人工智能及機器人課題檢索.doc

.文獻檢索課程姓名 _班級 _學(xué)號_檢索課題（中文） _ （英文） _ 人工智能及機器人課題檢索1：檢索途徑：中文數(shù)據(jù)庫清華數(shù)據(jù)庫主題詞“人工智能及機器人” 淺談自動化技術(shù)與人工智能的發(fā)展對機器人研究的影響電子制作,Practical Electronics,編輯部郵箱,2013年05期給本刊投稿【作者】王曉言；【機構(gòu)】濟南中油西瓦克控制設(shè)備有限公司；【摘要】機器人一詞由來已久,但時至今日國際上還未有一個完善的定義被廣泛認(rèn)可。盡管如此,隨著自動化技術(shù)的產(chǎn)生和高速發(fā)展,特別是人工智能的產(chǎn)生和發(fā)展.機器人技術(shù)有了長足的進步,人們在不斷探索中發(fā)明和使用不同類型的機器人為人類服務(wù)?！娟P(guān)鍵詞】自動化技術(shù)；人工智能；機器人；【所屬期刊欄目】 科學(xué)論壇 （2013年05期）淺談我國工業(yè)機器人技術(shù)現(xiàn)狀與產(chǎn)業(yè)化發(fā)展西部資源,Westem Resources,編輯部郵箱,2015年06期給本刊投稿【作者】杜欣悅；【機構(gòu)】湖南省長沙市實驗中學(xué)；【摘要】工業(yè)機器人是一種集計算機科學(xué)技術(shù)、電子工程技術(shù)、機械工程技術(shù)、信息傳感器技術(shù)、人工智能學(xué)、控制理論等學(xué)科于一體的高新技術(shù),工業(yè)機器人技術(shù)是目前各個國家研究的重點技術(shù),本文主要針對我國工業(yè)機器人技術(shù)現(xiàn)狀與產(chǎn)業(yè)化發(fā)展進行分析。【關(guān)鍵詞】我國工業(yè)機器人；技術(shù)現(xiàn)狀；產(chǎn)業(yè)化發(fā)展；2: 檢索途徑：中文數(shù)據(jù)庫萬方數(shù)據(jù)庫主題詞“人工智能及機器人”淺談人工智能在機械電子工程中的應(yīng)用0推薦查看全文下載全文導(dǎo)出添加到引用通知分享到|下載PDF閱讀器doi：10.3969/j.issn.2095-6630.2015.33.565摘要：改革開放以來,機械電子工程得到了快速發(fā)展,在生產(chǎn)生活中的地位也不斷提高.然而,在發(fā)揮著巨大作用的同時,對于科學(xué)技術(shù)水平的提高也迫在眉睫.通過人工智能在機械電子工程中的應(yīng)用,能夠為機械電子工程的未來發(fā)展提供更多更好的機遇.本文通過對人工智能和機械電子工程的分析,從而探討人工智能在機械電子工程中的應(yīng)用前景.作者：湯敏作者單位：江蘇和興建筑裝飾工程有限公司刊 名：建筑工程技術(shù)與設(shè)計Journal：Architectural Engineering Technology and Besign年，卷(期)：2015,(33)關(guān)鍵詞：人工智能機械電子工程機器人在線出版日期：2015年12月30日3：檢索途徑：中文數(shù)據(jù)庫維普數(shù)據(jù)庫主題詞“人工智能及機器人”面向作業(yè)與人工智能的仿人機器人研究進展Research progress of humanoid robots for mobile operation and artificial intelligence加關(guān)注用中文期刊手機助手掃描關(guān)注窗體頂端窗體底端作者：吳偉國1WU Weiguo （School of Mechatronics Engineering, Harbin Institute of Technology, 150001 Harbin, China）機構(gòu)地區(qū)：1哈爾濱工業(yè)大學(xué)機電工程學(xué)院,哈爾濱150001出處：哈爾濱工業(yè)大學(xué)學(xué)報2015年第7期1-19,共19頁Journal of Harbin Institute of Technology基金：國家高技術(shù)研究發(fā)展計劃（863-2006AA04Z201）摘要：以人類自身為原型參照的仿人全身機器人是國際機器人研究領(lǐng)域最具代表性的研究對象,也是機器人學(xué)、機器人技術(shù)以及人工智能的終極研究目標(biāo).針對國際上目前在仿人全身機器人、仿人頭部、多指手、仿人雙足步行機等方面理論與關(guān)鍵技術(shù)進行了全面回顧、綜述與分析,包括作者在仿人機器人方面相關(guān)研究工作的回顧;歸納總結(jié)了目前仿人全身機器人技術(shù)程度,從驅(qū)動元部件、步行穩(wěn)定性、本體集成化設(shè)計、操作能力以及環(huán)境適應(yīng)性等角度分別提出了目前存在的主要技術(shù)問題以及今后發(fā)展的趨勢;為從事與仿人機器人及其各組成部分相關(guān)研究的科研人員以及相關(guān)科技管理部門提供參考.Whole-body humanoid robot,which regards human beings as the reference prototype,is the most representative research object and the ultimate goal of robotics,robot technology and artificial intelligence.Comprehensive review,summary and analysis of current theories and key technologies in whole-body humanoid robot,humanoid head,multi-finger hand,humanoid biped walking machine and so on are given,including review of the relevant works on humanoid robots by the author. By summarizing the current technical level of humanoid robots development,the main technical problems and the trend of the future development are respectively discussed in the aspects of driving elements,walking stability,integrated designation and operation ability of the mechanical systems,environment adaptability and so on. References of decision making are provided for researchers and managers engaged in the relevant works of humanoid robots and their components.關(guān) 鍵 詞：仿人機器人雙足步行表情機器人多指手多移動方式分 類 號：TP242.6自動化與計算機技術(shù)控制科學(xué)與工程;自動化與計算機技術(shù)檢測技術(shù)與自動化裝置機器人產(chǎn)業(yè)大軍全面出擊人工智能前景廣闊加關(guān)注窗體頂端窗體底端出處：機床與液壓2015年第8期81-81,共1頁Machine Tool Hydraulics摘要：4月22日召開的人工智能與服務(wù)型機器人論壇上，來自北京市科委、中國機器人產(chǎn)業(yè)聯(lián)盟的相關(guān)領(lǐng)導(dǎo)透露，我國將加快推動發(fā)展人工智能，加快國產(chǎn)機器人產(chǎn)業(yè)的發(fā)展，提升競爭力擴大市場份額。關(guān) 鍵 詞：機器人產(chǎn)業(yè)人工智能工業(yè)機器人自動化分 類 號：TP242自動化與計算機技術(shù)控制科學(xué)與工程;自動化與計算機技術(shù)檢測技術(shù)與自動化裝置TP18自動化與計算機技術(shù)控制科學(xué)與工程;自動化與計算機技術(shù)控制理論與控制工程4：檢索途徑：外文數(shù)據(jù)庫萬方外文文獻數(shù)據(jù)庫主題詞“artificial intelligence”Advancing artificial intelligence research and dissemination through conference series: Benchmark, scientific impact and the MICAI experience0推薦分享到|作者：Francisco Javier Cantu-Ortiz期刊：Expert Systems with ApplicationEISCI年，卷(期)：2014, 41(3)關(guān)鍵詞：Artificial intelligenceAl research and developmentScientific impact of Al正文語種：eng5檢索途徑：外文數(shù)據(jù)庫ACS數(shù)據(jù)庫TITLE“artificial intelligence”Electron-transfer reactions in proteins: anartificialintelligenceapproach to electronic couplingPrabha Siddarth,R. A. MarcusJ. Phys. Chem.,1993,97(10), pp 24002405DOI:10.1021/j100112a047Publication Date: March 1993Note:In lieu of an abstract, this is the articles first page.Click to increase image size6:檢索途徑：外文數(shù)據(jù)庫springer數(shù)據(jù)庫TITLE“artificial intelligence”Annals of Mathematics and Artificial IntelligenceApril 2008,Volume 52,Issue2,pp 107-108First online:04 March 2009Editorial: Annals of Mathematics and Artificial Intelligence special issue on multi-robot coverage, search, and exploration GalA.Kaminka ,AmirShapiroTitleEditorial: Annals of Mathematics and Artificial Intelligence special issue on multi-robot coverage, search, and explorationJournalAnnals of Mathematics and Artificial IntelligenceVolume 52, Issue 2-4 , pp 107-108Cover Date2008-04DOI10.1007/s10472-008-9105-6Print ISSN1012-2443Online ISSN1573-7470PublisherSpringer NetherlandsAdditional Links Register for Journal Updates Editorial Board About This Journal Manuscript SubmissionTopics Complexity Mathematics, general Computer Science, general Artificial Intelligence (incl. Robotics)Industry Sectors Pharma Biotechnology Electronics IT & Software Telecommunications AerospaceAuthors Gal A. Kaminka(1) Amir Shapiro(2)Author Affiliations 1.Computer Science Department, Bar Ilan University, Ramat Gan, 52900, Israel 2.Mechanical Engineering Department, Ben Gurion University of the Negev, P.O. Box 653, Beer Sheva, 84105, Israel7：檢索途徑：外文數(shù)據(jù)庫IEEE網(wǎng)絡(luò)電子版期刊TITLE“artificial intelligence”A Simplified Cerebellar Model with Priority-based Delayed Eligibility Trace Learning for Motor Control5Author(s)Vui Ann Shim;Institute for Infocomm Research, A*STAR, Singapore ;Chris Stephen Naveen Ranjit;Bo Tian;Miaolong Yuanmore authorsThe study of cerebellum has resulted in a common agreement that it is implicated in motor learning for movement coordination. Learning governed by error signal through synaptic eligibility traces has been proposed to be a learning mechanism in cerebellum. In this paper, we extend this idea and suggest a simplified and improved cerebellar model with priority-based delayed eligibility trace learning rule (S-CDE) that enables a mobilerobotto freely and smoothly navigate in an environment. S-CDE is constructed in a brain-based device which mimics the anatomy, physiology, and dynamics of cerebellum. The input signal in terms of depth information generated from a simulated laser sensor is encoded as neuronal region activity for velocity and turn rate control. A priority-based delayed eligibility trace learning rule is proposed to maximize the usage of input signals for learning in synapses on Purkinje cell and cells in the deep cerebellar nuclei of cerebellum. Error signal generation and input signal conversion algorithms for turn rate and velocity are designed to facilitate training in an environment containing turns of varying curvatures. S-CDE is tested on a simulated mobilerobotwhich had to randomly navigate maps of Singapore and Hong Kong expressways.Published in:IEEE Transactions on Autonomous Mental Development(Volume:7 ,Issue: 1)Page(s):26 - 38ISSN :1943-0604INSPEC Accession Number:14982520DOI:10.1109/TAMD.2014.2377093Date of Publication :23 十二月 2014Date of Current Version :11 三月 2015Issue Date :March 2015Sponsored by :IEEE Computational Intelligence SocietyPublisher:IEEEMultistep Prediction of Physiological Tremor Based on Machine Learning for Robotics Assisted Microsurgery3Author(s)Sivanagaraja Tatinati;School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, Korea ;Kalyana C. Veluvolu;Wei Tech AngFor effective tremor compensation in robotics assisted hand-held device, accurate filtering of tremulous motion is necessary. The time-varying unknown phase delay that arises due to both software (filtering) and hardware (sensors) in these robotics instruments adversely affects the device performance. In this paper, moving window-based least squares support vector machines approach is formulated for multistep prediction