2018
DOI: 10.1590/1679-78254044
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Multi-objective optimization of a parallel manipulator for the design of a prosthetic arm using genetic algorithms

Abstract: This paper presents a synthesis of a spherical parallel manipulator for a shoulder of a seven-degrees-of-freedom prosthetic human arm using a multi-objective optimization. Three design objectives are considered, namely the workspace, the dexterity, and the actuators torques. The parallel manipulator is modelled considering 13 design parameters in an optimization procedure. Due to the non-linearity of the design problem, genetic algorithms are implemented. The outcomes show that a suitable performance of the ma… Show more

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Cited by 15 publications
(18 citation statements)
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“…[2]) and parallel (e.g. [8,10]) robots. In this study, we selected the classical Yoshikawa's manipulability index [14] because of the following reasons.…”
Section: Optimization Of the End-effectormentioning
confidence: 99%
“…[2]) and parallel (e.g. [8,10]) robots. In this study, we selected the classical Yoshikawa's manipulability index [14] because of the following reasons.…”
Section: Optimization Of the End-effectormentioning
confidence: 99%
“…O mecanismo do ombro é modelado como um manipulador paralelo esférico do tipo 3-RRR (Figura 1 ). A síntese dimensional deste manipulador foi realizada utilizando uma otimização multiobjetiva baseada em algoritmos genéticos [ 11 ].…”
Section: Design De Ombrounclassified
“…A função objetivo foi a minimização do torque máximo exercido pelo motor. Uma análise estática do mecanismo de quatro barras foi realizada como mostrado na Figura 2 .De simulações dinâmicas anteriores[ 11 ], e são valores conhecidos. Uma otimização baseada no GA foi realizada usando o algoritmo da Figura 3 .…”
unclassified
“…The shoulder mechanism is modeled as a 3-RRR-type spherical parallel manipulator ( Figure 1 ). The dimensional synthesis of this manipulator was carried out using a multiobjective optimization based on genetic algorithms [ 11 ].…”
Section: Shoulder Designmentioning
confidence: 99%
“…From previous dynamic simulations [ 11 ], τ out and θ 3 are known values. An optimization based on GA was carried out using the algorithm in Figure 3 .…”
Section: Shoulder Designmentioning
confidence: 99%