CONTACT RECOGNITION USING TACTILE SENSOR

IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS 2012), 2012

Object surface properties are among the most important information which a robot requires in order to effectively interact with an unknown environment. This paper presents a novel haptic exploration strategy for recognizing the physical properties of unknown object surfaces using an intelligent finger. This developed intelligent finger is capable of identifying the contact location, normal and tangential force, and the vibrations generated from the contact in real time. In the proposed strategy, this finger gently slides along the surface with a short stroke while increasing and decreasing the sliding velocity. By applying a dynamic friction model to describe this contact, rich and accurate surface physical properties can be identified within this stroke. This allows different surface materials to be easily distinguished even if when they have very similar texture. Several supervised learning algorithms have been applied and compared for surface recognition based on the obtained surface properties. It has been found that the naïve Bayes classifier is superior to radial basis function network and k-NN method, achieving an overall classification accuracy of 88.5% for distinguishing twelve different surface materials.

Object surface properties are among the most important information which a robot requires in order to effectively interact with an unknown environment. This paper presents a novel haptic exploration strategy for recognizing the physical properties of unknown object surfaces using an intelligent finger. This developed intelligent finger is capable of identifying the contact location, normal and tangential force, and the vibrations generated from the contact in real time. In the proposed strategy, this finger gently slides along the surface with a short stroke while increasing and decreasing the sliding velocity. By applying a dynamic friction model to describe this contact, rich and accurate surface physical properties can be identified within this stroke. This allows different surface materials to be easily distinguished even if when they have very similar texture. Several supervised learning algorithms have been applied and compared for surface recognition based on the obtained surface properties. It has been found that the naïve Bayes classifier is superior to radial basis function network and k-NN method, achieving an overall classification accuracy of 88.5% for distinguishing twelve different surface materials.

2006

This paper discusses a matrix based algorithm to sense unknown forces acting on a 3D tactile skin consisting of thin film force sensors. The skin can be mounted on any robotic system to measure forces acting on the robot's surface upon contact. The paper describes a kinematic model of the forces acting on the skin, stiffness of the skin and an analytical method to extract the value of unknown forces acting in between the discrete sensor array.

IEEE Transactions on Robotics, 2000

The two major components of a robotic tactile sensing system are the tactile sensing hardware at the lower level, and the computational/software tools at the higher level. Focusing on the later, this research assesses the suitability of Computational Intelligence tools for tactile data processing. In this context, this paper addresses the classification of sensed object material from the recorded raw tactile data. For this purpose, three computational intelligence paradigms, namely, Support Vector Machine (SVM), Regularized Least Square (RLS) and Regularized Extreme Learning Machine (RELM) have been employed and their performance compared for the said task. The comparative analysis shows that SVM provides the best trade-off between classification accuracy and computational complexity of the classification algorithm. Experimental results indicate that the Computational Intelligence tools are effective in dealing with the challenging problem of material classification.

2000

A general paradigm for recognizing 3D objects is o ered, and applied to some geometric primitives (spheres, cylinders, cones, and tori). The assumption is that a curve on the surface, or a pair of intersecting curves, was measured with high accuracy (for instance, by a sensory robot). Di erential invariants of the curve(s) are then used to recognize the surface. The motivation is twofold: the output of some devices is not surface range data, but such curves. Also, a considerable speedup is obtained by using curve data, as opposed to surface data which usually contains a much higher number of points.

IEEE Transactions on Instrumentation and Measurement, 2002

A novel smart tactile sensor that recognizes the nature of the surfaces is presented. The approach is based on the idea of analyzing the signal produced when the sensor touches and stimulates the surface. An "intelligent probing" system for material recognition has been developed. It is based on the use of bimorph piezo-ceramic actuators and sensors that allow the unknown surface to be stimulated and the response signal sensed. Two different experimental prototypes of the tactile sensing system have been realized and their performance has been characterized. Several interesting applications have been considered with particular emphasis on the problems of "humanitarian demining" and automatic waste material recycling. Experimental results are given to show the efficiency of the smart measuring system. ).

Proceedings of International Conference on Robotics and Automation, 1997

In this paper we propose an approach for investigating the shape properties of objects with curved surfaces using tactile sensing by a dexterous robot hand. Our proposed Exploratory Procedure (EP) uses multiple fingers to slide along a surface while sensing contact points and surface normals. Our approach incorporates the surface normal information into a B-spline surface fit of the data gathered from this EP. This additional surface normal information was found to improve the approximation of the surface. The shape properties of this B-spline surface are analyzed to recognize the shape of the object. A confidence estimate of the estimated shape is also calculated.

IEEE Transactions on Instrumentation and Measurement, 1994

The paper discusses a tactile sensor with a specially designed compliant overlay which reduces the tactile sensor distortions caused by deformations of the elastic material during probing. The sensor is used for model-based active tactile recognition of fixed 3-D objects. T to enhance the visual capability of robots by improving their ability to identify and measure the position and orientation of objects, especially during robotic manipulation operations [ 11-[4]. A robotic tactile-sensing system for object recognition and manipulation, in essence, emulates the mechanisms of human tactile perception. This is a complex process with two distinct modes: passive touch , produced by the "cutaneous" field of "mechanoreceptive" transducers covered by an elastic material which provides contact force, contact geometric profile, and temperature information, and active tactile sensing, which integrates cutaneous sensory data and "kinesthetic" information (i.e., limb/joint positions and the states of motor muscles) [4] and [ 5 ] . This paper discusses a tactile sensor system with high sampling resolution and its application for the active perception of stationary polyhedral objects which are larger than the tactile probe dimensions. The experimental tactile sensor is based on force-sensitive transducer technology and has an elastic overlay with protruding tabs which provides a de facto spatial sampling. The new elastic overlay design allows for a higher overall sampling resolution than other tactile sensor arrays which use one-piece elastic overlays [SI-[ 111. A tactile sensor is a contact-type measuring instrument which requires an extemal bias force to impose the contact with the touched object. Under the pressure of this force, the local geometric profile of the object's surface indents an elastic Manuscript