{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:08:04Z","timestamp":1764936484293},"reference-count":17,"publisher":"Cambridge University Press (CUP)","issue":"6","license":[{"start":{"date-parts":[[2008,11,1]],"date-time":"2008-11-01T00:00:00Z","timestamp":1225497600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2008,11]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>A general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of the NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MODE are best for this problem.<\/jats:p>","DOI":"10.1017\/s0263574708004359","type":"journal-article","created":{"date-parts":[[2008,3,27]],"date-time":"2008-03-27T14:20:55Z","timestamp":1206627655000},"page":"753-765","source":"Crossref","is-referenced-by-count":25,"title":["Multiobjective trajectory planner for industrial robots with payload constraints"],"prefix":"10.1017","volume":"26","author":[{"given":"R.","family":"Saravanan","sequence":"first","affiliation":[]},{"given":"S.","family":"Ramabalan","sequence":"additional","affiliation":[]},{"given":"C.","family":"Balamurugan","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2008,11,1]]},"reference":[{"key":"S0263574708004359_ref7","doi-asserted-by":"publisher","DOI":"10.1109\/41.824136"},{"key":"S0263574708004359_ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.euromechsol.2004.02.006"},{"key":"S0263574708004359_ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0094-114X(99)00062-2"},{"key":"S0263574708004359_ref13","doi-asserted-by":"publisher","DOI":"10.5772\/5781"},{"key":"S0263574708004359_ref4","doi-asserted-by":"publisher","DOI":"10.1023\/A:1017587311457"},{"key":"S0263574708004359_ref15","unstructured":"15. Babu B. V. and Anbarasu B. , \u201cMulti-Objective Differential Evolution (MODE): An Evolutionary Algorithm for Multi-Objective Optimization Problems (MOOPs),\u201d http:\/\/discovery.bitspilani.ac.in\/discipline\/chemical\/BVb\/publications\/htmlPrice."},{"key":"S0263574708004359_ref2","first-page":"1","article-title":"Dynamic optimization for the trajectory planning of robot manipulators in the presence of obstacles","volume":"21","author":"Saramago","year":"1999","journal-title":"J. Brazilian Soc. Mech. 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