixations decreased when subjects repeated the tracing and was more marked in the combined visual and tactile condition than in the visual only condition. The results suggest that finger movements become faster and use of vision is reduced when both visual and tactile information are given.