G. Canepa

A sensor-based minimally invasive surgery tool for detecting tissutal elastic properties(003) 5323219

Nowadays, the surgeon who is using minimally invasive tools loses almost completely the haptic perception of the manipulated tissue. In particular, he or she loses the perception of the tissue elastic properties. It is possible to modify the actual mini-invasive surgical tools in such a way that they may give a reliable estimation of the manipulated tissue properties for recognition and characterization purpose. In this paper we present a first attempt to realize a prototype of sensor-based surgical tool using a modified commercial tool. Experimental tests have shown that using such a tool could enhance surgeons haptic perception of the manipulated tissue.

Detection of incipient object slippage by skin-like sensing and neural network processing

Detection of incipient slippage is of great importance in robotics for the control of grasping and manipulation tasks. Together with fine-form reconstruction and primitive recognition, it has to be the main feature of an artificial tactile system. The system presented here is based on a neural network used to detect incipient slippage and on a skin-like sensor sensible to normal and shear stresses. Normal and shear stresses components inside the sensor are the input data of the neural net. An important feature of the system is that the a priori knowledge of the friction coefficient between the sensor and the object being manipulated is not needed. To validate the method we worked on both simulated and experimental data. In the first case, the finite element method is used to solve the direct problem of elastic contact in its full nonlinearity by resorting to the lowest number of approximations regarding the real problem. Simulation has shown that the network learns and is robust to noise. Then an experimental test was carried out. Experimental results show that, in a simple case, the method is able to detect the insipiency of slippage between an object and the sensor.

Towards the realization of an artificial tactile system: fine-form discrimination by a tensorial tactile sensor array and neural inversion algorithms

This paper describes techniques and methodologies so far developed to investigate object fine-form discrimination by means of artificial tactile sensors. Sensor arrays, selectively sensitive to stress-tensor components and based on piezoelectric polymer technology, have been realized. Sensor output data are used to solve inverse elastic contact problems, by means of neural networks suitably trained to learn regularized inverse maps. Two possible neural network designs are considered: one is based on the multilayer perceptron trained with the standard backpropagation algorithm, and the other is based on the use of radial basis functions. In both cases, reconstruction of object shapes is demonstrated to be effective and robust with both simulated and real data. >

Fine-form tactile discrimination through inversion of data from a skin-like sensor

Techniques and methodologies being developed to investigate tactile fine-form discrimination through machine perception are described. A tactile sensor array capable of resolving the six independent components of the stress tensor field is briefly described. Robust numerical solutions of inverse elastic contact problems, through discrete inverse operators, are reported.<<ETX>>

Kinematic calibration by means of a triaxial accelerometer

A new method for kinematic calibration of a robot is presented, based on triaxial measurement of acceleration by a sensor fixed to the robot end-point. The kinematic parameters are evaluated by means of a series of simple tests (two for each joint). In these tests each joint, except the one under consideration, is kept fixed. Acceleration and encoder output are then acquired. Simulations and experimental results applied on Sarcos dextrous arm are presented to verify this method.<<ETX>>

Shape from touch by a neural net

The authors report the implementation and testing of a neural network algorithm to solve the fine-form discrimination problem. The skin-like sensor design is briefly reviewed, and the fine-form discrimination task is stated as an inverse problem of contact mechanics. The inverse problem is discussed using a singular value decomposition argument, to enlighten the basic regularity assumptions that are needed in the inversion, and the class of shapes that it is possible to recover from the data. The network design is given, along with the information about training and testing sets and learning rate. Simulated experimental results are reported.<<ETX>>

Inversion Of Tactile Data Through A Skin-like Sensor Sensitive To Stress Components

In this paper we briefly describe techniques and methodologies we are developing to investigate tactile fineform discrimination through machine perception. A tactile sensor array sensitive to stress-tensor components, based on piezoelectric polymer technology, is described and regularized solutions of inverse elastic contact problems, through discrete inverse operators, are reported on a limited set of significant contact primitives. Simulated reconstruction of the shape

Slip detection by a tactile neural network

Detection of incipient slippage is of great importance in robotics for the control of grasping and manipulation tasks. Together with fine-form reconstruction and primitive recognition, it has to be the main feature of an artificial tactile system. The system presented here is based on a neural network devoted to detecting incipient slippage of a body pressing on a skin-like sensor. Normal and shear stress components inside the sensor are the input data. An important feature of the system is that the a priori knowledge of the friction coefficient between the sensor and the object being manipulated is not needed. The finite element method is used to solve the direct problem of elastic contact in its full non-linearity by resorting to the lowest number of approximations with respect to the real problem. Simulations show that the network learns and is robust with respect to noise.<<ETX>>

A System For Testing A Multielement Tactile Sensor Array

This paper describes the hardware of a system built for testing a piezoelectric tactile sensor array, which has been conceived and realized for recognizing the contact stress components of an object loading its surface. The system is capable of generating a controlled contact on the sensor surface of known load and geometry and measuring the electric responses of the array, by which the six independent components of the stress tensor field are computed. By means of this system a great variety of stress shapes can be tested and the generated space-time dicretized stress field on the sensor can be measured, acquired and stored for each contact applied. In the world of engineering, artificial tactile perception, that can be obtained during object exploration and manipulation by means of miniaturized sensors, is becoming increasingly important in robotics, telemanipulation and advanced prosthetics and orthotics. In the field of artificial taction, a new tactile sensor array has been conceived and realized at the University of Pisa (Italy) [l]. One of the purposes of this research program is to face the problem of fine-form discrimination of an object in non conformal contact with the sensor surface. This problem implies to solve, analytically or numerically, the inverse elastic problem in order to reconstruct the geometry of the touching object from the spatially discretized stress tensor field [2]. Thus, a very precise apparatus is needed to experimentally test the sensor, to acquire and to record data for the comparison and the validation of theorethical solutions of the inverse elastic problem. Moreover, since the sensor has been realized as an array of sensing elements, the single element signals have to be recorded by a controlled acquisition chain, which takes into account the gain and the time constant of each detected response. The multielement tactile sensor is based on the piezoelectric polymer technology, which allows to implement arrays selectively sensitive to stress-tensor components. The six independent components of the tensor field in each point are computed from a linear combination of the measured electric responses of six miniaturized elements of the sensor, each of them sensitive to a particular direction of the stress. On the sensor surface 7 small zones have been realized, each one containing the six elements, in order to reconstruct a 7 point

Shape estimation with tactile sensors: a radial basis functions approach

Fine-form detection and discrimination of an object in contact with a skin-like tactile sensor is a basic feature in machine taction for perceptual, grasping and manipulation tasks. The inversion of tactile data in the form of normal and shear stress components in order to recover the contact shape and contact radius in the class of axisymmetry indenters gives rise to a nonlinear inverse problem which must be conceptually solved by using regularization techniques. Radial basis functions networks are used to solve this problem owing to their direct connection with regularization and approximate theory. Simulation results show the effectiveness of the proposed approach even with large noise added to the data.<<ETX>>