機(jī)械手畢業(yè)設(shè)計(jì)外文翻譯--最小化傳感級別不確定性聯(lián)合策略的機(jī)械手控制-機(jī)械手-資料下載頁
2025-01-19 06:11本頁面
【導(dǎo)讀】個不可缺少的環(huán)節(jié)。此環(huán)節(jié)是培養(yǎng)學(xué)生閱讀專業(yè)外文和檢驗(yàn)學(xué)生專。工作打下扎實(shí)的基礎(chǔ)。并將其中1篇的外文翻譯成中文。外文內(nèi)容是否與課題有關(guān)由指導(dǎo)教師把關(guān)，外文原文附在后面。一按照此排版格式進(jìn)行填寫，完成后打印出來。是否通順以及格式是否規(guī)范等方面去進(jìn)行評價。本文提出了一種基于廣義融合雜交分類（人工神經(jīng)網(wǎng)絡(luò)的力量，手控制的不確定性，這中方法已經(jīng)通過了嚴(yán)格的模擬仿真和試驗(yàn)。機(jī)械手，可重復(fù)性，準(zhǔn)確性，協(xié)方差矩陣，不確定性，不確定性橢球。根據(jù)感官測量數(shù)據(jù)結(jié)果顯示，參與的質(zhì)。量就不確定性剩余而言及傳播感覺信息的理想性加以調(diào)查。夠最大限度地減少不確定性的任何期望的水平。盡量減少不確定性的程度這取決于這一任務(wù)最小的具體應(yīng)用。覺傳感器，跨火，超載和支路傳感裝置等。此外，還有各種內(nèi)部國家傳感器，
【正文】 nd the state model that characterizes the sensor’s dependence on its location and internal state. Verification of Hybrid(NeuroFFAFDD) Approaches for handling Uncertainties in Improving Repeatability of Robotic Manipulator In robotic manipulations,there are a number of sources of uncertainties: (i)uncertainties associated with sensors, (ii)uncertainties associated with actuators, (iii)uncertainties associated with modeling. In the present investigation,attention is focussed towards uncertainties associated with sensors and their industrial robots execute simple repetitive tasks by playing back prerecorded or preprogrammed sequences of motions that have been previously guided or taught by a this type of performance,the robots do not need any information about its working sensors are not that important here,as manipulators have to simply move to goal points that have been “taught”point is that to which the manipulator is moved physically,the corresponding joint position sensors are determined,and the jointangle values ,in the next mand to the robot to return to the same point in space,each joint is moved to the stored precision with which a ma nipulator can return to a“taught”point is specified by the factor“repeatability of the manipulator”.An indispensable capability for most manipulator application is to provide a high speed and high precision such applications the repeatability of these manipulators need to be quantified as accurately as this,the analytical description of the spatial displacement of the robot,as a function of time is primarily ,in particular,depends on the functional relation between the joint angle variables and the position and orientation(with respect to a reference coordinate frame)of the robot arm endeffector. Future humanoid robots will have to work in a multisensor framework,the fused information needs to be represented with minimized level up to which the minimization would be significant is once again depends on specific application and the sophistication of the classifier handling the fused paper has proposed and developed a hybrid sensor fusion classifier which consists of three levels of fusion–Geometric Fusion,Fission Fusion Approach (FFA)and Fusion in the Differential Domain(FDD).These are directed towards the objective of minimizing uncertainties associated with any type of information that has been acquired through multiple sensors or sensory essence of the FFA technique is based on“Fissioning”(dimensionality reduction of)the covariance matrix and considering each dimension of information separately for fusion. The FDD processing technique uses feedback from the fusion processor( fused information)to each of the individual sensory formulation for this has been presented through a recursive iteration process that predicts correction terms for the different sensory fusing these terms in the differential domain using the same weighting matrices,the fused information is represented with minimized uncertainty in a recursive way. , DurrantWhyte,.:Inertial navigation systems for mobile robots,IEEE (3)(1995),328–342. ,.,Korde,.,and Skaar,.:Position control experiments using vision,Internat. (3)(1994),199–208. ,.,Jackson,.,and Gosine,.:An interactive robot control environment for rehabiltation applications,Robotica 11(1993),541–551. ,X.,Ghosh,.,Ning,X.,and Tzyh,.:Intelligent robotic manipulation with hybrid position/force control in an uncalibrated workspace,in: Robotics and Automation,May 1998,–1676. ,.,Hert,.,and Stork,.:Pattern Classification,2nd edn,Wiley,New York,2021. ,.:Sensor models and multisensor integration, (6)(1988),97–113. , Brandt,.:An optimal control approach to robust control of robot manipulators,in:IEEE Control Applications,September 1996,– AND ,M.,Maio,D.,and Rizzi,S.:Correction of deadreckoning errors in map building for mobile robots,IEEE (1)(2021),37–, Llinas,J.:An introduction to multisensor data fusion, 85(1)(1997),6–23. ,S.:Robot arm geometric link parameter estimation,in: the 22nd IEEE Decision and Control,December 1983. , al.:Advances in robotics:The DLR experience, (11)(1999),1064–1087. ,.:Sensor and Data Fusion Concepts and Applications,SPIE Publications,San Jose,CA,USA,1993. ,D.,Elliott,J.,and Croft,.:Sensor uncertainty management for an encapsulated logical device II:A control policy for sensor uncertainty,in:American Control Conference,June 2021,–4293.
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