Grip Force and Slip Analysis in Robotic Grasp: New Stochastic Paradigm Through Sensor Data Fusion

Abstract

Sensors, Focus on Tactile, Force and Stress Sensors 218 loss of information, yet it is the most optimal choice for sensory system design because of high reliability, compact hardware, lower cost and a user-friendly operative environment. In fact, this group of signal processing via localized decision vis--vis the field of 'Decentralized / Distributed Decision Making' has been an active area of research, wherein the realization has come out in the manner that these very problems are qualitatively different from the corresponding decision thematic with centralized information. It is, perhaps, wise to conjecture that the prohibitive factor in decentralized problems is not so much the inadequacy of the mathematical tools presently been used, rather the inherent complexity of the problems that have usually been formulated. The classical theory of optimal sensor signal processing is based on 'Decentralized Testing & Augmentation', using statistical estimation and hypothesis testing methods. The logically driven coherent unified output of the said aggregation is being used for processing allied control system signals of the robotic /gripper system. Unlike most of the decentralized control problems, the hypothesis-testing problem can be solved in a relatively straightforward way. This is due principally to the fact that since the decisions made do not get looped back into the system dynamics, those do not affect the information of other decision makers either. However, even in the case of independent observations, several types of unusual behaviour can occur. For example, the threshold computations can yield locally optimal thresholds, which are far from the globally optimal values. The paradigm of decentralized sensor fusion has hitherto been attributed largely by Bayesian Theory, which deals quite robustly the situations involving probabilistic hypothesis testing but fails to address the cases where fuzziness is involved in the main process itself. On the contrary, Dempster-Shafer Theory tackles only those problems where system caters for fuzzy concepts. Unfortunately both of the theories are inadequate so far as the data fusion in mechatronic system is concerned. We propose a new fusion theory wherein the threshold for fusion can be suitably adapted depending upon the end-application. The proposed schemata provides insight to two aspects, namely evolution of new rule-bases towards data fusion and an optimized inference about object's presence or absence based on stochastic hypothesis testing model. This dynamic thresholding of the proposed hypothesis helps fusing the sensory data from the physical device (a multi-input heterogeneous tactile array sensor in the present case), based on the requirement of the user. Moreover, the fusion rules, do represent a unique strategy for assimilating the raw sensor data. The threshold estimation has been based on using the variable limits, exploiting the metrics of Type I error (i.e. rejecting the Alternative Hypothesis when true), as well as Type II error (i.e., accepting the Null Hypothesis when false), corresponding to three different fusion rule-bases. The aim of our work in developing a tailor-made fusion-based hypothesis is concentrated on two vital aspects, viz. it should be able to i] cater large number of sensor-cells, which are heterogeneous in nature and ii] sense the presence of tiny 'point-objects' on the gripper surface. It may be mentioned that both of these two paradigms were overlooked in the researches hitherto and thus, the existing fusion cum hypothesis testing models are unsuitable to real-life applications in robotics. In the contrary, our model of data fusion and statistical hypothesis testing with new threshold thematic will ensure reliable measure towards overall qunatization (e.g. overall external shape, surface area and approximate contour) of the object(s) present in the vicinity of the gripper. In our model, hypotheses are postulated corresponding to different types