Multi-Objective Design of Parallel Manipulator Using Global Indices

Abstract

The paper addresses the optimal design of parallel manipulators based on multi-objective optimization. The objective functions used are: Global Conditioning Index (GCI), Global Payload Index (GPI), and Global Gradient Index (GGI). These indices are evaluated over a required workspace which is contained in the complete workspace of the parallel manipulator. The objective functions are optimized simultaneously to improve dexterity over a required workspace, since single optimization of an objective function may not ensure an acceptable design. A Multi-Objective Evolution Algorithm (MOEA) based on the Control Elitist Non-dominated Sorting Genetic Algorithm (CENSGA) is used to find the Pareto front.

Analysis and Optimization of the Double-Axle Steering Mechanism with Dynamic Loads

“…To find: Y J , X H , X I , Z I , Y M , X K , X L , Z L , X B , Z B , X D , Z D , X E , Z E , X G , Z G to minimize: where real_angle is the right turn angle determined by steering system, theoretic_angle is the right turn angle calculated by Eqs. (24) and (28). When using the target cascading method we divide the whole steering system into 7 subsystems based on target cascading method, optimizing the first and second steering trapeziums, the first and second steering symmetrical characteristics, the first and second interference steering, middle linkage system.…”

“…In an optimization problem we have to balance the size of design variables and the efficiency. An original multiobjective optimum algorithm can be used by considering multi-different objective functions [23,24]. However, the target cascading method [21] is a perfect means to solve the problems with large number of design variables.…”

SQP Optimization of 6dof 3x3 UPU Parallel Robotic System for Singularity Free and Maximized Reachable Workspace

“…Specifically, linearly driven six legs which are connected to a mobile and a base platform cause a full spatial motion of 3-axis translational and 3-axis rotational motion. This PRS is called Stewart Platform [20,25,26] and classified by the number of leg connections on upper and lower platforms. A 3x3 SP includes 3 connections on lower and upper side of the legs.…”

“…In the literature, there are other performance indexes such as GSI (Global Stiffness Index) and VTF (Velocity Transformation Factor) [19]. Therefore, some researchers optimized PRS for multiperformance indexes which are called MOO (Multiobjective Optimization) [20][21][22]. These optimization methods are used for investigating the best geometric parameters for structures of parallel manipulators.…”

Design for Optimal Performance of 3-RPS Parallel Manipulator Using Evolutionary Algorithms

“…F.A. Lara-Molina et al [30] performed the optimal design of a spatial Stewart-Gough platform based on multi-objective optimization; the objective functions are Global Condition Index (GCI), Global Payload Index (GPI) and Global Gradient Index (GGI) using multi-objective Evolutionary Algorithm (MOEA). Chun-Ta Chen et al [31] presented a constrained multi-objective genetic algorithm for a general motor-driven parallel kinematic manipulator 3UPS for the optimal trajectory of a PKM with linear actuators, in which travelling time and energy expended in driving the platform from one pose to another are minimized.…”