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F.: Comparison of a fuzzy logic controller with aP +D control law,ASME Dynamic Sys. Meas. Control 111 (1989), 128–137.\nDote, Y.: Stability analysis of variable-structured PI controller by fuzzy logic for servo system, inProc. IEEE Conf. Decision and Control, Brighton, England, December 1991, pp. 1217–1218.\nKiska, J. B., Gupta, M. M., and Nikiforuk, P. N.: Energistic stability of fuzzy dynamic systems,IEEE Trans. Systems Cybernet. 4(5) (1985), 783–792.\nKang, H. and Vachtsevanos, G.: Model reference fuzzy control, inProc. IEEE Conf. Decision and Control, Tampa, FL, December 1989, pp. 751–756.\nLee, C. C.: Fuzzy logic in control Systems: fuzzy logic controller, Part I,IEEE Trans. Systems Man Cybernet. 20(2) (1990), 404–418.\nLee, C. C.: Fuzzy logic in control systems: fuzzy logic controller, Part II,IEEE Trans. Systems Man Cybernet. 20(2) (1990), 419–435.\nMamdani, E. H.: Application of fuzzy algorithms for simple dynamic plant,Proc. IEE, Part D121(12) (1974), 1585–1588.\nParaskevopoulos, P. N.: On the pole assignment by proportional plus derivative output feedback,Electronic Lett. 14 (1976), 34–35.\nPedrycz, W.: An approach to the analysis of fuzzy systems,Internat. J. Control 34(3) (1981), 403–421.\nRutherford, D. A. and Bloore, G. C.: The implementation of fuzzy algorithms for control,Proc. IEEE 64(4) (1976), 572–573.\nRay, K. S. and Majumber, D. D.: Application of stability criteria for stability analysis of linear SISO and MIMO systems associated with fuzzy logic controller,IEEE Trans. Systems Man Cybernet. 3(2) (1984), 345–349.\nShynk, J. J.: Adaptive IIR filtering,IEEE ASSP Magazine 6(2) (1989), 4–21.\nTang, K. L. and Mulholland, R. J.: Comparing fuzzy logic with classical controller designs,IEEE Trans. Systems Man Cybernet. 17(6) (1987), 1085–1087.\nTakagi, T. and Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control,IEEE Trans. Systems Man Cybernet. 15(1) (1985), 116–132.\nTzes, A. and Yurkovich, S.: A frequency domain identification scheme for flexible structure control,Trans. ASME J. Dyn. Meas. Control 112 (1990), 427–434.\nTzes, A. and Yurkovich, S.: An adaptive input shaping control scheme for vibration supression in slewing flexible structures,IEEE Trans. Control Systems Technol. 1 (June 1993).\nTzes, A.:Self-tuning controllers for flexible link manipulators, PhD thesis, The Ohio State University, 1990.\nWang, B. H. and Vachtsevanos, G.: Fuzzy logic control: A systematic design methodology, inProc. IEEE Conf. Decision and Control, Brighton, England, December 1991, pp. 1219–1220.\nZadeh, L. A.: Fuzzy sets as a basis for a theory of possibility,Fuzzy Sets Systems 1(1) (1978), 3–28.",{"EN":261},"This paper addresses the implementation of an adaptive fuzzy controller for flexible link robot arms. The design technique is a hybrid scheme involving both frequency and time domain techniques. The eigenvalues of the open loop plant can be estimated through application of a frequency domain based identification algorithm. The region of the eigenvalue space, within which the system operates, is partitioned into fuzzy cells. Membership function are assigned to the fuzzy sets of the eigenvalue universe of discourse. The degree of uncertainty on the estimated eigenvalues is encountered through these membership functions. The knowledge data base consists of feedback gains required to place the closed loop poles at predefined locations. A rule based controller infers the control input variable weighting each with the value of the membership functions at the identified eigenvalue. The afore-mentioned controller is compared through simulation with conventional techniques, namely pole placement and gain scheduling.",{"EN":263},"A hybrid frequency—time domain adaptive fuzzy control scheme for flexible link manipulators",{"VOID":265},"10.1007\u002FBF01258262","PUBLICATION","VERIFIED","Auto Verify",2,"http:\u002F\u002Flink.springer.com\u002F10.1007\u002FBF01258262",[272,288],{"id":273,"sortIndex":21,"researcher":20,"roles":274,"affiliations":276,"properties":285},"56888d5f-89a1-4478-aedb-5c6e62957ce6",[275],"AUTHOR",[277],{"id":20,"sortIndex":21,"affiliation":278,"properties":20},{"id":279,"createTime":280,"updateTime":280,"relativeEntities":281,"slug":20,"properties":282,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"1cd7fc45-56f8-4821-a03f-94c161bce053","2023-12-07T14:57:25.279+00:00",[],{"title":283},{"VI":284},"Department of Mechanical Engineering, Polytechnic University, Brooklyn, USA",{"title":286},{"VI":287},"Anthony Tzes",{"id":289,"sortIndex":149,"researcher":20,"roles":290,"affiliations":291,"properties":297},"e1e40ed1-8ed1-4f99-9b5a-0b438a219ee9",[275],[292],{"id":20,"sortIndex":21,"affiliation":293,"properties":20},{"id":279,"createTime":280,"updateTime":280,"relativeEntities":294,"slug":20,"properties":295,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":296},{"VI":284},{"title":298},{"VI":299},"Kyriakos Kyriakides","ARTICLE",{"url":270,"publisher":302,"properties":330},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":303,"slug":10,"properties":304,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":308,"manageAffiliations":309,"indexDatabases":310,"url":139,"thumbnailPath":20,"statistic":325,"gsStatistic":20,"type":246,"analyzePriority":20},[],{"issn":305,"eissn":306,"title":307},{"VOID":13},{"VOID":15},{"EN":17},[],[],[311,318],{"id":96,"indexDatabase":312,"url":111,"indexYears":20,"academicFieldIds":317,"indexDatabaseRanking":20},{"id":98,"createTime":99,"updateTime":100,"relativeEntities":313,"label":314,"description":315,"key":107,"publicationTags":316,"standard":20},[],{"EN":103,"VI":103},{"VI":105,"EN":106},[109,110],[113,114],{"id":116,"indexDatabase":319,"url":129,"indexYears":130,"academicFieldIds":324,"indexDatabaseRanking":138},{"id":118,"createTime":119,"updateTime":120,"relativeEntities":320,"label":321,"description":322,"key":126,"publicationTags":323,"standard":20},[],{"EN":123,"VI":123},{"EN":123,"VI":125},[128],[132,133,134,135,136,137],{"impactFactor":21,"impactFactorByYear":326,"i10Index":152,"i10IndexLast5Year":153,"totalPublication":154,"totalPublicationByYear":327,"totalCitation":187,"totalCitationByYear":328,"totalCitationPerPublication":216,"totalCitationPerPublicationByYear":329,"hindexLast5Year":190,"hindex":190},{"2012":142,"2013":143,"2014":144,"2015":144,"2016":145,"2017":146,"2018":147,"2019":146,"2020":148,"2021":149,"2022":150,"2023":151},{"1988":156,"1989":157,"1990":158,"1991":159,"1992":160,"1993":160,"1994":161,"1995":162,"1996":161,"1997":163,"1998":164,"1999":165,"2000":166,"2001":167,"2002":168,"2003":169,"2004":170,"2005":171,"2006":172,"2007":173,"2008":172,"2009":174,"2010":175,"2011":176,"2012":177,"2013":178,"2014":179,"2015":180,"2016":175,"2017":181,"2018":182,"2019":180,"2020":178,"2021":183,"2022":184,"2023":185,"2024":186},{"1988":189,"1989":190,"1990":191,"1991":192,"1992":193,"1993":194,"1994":170,"1995":166,"1996":195,"2004":196,"2005":197,"2006":198,"2007":199,"2008":200,"2009":201,"2010":202,"2011":203,"2012":204,"2013":205,"2014":206,"2015":207,"2016":208,"2017":209,"2018":210,"2019":211,"2020":212,"2021":213,"2022":214,"2023":215},{"1988":218,"1989":219,"1990":220,"1991":221,"1992":222,"1993":223,"1994":224,"1995":225,"1996":151,"2004":226,"2005":227,"2006":228,"2007":229,"2008":230,"2009":231,"2010":232,"2011":233,"2012":234,"2013":235,"2014":236,"2015":237,"2016":238,"2017":239,"2018":240,"2019":241,"2020":242,"2021":243,"2022":244,"2023":245},{"volume":331,"pages":333},{"VOID":332},"10",{"VOID":334},"283-300","1994-07-01",1994,false,{"id":339,"createTime":340,"updateTime":340,"relativeEntities":341,"slug":20,"properties":342,"entityType":266,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":351,"fullTextUrl":20,"authors":352,"publicationType":300,"publisherRelationship":392,"citationCount":20,"citationInfo":20,"publishDate":426,"publishYear":427,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":337},"2342837f-1d4c-4592-bd9e-9335db2c7e06","2024-02-06T23:59:03.579+00:00",[],{"references":343,"abstract":345,"title":347,"doi":349},{"VOID":344},"Asada, H. and Slotine, J. J., 1986,Robot Analysis and Control, Wiley, New York.\nFunahashi, K.-I., 1989, On the approximate realization of continuous mappings by neural networks,Neural Networks 2 183–192.\nHornik, K., Stinchombe, M.et al., 1989, Multilayer feedforward networks are universal approximators,Neural Networks 2 359–366.\nJosin, G., 1990, Neural network computer transforms coordinates,NASA Tech. Briefs 14(7) (July).\nKawato, M., Uno, Y.et al., 1988, Hierarchical neural network model for voluntary movement with application to robotics,IEEE Control Systems Magazine 8 (April), 8–15.\nMiyamoto, H., Kawato, M.et al., 1988, Feedback-error-learning neural network for trajectory control of a robotic manipulator,Neural Networks 1 251–265.\nNarendra, K. S. and Parthasarathy, K., 1991, Gradient methods for the optimization of dynamical systems containing neural networks,IEEE Trans. Neural Networks 2 252–262.\nNarendra, K. S. and Parthasarathy, K., 1990, Identification and control of dynamical systems using neural networks,IEEE Trans. Neural Networks 1 4–27.\nRumelhart, D. E., Hinton, G. E. and Williams, R. J., 1986, Learning internal representations by error propagation, inParallel Distributed Processing, vol 1, MA: MIT Press, Cambridge, Mass, pp. 318–362.",{"EN":346},"The use of artificial neural networks is investigated for application to trajectory control problems in robotics. The relative merits of position versus velocity control is considered and a control scheme is proposed in which neural networks are used as static maps (trained off-line) to compute the inverse of the manipulator Jacobian matrix. A proof of the stability of this approach is offered, assuming bounded errors in the static map. A representative two-link robot is investigated using an artificial neural network which has been trained to compute the components of the inverse of the Jacobian matrix. The controller is implemented in the laboratory and its performance compared to a similar controller with the analytical inverse Jacobian matrix.",{"EN":348},"Applications of neural networks for coordinate transformations in robotics",{"VOID":350},"10.1007\u002FBF01257949","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF01257949",[353,368,380],{"id":354,"sortIndex":269,"researcher":20,"roles":355,"affiliations":356,"properties":365},"9c8029f5-170d-45e9-8a37-e717e3700210",[275],[357],{"id":20,"sortIndex":21,"affiliation":358,"properties":20},{"id":359,"createTime":360,"updateTime":360,"relativeEntities":361,"slug":20,"properties":362,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"8a7b0479-4f60-4998-b808-ca505ee5d57c","2023-12-09T06:57:15.094+00:00",[],{"title":363},{"VI":364},"Department of Mechanical Engineering, The Pennsylvania State University, University Park, USA",{"title":366},{"VI":367},"G. 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IEEE Int. Conf. Robotics Automat.(1986), pp. 1180–1185.\nBurdick, J. W.: A classification of 3R regional manipulator singularities and geometries, in: Proc. IEEE Int. Conf. Robotics Automat.(1991), pp. 2670–2675.\nBurdick, J. W.: A recursive method for finding revolute-jointed manipulator singularities, in: Proc. IEEE Int. Conf. Robotics Automat.(1992), pp. 448–453.\nShiller, Z.: Interactive time optimal robot motion planning and work-cell layout design, in: Proc. IEEE Int. Conf. Robotics Automat.(1989), pp. 964–969.\nWenger, Ph.: A new general formalism for the kinematic analysis of all nonredundant manipulators, in: Proc. IEEE Int. Conf. Robotics Automat.(1992), pp. 442–447.\nWenger, Ph. and Chedmail, P.: On the connectivity of manipulator free workspace, Journal of Robotic Systems 8(6) (1991), 767–799.\nWenger, Ph. and Chedmail, P.: Ability of a robot to travel through Cartesian free workspace in an environment with obstacles, Int. Journal of Robotics Research 10(3) (1991), 214–227.\nPamanes, G. J. A. and Zeohloul, S.: Multicriteria optimal placement of manipulators in cluttered environments, ASME Int. Conf. on Comput. in Eng., Vol. 2 (1991), pp. 419–424.\nClourier, G. et al.: Robotization of predetermined environments: Solution to the workstation design problem, in: Int. Conf. on CAD\u002FCAM, Robotics and Factories of the Future(1992), pp. 1411–1425.\nLueth, T. C.: Automatic planning of robot workcell layouts, in: Proc. IEEE Int. Conf. Robotics Automat.(1992), pp. 1103–1108.\nNelson, B., Perdersen, K., and Donath, M.: Locating assembly tasks in a manipulator’s workspace, in: Proc. IEEE Int. Conf. Robotics Automat.(1987), Raleigh, North Carolina, pp. 1367–1372.\nChiu: Task compatibility of manipulator postures, Int. Journal of Robotics Research 7(5) (1988), 13–21.\nDeneb Robotics, IGRIP Version 2.3 User Manual (1993), Pittsburg.\nChedmail, P. and Wenger, Ph.: Design and positioning of a robot in an environment with obstacles using optimal search, in: Proc. IEEE Int. Conf. Robotics Automat.(1989), Scottsdale, pp. 1069–1076.\nReynier, F., Chedmail, P., and Wenger, Ph.: Automatic positioning of robots, continuous trajectories feasibility among obstacles, in: Proc. IEEE Int. Conf. Syst. Man and Cybern.(1992), Chicago, pp. 189–194.\nPark,. S., Yoon, J. S., and Cho, H. S.: Robot positioning based on workspace connectivity, Proc. ASME Winter Annual Meeting, DSMC, Vol. 1 (1995), pp. 121–128.\nGargantini, I.: An effective way to represent quadtrees, Computer Graphics and Image Processing 25(12) (1982), 905–910.\nBauer, M. A.: Set operations on linear quadtrees, Computer Vision, Graphics, and Image Processing 29(1985), 248–258.\nUnnikrishnan, A., Venkatesh, Y. V., and Shankar, P.: Connected component labelling using quadtrees–A buttom-up approach, The Computer Journal 30(2) (1987), 176–182.\nNelder, J. A. and Mead, R.: A simplex method for function minimization, Computer Journal 7(1965), 308–313.\nBeightler, C. S., Phillips, D. T., and Wilde, D. J.: Foundation of Optimization, Prentice-Hall, New Jersey (1979).\nBellman, R. E.: Dynamic Programming, Princeton Univ. Press, Princeton, New Jersey (1957).",{"EN":438},"A method for generating discrete optimal sequences of base locations for mobile manipulators is presented that considers the task capability of the workspace in a cluttered environment. In implementation, the obstacles and task trajectories are represented by 2n trees, so that a series of set operations are performed to characterize the manipulator’s configuration space into topological subspaces. By incorporating trajectory-motion-capable subspaces into the enumeration of the cost function, an optimum search technique is made applicable to the determination of a task feasible location. The method is then extended to a multiple positioning problem by concatenating the single optimization processes into a serial multistage decision making system, for which an optimal set of decisions can be found through a computationally efficient dynamic programming process.The computational paradigm of the present method is coherent with topological workspace analysis, and thus applicable to task trajectories of arbitrary dimensions and shapes. The effectiveness of the presented method is demonstrated through simulation studies performed for a 3-d.o.f. regional manipulator operating under various task conditions.",{"EN":440},"Task Oriented Optimum Positioning of a Mobile Manipulator Base in a Cluttered Environment",{"VOID":442},"10.1023\u002FA:1007982206837","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1007982206837",[445,460],{"id":446,"sortIndex":149,"researcher":20,"roles":447,"affiliations":448,"properties":457},"004c4205-31ce-4b72-ada4-463437ce0617",[275],[449],{"id":20,"sortIndex":21,"affiliation":450,"properties":20},{"id":451,"createTime":452,"updateTime":452,"relativeEntities":453,"slug":20,"properties":454,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"a01be3b5-1c5f-4aba-b16d-da95b57a6582","2024-01-18T03:40:00.948+00:00",[],{"title":455},{"VI":456},"Dept. of Mechanical Engineering, Korea Advance Institute of Science and Technology, Yusung-gu, Taejon, Korea",{"title":458},{"VI":459},"Hyung Suck Cho",{"id":461,"sortIndex":21,"researcher":20,"roles":462,"affiliations":463,"properties":472},"d27311a8-7664-435c-9553-3bec8b3b2c47",[275],[464],{"id":20,"sortIndex":21,"affiliation":465,"properties":20},{"id":466,"createTime":467,"updateTime":467,"relativeEntities":468,"slug":20,"properties":469,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"84041333-83e0-4875-96da-7cbacfc2b156","2024-01-18T03:40:00.837+00:00",[],{"title":470},{"VI":471},"Nuclear Environment Management Center, Korea Atomic Energy Research Institute, Yusung-gu, Taejon, Korea",{"title":473},{"VI":474},"Young Soo Park",{"url":443,"publisher":476,"properties":504},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":477,"slug":10,"properties":478,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":482,"manageAffiliations":483,"indexDatabases":484,"url":139,"thumbnailPath":20,"statistic":499,"gsStatistic":20,"type":246,"analyzePriority":20},[],{"issn":479,"eissn":480,"title":481},{"VOID":13},{"VOID":15},{"EN":17},[],[],[485,492],{"id":96,"indexDatabase":486,"url":111,"indexYears":20,"academicFieldIds":491,"indexDatabaseRanking":20},{"id":98,"createTime":99,"updateTime":100,"relativeEntities":487,"label":488,"description":489,"key":107,"publicationTags":490,"standard":20},[],{"EN":103,"VI":103},{"VI":105,"EN":106},[109,110],[113,114],{"id":116,"indexDatabase":493,"url":129,"indexYears":130,"academicFieldIds":498,"indexDatabaseRanking":138},{"id":118,"createTime":119,"updateTime":120,"relativeEntities":494,"label":495,"description":496,"key":126,"publicationTags":497,"standard":20},[],{"EN":123,"VI":123},{"EN":123,"VI":125},[128],[132,133,134,135,136,137],{"impactFactor":21,"impactFactorByYear":500,"i10Index":152,"i10IndexLast5Year":153,"totalPublication":154,"totalPublicationByYear":501,"totalCitation":187,"totalCitationByYear":502,"totalCitationPerPublication":216,"totalCitationPerPublicationByYear":503,"hindexLast5Year":190,"hindex":190},{"2012":142,"2013":143,"2014":144,"2015":144,"2016":145,"2017":146,"2018":147,"2019":146,"2020":148,"2021":149,"2022":150,"2023":151},{"1988":156,"1989":157,"1990":158,"1991":159,"1992":160,"1993":160,"1994":161,"1995":162,"1996":161,"1997":163,"1998":164,"1999":165,"2000":166,"2001":167,"2002":168,"2003":169,"2004":170,"2005":171,"2006":172,"2007":173,"2008":172,"2009":174,"2010":175,"2011":176,"2012":177,"2013":178,"2014":179,"2015":180,"2016":175,"2017":181,"2018":182,"2019":180,"2020":178,"2021":183,"2022":184,"2023":185,"2024":186},{"1988":189,"1989":190,"1990":191,"1991":192,"1992":193,"1993":194,"1994":170,"1995":166,"1996":195,"2004":196,"2005":197,"2006":198,"2007":199,"2008":200,"2009":201,"2010":202,"2011":203,"2012":204,"2013":205,"2014":206,"2015":207,"2016":208,"2017":209,"2018":210,"2019":211,"2020":212,"2021":213,"2022":214,"2023":215},{"1988":218,"1989":219,"1990":220,"1991":221,"1992":222,"1993":223,"1994":224,"1995":225,"1996":151,"2004":226,"2005":227,"2006":228,"2007":229,"2008":230,"2009":231,"2010":232,"2011":233,"2012":234,"2013":235,"2014":236,"2015":237,"2016":238,"2017":239,"2018":240,"2019":241,"2020":242,"2021":243,"2022":244,"2023":245},{"volume":505,"pages":507},{"VOID":506},"18",{"VOID":508},"147-168","1997-02-01",1997,{"id":512,"createTime":513,"updateTime":514,"relativeEntities":515,"slug":516,"properties":517,"entityType":266,"verifyStatus":267,"verifyTime":514,"verifyNote":268,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":526,"fullTextUrl":20,"authors":527,"publicationType":300,"publisherRelationship":572,"citationCount":20,"citationInfo":20,"publishDate":606,"publishYear":607,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":337},"cc45a418-0610-4ddd-86fb-21348e8c4c18","2024-01-25T08:31:38.845+00:00","2024-12-11T23:58:39.156+00:00",[],"Mixed-Fuzzy-Sliding-Mode-Tracking-with-Backstepping-Formation-Control-for-Multi-Nonholonomic-Mobile-Robots-Subject-to-Uncertainties",{"references":518,"abstract":520,"title":522,"doi":524},{"VOID":519},"Kwon, J.-W., Chwa, D.: Hierarchical formation control based on a vector field method for wheeled mobile robots. IEEE Trans. Robot 28(6), 1335–1345 (2012)\nHernandez-Martinez, E.G., Aranda-Bricaire, E.: Multi-agent formation control with collision avoidance based on discontinuous vector fields. In: Proceedings of the IEEE International Conference Industrial Electronics, pp 2283–2288 (2009)\nHsu, H.C.-H., Liu, A.: Multiagent-based multi-team formation control for mobile robots. J. of Intelligent Robotic Systems 42(4), 337–360 (2005)\nBalch, T., Arkin, R.C.: Behavior-based formation control for multirobot teams. IEEE Trans. Robot. Autom. 14(6), 926–939 (1998)\nRezaee, H., Abdollahi, F.: A decentralized cooperative control scheme with obstacle avoidance for a team of mobile robots. IEEE Trans. Ind. Electron. 61(1), 347–354 (2014)\nZhang, Q., Lapierre, L., Xiang, X.: Distributed control of coordinated path tracking for networked nonholonomic mobile vehicles. IEEE Trans. Ind. Informatics 9(1), 472–484 (2013)\nVela, P., Betser, A., Malcolm, J., Tannenbaum, A.: Vision-based range regulation of a leader-follower formation. IEEE Trans. Contr. Syst. Technol. 17(2), 442–448 (2009)\nGu, D., Yang, E.: Fuzzy policy reinforcement learning in cooperative multi-robot systems. J. of Intelligent Robotic Systems 48(1), 7–22 (2007)\nMehrjerdi, H., Saad, M., Ghommam, J.: Hierarchical fuzzy cooperative control and path following for a team of mobile robots. IEEE\u002FASME Trans. Mechatronics 16(5), 907–917 (2011)\nDierks, T., Jagannathan, S.: Neural network output feedback control of robot formation. IEEE Trans. Syst., Man and Cybern. 40(2), 383–399 (2010)\nDierks, T., Brenner, B., Jagannathan, S.: Neural network-based optimal control of mobile robot formations with reduced information exchange. IEEE Trans. Contr. Syst. Technol. 21(4), 1407–1415 (2013)\nDefoort, M., Floquet, T., Kokosy, A., Perruquetti, W.: Sliding-mode formation control for cooperative autonomous mobile robots. IEEE Trans. Ind. Electron. 55(11), 3944–3953 (2010)\nChang, Y.H., Chang, C.W., Chen, C.L., Tao, C.W.: Fuzzy sliding-mode formation control for multirobot systems: Design and implementation. IEEE Trans. Syst., Man and Cybern. 42(2), 444–457 (2012)\nMariottini, G.L., Morbidi, F., Prattichizzo, D., Vander Valk, N., Michael, N., Pappas, G., Daniilidis, K.: Vision-Based localization for leader-follower formation control. IEEE Trans. Robot. 25(6), 1431–1438 (2009)\nRanjbar-Sahraei, B., Shabaninia, F., Nemati, A., Stan, S.: A novel robust decentralized adaptive fuzzy control for swarm formation of multiagent. IEEE Trans. Ind. Electron. 59(8), 3124–3134 (2012)\nFierro, R., Lewis, F.L.: Control of a nonholonomic mobile robot using neural networks. IEEE Trans. Neural Networks 9(4), 389–400 (1998)\nDierks, T., Jagannathan, S.: Neural network control of mobile robot formations using rise feedback. IEEE Trans. Syst., Man and Cybern. 39(2), 332–347 (2009)\nEgerstedt, M., Hu, X., Stotsky, A.: Control of mobile platforms using a virtual vehicle approach. IEEE Trans. Automat. Contr. 46(11), 1777–1782 (2010)\nKanayama, Y., Kimura, Y., Miyazaki, F., Noguchi, T.: A stable tracking control method for an autonomous mobile robot. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp 384–389 (1990)\nKhalil, H.K.: Nonlinear Systems, 2nd edn. Prentice-Hall, Upper Saddle River, NJ 07458 (1996)\nHwang, C.L., Chang, L.J., Yu, Y.S.: Network-based fuzzy decentralized sliding-mode control for car-like mobile robots. IEEE Trans. Ind. Electron. 54(1), 574–585 (2007)",{"EN":521},"This paper aims at attaining one-leader & two-followers (1L-2F) formation control of multi-nonholonomic mobile robot (multi-NMR) systems subject to uncertainties and, at the same time, achieves trajectory-tracking of the leader NMR. To begin, the tracking error between the leader and a virtual reference robot is defined. Then, the extension to a leader-follower formation control structure is utilized to define the formation error (i.e., separation and orientation errors) between the leader and the followers. It has been proven that fuzzy sliding-mode tracking control (FSMTC) and backstepping formation control (BFC) can improve performance and stability when the overall closed-loop system is subject to uncertainties. Therefore, FSMTC and BFC are used for trajectory tracking of the leader NMR and formation control for two followers with respect to the leader, respectively. The stability of the closed-loop multi-NMR systems, i.e., trajectory tracking and formation control, is demonstrated through Lyapunov stability criteria. Finally, to validate the theoretical developments, computer simulations are conducted which prove the effectiveness, efficiency and robustness of the proposed scheme.",{"EN":523},"Mixed Fuzzy Sliding-Mode Tracking with Backstepping Formation Control for Multi-Nonholonomic Mobile Robots Subject to Uncertainties",{"VOID":525},"10.1007\u002Fs10846-014-0131-9","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10846-014-0131-9",[528,543,555],{"id":529,"sortIndex":21,"researcher":20,"roles":530,"affiliations":531,"properties":540},"a37432de-ab4c-42ed-bcf0-932d36d0888c",[275],[532],{"id":20,"sortIndex":21,"affiliation":533,"properties":20},{"id":534,"createTime":535,"updateTime":535,"relativeEntities":536,"slug":20,"properties":537,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"a8035a2b-346b-4bb4-9d0b-c9d4192c67ec","2024-01-14T15:57:18.451+00:00",[],{"title":538},{"VI":539},"Department of Mechanical Engineering, Texas A&M University at Qatar, Doha, Qatar",{"title":541},{"VI":542},"Hsiu-Ming 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D.G. and Cormier, R.A., 1989, A heat exchanger expert system,ASHRAE Trans. 95(2), 927–933.\nBoonyatilkarn, S., Arch, D., and Jones, J.R., 1989, Smart building controls strategies: research and application,ASHRAE Trans. 95(1), 557–562.\nBrothers, P.W., 1988, Knowledge engineering for HVAC expert systems,ASHRAE Trans. 94(1), 1063–1073.\nCaffrey, R.J., 1988, The intelligent building — an ASHRAE opportunity,ASHRAE Trans. 94(1), 925–932.\nCamejo, P.J. and Hittle, D.C., 1989, An expert system for the design of heating, ventilation, and airconditioning systems,ASHRAE Trans. 95(1), 379–386.\nDoherty, T.J. and Arens, E., 1988, Evaluation of the physiological bases of thermal comfort models,ASHRAE Trans. 94(1), 1371–1378.\nDavis, R. and Lenat, D., 1982, Knowledge-Based System in Artificial Intelligence, McGraw-Hill, New York.\nFanger, P.O., 1970, Thermal Comfort Analysis and Application in Environmental Engineering, McGraw-Hill, New York.\nHaberl, J.S., Smith, L.K., Cooney, K.P., and Stern, F.D., 1988, An expert system for building energy consumption analysis: Applications at a university campus,ASHRAE Trans. 94(1), 1037–1051.\nHaberl, J.S. and Claridge, D.E., 1987, An expert system for building energy consumption analysis: prototype results,ASHRAE Trans. 93(1), 979–987.\nHachner, R.J., Mitchell, J.W., and Beckman, W.A., 1984, HVAC system and energy use in existing buildings — part 1,ASHRAE Trans. 90(2B), 523–535.\nHaines, R.W., 1987, Control Systems for Heating, Ventilating, and Air Conditioning, Van Nostrand Reinhold, New York.\nHartman, T.B., 1989, An operator's control language to improve EMS successes,Energy Engineering 86(2), 6–11.\nJohnson, G.A., 1984, Retrofit of a constant volume air system for variable speed fan control,ASHRAE Trans. 90(2B), 201–211.\nKreider, J.F., Cooney, K., Graves, L., Meadows, K., Stern, F., and Weilert, L., 1990, An expert system for comercial building HVAC and energy audits — A progress report,ASHRAE Trans. 96(1), 1549–1571.\nMorre, T., 1986, Artificial intelligence and power production,Energy Engineering 83(3), 4–16.\nNelson, T.M., Nilsson, T.H., and Hopkin, G.W., 1987, Thermal comfort: advantages and deviations,ASHRAE Trans. 93(1), 1039–1047.\nPetze, J.D. and Reed, D.R., 1988, Artificial intelligence in building control systems,ASHRAE Trans. 94(1), 960–969.\nRao, M., 1992, Frontiers and challanges of intelligent process control,Engineering Appl. Artificial Intell.\nRao, M., 1991,Integrated System for Intelligent Control, Lecture Note Control and Inform. Sci., Springer-Verlag, Berlin.\nRao, M., Jiang, T.S., and Tsai, J.P., 1989, Combining symbolic and numerical processing for real-time intelligent control,Engineering Appl. AI 2, 19–27.\nRao, M., Jiang, T.S., and Tsai, J.P., 1988, An intelligent decisiormaker for optimal control,Apll. Artificial Intell. 2, 285–305.\nRao, M., Jiang, T.S., and Tsai, J.P., 1987, A framework of integrated intelligent system,Proc. IEEE Intern. Conf. on Systems, Man, and Cybernetics, Alexandria, Vir., pp. 1133–1137.\nReference Manual of Personal Consutant Plus, by Texas Instruments Inc., 1987.\nRouse, K., 1988, BEMS energy management systems — an overview,Measurement and Control 21.\nShi, J.T. and Zhou, H., 1985, Air conditioning control system with microcomputer using an adaptive regulator,Proc. CLIMA 2000 World Congress on Heating, Ventilating and Air Conditioning, Copenhagen'85.\nTeji, D.S., 1987, Controlling air supply for energy conservation,Energy Engineering 84(3), 4–14.\nTsatsoulis, C. and Kashyap, R.L., 1988, A case-based system for process planning,Robotic & Computer-Integrated Manufacturing 4(3), 557–570.\nTuluca, A.N., Turner, G.E., Krarti, M., and Antipa, V., 1989, Expert system for selection of standardized research greenhouses,ASHRAE Trans. 95(2), 938–953.\nYoung, L.F. and Nelson, R.M., 1990, An expert system to select heat exchangers for waste heat recovery applications,ASHRAE Trans. 96(1), 1539–1548.\nZhou, H., Rao, M., and Chuang, K.T., 1993, Artificial intelligence approach to energy management and control in HVAC process: An evaluation, development, and discussion,Developments in Chemical Engineering: Mineral Processing 1(1), 42–51.",{"EN":618},"This paper describes the construction of an Intelligent System for Operation Planning (ISOP) in heating, ventilating, and air conditioning (HVAC) processes. The system contains important expertise, qualitative reasoning, and quantitative computation. It is used to assist or train operators to achieve better operation in HVAC systems. Expertise about operation planning is expressed as air enthalpy, and moisture conditions and air supply are considered as dynamic parameters. Therefore, it provides a real-time integrated operation planning method in HVAC processes. It offers better energy conservation, comfort and indoor air quality than other methods being currently used. ISOP consists of two levels of frames. The first level classifies HVAC systems by qualitatively reasoning the system structure information, and activates the subframe. In the second level, 16 frames that correspond to the HVAC system structure, accomplish indoor comfort setting, supply air parameter estimations, air enthalpy, and misture evaluation, and then recommend optimal operation conditions. An integrated distributed intelligent system framework is introduced to integrate qualitative reasoning and quantitative computation.",{"EN":620},"Integrated operation planning: Intelligent system approach for HVAC processes",{"VOID":622},"10.1007\u002FBF01276705","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF01276705",[625,640,652],{"id":626,"sortIndex":21,"researcher":20,"roles":627,"affiliations":628,"properties":637},"95523be8-fc54-41a6-abdb-7ceeca75f02d",[275],[629],{"id":20,"sortIndex":21,"affiliation":630,"properties":20},{"id":631,"createTime":632,"updateTime":632,"relativeEntities":633,"slug":20,"properties":634,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"86ba92ee-23d9-4e50-8084-da2b2fd34ca6","2024-02-10T20:29:31.567+00:00",[],{"title":635},{"VI":636},"Department of Chemical Engineering, University of Alberta, Edmonton, Canada",{"title":638},{"VI":639},"Hong Zhou",{"id":641,"sortIndex":269,"researcher":20,"roles":642,"affiliations":643,"properties":649},"09d192e7-45ee-4ef9-b305-46654d5a2389",[275],[644],{"id":20,"sortIndex":21,"affiliation":645,"properties":20},{"id":631,"createTime":632,"updateTime":632,"relativeEntities":646,"slug":20,"properties":647,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":648},{"VI":636},{"title":650},{"VI":651},"Karl T. 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Springer (2016)\nSiradjuddin, I., Behera, L., McGinnity, T.M., Coleman, S.: A position based visual tracking system for a 7 DOF robot manipulator using a kinect camera. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2012)\nLiegeois, A.: Automatic supervisory control of the configuration and behaviour of multibody mechanisms. IEEE Trans. Syst. Man Cybern. 7(12), 868–871 (1977)\nWang, J.G., Li, Y., Zhao, X.: Inverse kinematics and control of a 7-DOF redundant manipulator. Int. J. Adv. Rob. Syst. 7(4), 1–9 (2010)\nKim, J., Sin, M., Lee, J., Kim, D.-H., Lim, H.-K., Kim, S.-R.: Kinematics analysis and motion planning for a 7-DOF redundant industrial robot manipulator. In: 2011 11th International conference on control, automation and systems, pp. 522–527. IEEE (2011)\nCrenganis, M., Breaz, R., Racz, G., Bologa, O.: Inverse kinematics of a 7 DOF manipulator using adaptive neuro-fuzzy inference systems. In: 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 1232–1237. IEEE (2012)\nMason, M.T.: Compliance and force control for computer controlled manipulators. IEEE Trans. Syst. Man. Cybern. 11(6), 418–432 (1981)\nZheng, Y.F., Luh, J.Y.S.: Control of two coordinated robots in motion. In: 1985 24th IEEE Conference on Decision and Control, pp. 1761–1766. IEEE (1985)\nNakano, E.: Cooperational control of the anthropomorphous manipulator” melarm”. In: Proc. of 4th International Symposium on Industrial Robots, pp. 251–260 (1974)\nUchiyama, M., Dauchez, P.: A symmetric hybrid position\u002Fforce control scheme for the coordination of two robots. In: 1988 IEEE International Conference on Robotics and Automation, 1988. Proceedings, pp. 350–356. IEEE (1988)\nKruse, D., Wen, J.T., Radke, R.J.: A sensor-based dual-arm tele-robotic system. IEEE Trans. Autom. Sci. Eng. 12(1), 4–18 (2015)\nCaccavale, F., Chiacchio, P., Marino, A., Villani, L.: Six-DOF impedance control of dual-arm cooperative manipulators. IEEE\u002FASME Trans. Mechatron. 13(5), 576–586 (2008)\nShamsuddin, S., Ismail, L.I., Yussof, H., Zahari, N.I., Bahari, S., Hashim, H., Jaffar, A.: Humanoid robot NAO: Review of control and motion exploration. In: 2011 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 511–516. IEEE (2011)\nShamsuddin, S., Yussof, H., Ismail, L., Hanapiah, F.A., Mohamed, S., Piah, H.A., Zahari, N.I.: Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO. In: 2012 IEEE 8th International Colloquium on Signal Processing and its Applications (CSPA), pp. 188–193. IEEE (2012)\nWu, F.C.: Mathematical Methods in Computer Vision. Science Press (2008)\nLam, L., Lee, S.-W., Suen, C.Y.: Thinning methodologies-a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14(9), 869–885 (1992)\nZhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)\nHe, L., Chao, Y., Suzuki, K.: A run-based two-scan labeling algorithm. IEEE Trans. Image. Process. 17(5), 749–756 (2008)\nKofinas, N., Orfanoudakis, E., Lagoudakis, M.G.: Complete analytical forward and inverse kinematics for the Nao humanoid robot. J. Intell. Robot. Syst. 77(2), 251–264 (2015)\nMayer, G.E., Paul, R.P., Shimano, B.: Differential kinematic control equations for simple manipulators. IEEE Trans. Syst. Man Cybern., 1(1) (1981)",{"EN":772},"With the rise of service robots, research on cooperation between two-arm robots has become increasingly important. In this paper, two NAO two-armed robots are used as the experimental platform and are combined with projective geometry, vision, robotics and other knowledge to carry out theoretical derivation and experiments on the coordinated movements of dual-arm robots. From the aspect of visual information processing, we analyse and solve the detailed target recognition process. Then, on this basis, we propose a set of complete coordinated motion control schemes. For object recognition, in this paper, we propose a highly adaptable linear stick recognition method. To solve the control flow of coordinated movement, we calculate the inverse kinematics of the unreachable pose of a single NAO manipulator by ignoring the degree of freedom of rotation around an end axis, and propose a trajectory planning method for the vertical constraint relationship between the tool and the workpiece plane in the coordinated manipulator movement. A comparison of the results of a simulation and a real experiment reveals that the trajectories of a workpiece clamped at the ends of the two robots’ mechanical arms are roughly the same; consequently, the coordinated control scheme proposed in this paper is feasible. Moreover, the scheme proposed in this paper is sufficiently accurate to meet service robot applications in daily life. Because the joint active clearance of the NAO robot arm is large and its sensor sensitivity is high, clearance change can be used in the future to replace the force sensor for hybrid control.",{"EN":774},"A Vision-Based Coordinated Motion Scheme for Dual-Arm Robots",{"VOID":776},"10.1007\u002Fs10846-019-01035-9","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10846-019-01035-9",[779,794],{"id":780,"sortIndex":21,"researcher":20,"roles":781,"affiliations":782,"properties":791},"dcec4881-fc81-481a-a482-b000ae763e37",[275],[783],{"id":20,"sortIndex":21,"affiliation":784,"properties":20},{"id":785,"createTime":786,"updateTime":786,"relativeEntities":787,"slug":20,"properties":788,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"18786b5f-c9ac-405b-8155-bfb2956c9784","2023-12-12T16:15:50.028+00:00",[],{"title":789},{"VI":790},"College of Electromechanical Engineering, Qingdao University of Science 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considering the dynamic response of a robot manipulator as characterized by the sliding function, a technique is proposed to estimate the perturbation in the robot control system. Perturbation compensation is then incorporated in the design of a robust control law to cancel the effects of system parametric uncertainties and external disturbances. A normalized power rate component is introduced to replace the discontinuous control that is usually associated with variable structure system. The suggested robust control law ensures that the robot control system reaches an user specified neighbourhood of the sliding manifold in finite time and with prescribed transient behaviour. Explicit estimates of the bounds on modelling errors and external disturbances are not required while signal measurement uncertainties can be accommodated. Furthermore, using the equivalent control concept, a very simple expression is derived to estimate the system perturbation signal. Detailed computer simulation results are presented to demonstrate the effectiveness of the proposed control law.",{"EN":854},"A Robust Control Law with Estimated Perturbation Compensation for Robot Manipulators",{"VOID":856},"10.1023\u002FA:1023093123100",[858],"EN","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1023093123100",[861],{"id":862,"sortIndex":21,"researcher":20,"roles":863,"affiliations":864,"properties":876},"833a9cd2-abbc-4f9f-b394-953ed56edfff",[],[865],{"id":866,"sortIndex":21,"affiliation":867,"properties":20},"fa7a5b06-10bc-4665-81aa-6f131ecd7e34",{"id":868,"createTime":869,"updateTime":870,"relativeEntities":871,"slug":872,"properties":873,"entityType":80,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"c1eac06e-ac11-4f41-b63e-6ef604208e38","2024-04-21T14:52:40.272+00:00","2024-06-19T12:00:28.254+00:00",[],"School-of-Electrical-and-Electronic-Engineering-Block-S1-Nanyang-Technological-University-Singapore-Republic-of-Singapore",{"title":874},{"EN":875},"School of Electrical and Electronic Engineering, Block S1, Nanyang Technological University, Singapore, Republic of Singapore",{"title":877},{"EN":878},"S. 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Robotics and Automation Conference, San Francisco, CA, IEEE, Piscataway, NJ, pp. 977–982.",{"id":1153,"createTime":1154,"updateTime":1155,"relativeEntities":1156,"slug":1157,"properties":1158,"entityType":266,"verifyStatus":267,"verifyTime":1167,"verifyNote":268,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1168,"fullTextUrl":20,"authors":1169,"publicationType":300,"publisherRelationship":1197,"citationCount":20,"citationInfo":20,"publishDate":1231,"publishYear":1232,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":337},"a52efb03-0f92-411a-902f-3a5616b9670c","2024-01-12T09:41:11.892+00:00","2024-12-26T23:56:07.878+00:00",[],"Near-Time-Optimal-Trajectory-Generation-for-Multirotors-using-Numerical-Optimization-and-Safe-Corridors",{"references":1159,"abstract":1161,"title":1163,"doi":1165},{"VOID":1160},"Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation (In Press) (2018)\nBeul, M., Behnke, S.: Analytical time-optimal trajectory generation and control for multirotors. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 87–96. IEEE (2016)\nBircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R: Receding horizon “next-best-view” planner for 3d exploration. IEEE International Conference on Robotics and Automation (ICRA) (2016)\nBircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R.: Receding horizon path planning for 3d exploration and surface inspection. Auton. Robot., pp 1–16 (2016)\nBonami, P., Lee, J.: Bonmin user’s manual. Numer. Math 4, 1–32 (2007)\nBurri, M., Oleynikova, H., Achtelik, M. W., Siegwart, R.: Real-time visual-inertial mapping, re-localization and planning onboard mavs in unknown environments. In: 2015 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1872–1878. IEEE (2015)\nFoehn, P., Scaramuzza, D.: Cpc: Complementary progress constraints for time-optimal quadrotor trajectories. arXiv preprint arXiv:2007.06255 (2020)\nGao, F., Wu, W., Pan, J., Zhou, B., Shen, S.: Optimal time allocation for quadrotor trajectory generation. In: 2018 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4715–4722. IEEE (2018)\nGuerra, W., Tal, E., Murali, V., Ryou, G., Karaman, S.: Flightgoggles: Photorealistic sensor simulation for perception-driven robotics using photogrammetry and virtual reality. arXiv preprint arXiv:1905.11377 (2019)\nHehn, M., D’Andrea, R.: Quadrocopter trajectory generation and control. IFAC Proceedings 44(1), 1485–1491 (2011)\nKamel, M., Burri, M., Siegwart, R.: Linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles. IFAC-PapersOnLine 50(1), 3463–3469 (2017)\nLiu, S., Atanasov, N., Mohta, K., Kumar, V.: Search-based motion planning for quadrotors using linear quadratic minimum time control. In: 2017 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2872–2879. IEEE (2017)\nLiu, S., Watterson, M., Mohta, K., Sun, K., Bhattacharya, S., Taylor, C.J., Kumar, V.: Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-d complex environments. IEEE Robot. Autom. Lett. 2(3), 1688–1695 (2017)\nLockheed-Martin: Alphapilot – lockheed martin ai drone racing innovation challenge. https:\u002F\u002Fwww.herox.com\u002Falphapilot, accessed 2019-09-14\nMadaan, R., Gyde, N., Vemprala, S., Brown, M., Nagami, K., Taubner, T., Cristofalo, E., Scaramuzza, D., Schwager, M., Kapoor, A.: Airsim drone racing lab (2020)\nMellinger, D., Kumar, V.: Minimum snap trajectory generation and control for quadrotors. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2520–2525. IEEE (2011)\nMicrosoft-Airsim: Game of drones competition. https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim-NeurIPS2019-Drone-Racing, Accessed 2019-09-14\nNocedal, J.: Knitro: an Integrated Package for Nonlinear Optimization. In: Large-Scale Nonlinear Optimization, pp. 35–60. Springer (2006)\nOettershagen, P., Stastny, T., Mantel, T., Melzer, A., Rudin, K., Gohl, P., Agamennoni, G., Alexis, K., Siegwart, R.: Long-endurance sensing and mapping using a hand-launchable solar-powered uav. Field and Service Robotics, pp. 441–454 (2016)\nRichter, C., Bry, A., Roy, N.: Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments. In: Robotics Research, pp 649–666. Springer (2016)\nShah, S., Dey, D., Lovett, C., Kapoor, A.: Airsim: High-fidelity visual and physical simulation for autonomous vehicles. In: Field and Service Robotics. https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.05065 (2017)\nSpedicato, S., Notarstefano, G.: Minimum-time trajectory generation for quadrotors in constrained environments. IEEE Trans. Control Syst. Technol. 26(4), 1335–1344 (2017)\nTutsoy, O., Barkana, D.E., Balikci, K.: A novel exploration-exploitation-based adaptive law for intelligent model-free control approaches. IEEE Trans. Cybern., pp. 1–9. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTCYB.2021.3091680 (2021)\nVerschueren, R., Frison, G., Kouzoupis, D., van Duijkeren, N., Zanelli, A., Quirynen, R., Diehl, M.: Towards a modular software package for embedded optimization. In: Proceedings of the IFAC Conference on Nonlinear Model Predictive Control (NMPC) (2018)\nWächter, A., Biegler, L.: Ipopt-an interior point optimizer (2009)",{"EN":1162},"Trajectory generation is a fundamental problem for every type of robot. In most applications, the robots should reach their goals in the minimum time possible. Time-optimal trajectory generation allows us to solve this problem. The generation of such trajectories for multirotors has gained traction with new applications in transport, delivery and search and rescue missions, as well as other applications in sports and entertainment such as drone racing. The current state-of-the-art is heavily based on polynomial methods and most methods choose a conservative approach when limiting the velocity or acceleration as a way to account for nonlinearities and guarantee feasibility, which limits time optimality and trajectory speed. We overcome this limitation by proposing a new formulation for multirotors trajectory generation that takes into account nonlinearities such as gravity and aerodynamic drag, It allows us to provide more time-optimal solutions then the state-of-the-art. We present an algorithm that uses our new formulation for near time-optimal trajectory generation for multirotors subject to obstacles\u002Fpath constraints. 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