【正文】 the case of advanced robotic systems(manipulating robots having redundant degrees of freedom or mobile robots having redundant sensory systems would fall in this category).These systems require various kinds of sensors for responding intelligently to a dynamic may be equipped with external sensors such as forcetorque sensors,range sensors,proximity sensors,ultrasonic and infrared sensors,tactile arrays and other touch sensors,overhead or eyeinhand vision sensors,crossfire,overload and slip sensing devices addition,there are also various internal state sensors such as encoders,tachometers,revolvers and is the number of sensors,more is the putational plexity for controlling the system and more is its intelligence recent industrial as well as nonindustrial applications need robotic systems with high level of intelligence,the plexity associated with it has to be addressed this purpose,systems equipped with multiple sensors having different ranges of uncertainties has been taken up here for study. Information obtained from different sensors are inherently uncertain,imprecise and it may also be inplete or partial,spurious or incorrect and at times,it is often geographically or geometrically inpatible amongst the different sensor knowledge of the spatial relationships among objects is also inherently the example of a manmade may not match its geometric model exactly because of manufacturing tolerance,human/machine errors and other natural if it does(in macro level),a sensor cannot measure the geometric features and locate the object exactly because of measurement if it can(within certain bounded tolerance limit),a robot using the sensor may not manipulate the object exactly as intended,may be because of all cumulative errors added with the endeffector positioning errors can be reduced to a very significant level for some tasks,by reengineering the solution,structuring the working environment and using specially suited high precision equipmentbut at great cost of time and equipment[20].An alternative solution may be to develop sensor fusion strategies that can minimize and eliminate the uncertainties of any engineering system to a desired level,at a much lesser cost,incorporating all inherent this paper we focus on developing a FDDFFAANN based hybrid type sensor fusion strategy. The anization of the paper has been arranged as 2 outlines the putational steps through which the overall fusion algorithm has been formulated and developments and propositions have been applied in Section 3 for validating on synthetic data of an observation 4 is dedicated towards applying the developed hybrid fusion strategies for improving repeatability of a hardware robot effectiveness has been extensively studied with a specially configured RCS type experimental robot having five degrees of neural work formulation of the fusion algorithm is also ,in Section 5 the significant results and inferences have been listed. of the Fusion Algorithm Structure The fusion algorithm structure consists of the following putational steps: (i)The uncertainties in the information derived through processing of multiple noisy sensory data are represented by individual uncertainty ellipsoids. (ii)The uncertainty ellipsoids are merged in a manner so as to minimize the volume of the fused uncertainty ellipsoid by proper assignment of optimal weighting matrices. (iii)Fusion in the Differential Domain(FDD)has been developed to further reduce the uncertainty of fused information at finer resolutions through an iterative process that predicts the correction terms for all the sensory terms are then fused and applied to the fused information to increase its precision. (iv)The Fission Fusion Approach(FFA)is used to minimize uncertainties significantly for some specific sensor models where the covariance matrix of the sensory information can be“fissioned”and information from multiple measurements of the same set of sensors are available for fusion. (v)An ANN model of the manipulator has been developed for initial estimation of uncertainties(Mean Square Error)of joint sensors which could be further minimized by fusion process(FDD,FFA). The fusion methods as represented by steps(i)and(ii)give a physical or rather geometric insight for the plicated information processing as it involves the fusion of the uncertainty ellipsoids of each individual sensory a set of uncertainty ellipsoids associated