Comparative study of SQP and metaheuristics for robotic manipulator design

Bergamaschi, Paulo Roberto; Saramago, Sezimária de Fátima Pereira; Coelho, Leandro dos Santos

doi:10.1016/j.apnum.2007.08.003

Cited by 26 publications

(25 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig. 11.1 The workspace for 3R manipulators and design parameters (Bergamaschi et al 2008; Reprinted with permission from Elsevier)…”

mentioning

confidence: 99%

Design Optimization of a Robot Manipulator Using TLBO and ETLBO Algorithms

Rao

2015

Teaching Learning Based Optimization Algorithm

View full text Add to dashboard Cite

The effectiveness of the TLBO and ETLBO algorithms is verified for design optimization of a robot manipulator by considering four cases and imposing different conditions to demonstrate the efficiency of the design process. The workspace volume is considered as an objective function. The results of the TLBO and ETLBO algorithms are compared with the SQP, GA, DE, and PSO algorithms. The computational results show that for all the four cases the TLBO and ETLBO algorithms have obtained more accurate solutions than those obtained by the other optimization methods. Design Optimization of Robot ManipulatorOver the past few decades, the interest of researchers is growing in the field of design optimization of robotic system in order to improve the system performance using advanced optimization techniques. In the present work, design optimization of a robot manipulator is considered. An industrial robot is a programmable, multifunctional manipulator designed to move materials, parts, tools, or special devices through variable programmed motions for a variety of tasks. Robots come in variety of sizes, shapes, and capabilities. Robots have four basic components namely a manipulator, an end effect which is a part of manipulator, computer controller, and a power supply. Robot anatomy is concerned with the physical construction of body, arm, and wrist of the machine. Relative movements between various components of the body, arm, and wrist are provided by a series of either sliding or rotating joints. The body, arm, and wrist assembly is called as manipulator. The manipulator is a mechanism consisting of the major linkages, minor linkages, and the end effector (gripper or tool). Robot is designed to reach a work piece within its work volume. Work volume is the term that refers to the space within which the robot can manipulate its wrist end. It is also called as work space. The surface of work space is termed as work envelop.

show abstract

“…Fig. 11.1 The workspace for 3R manipulators and design parameters (Bergamaschi et al 2008; Reprinted with permission from Elsevier)…”

mentioning

confidence: 99%

Design Optimization of a Robot Manipulator Using TLBO and ETLBO Algorithms

Rao

2015

Teaching Learning Based Optimization Algorithm

View full text Add to dashboard Cite

show abstract

“…1 [28]. The study of this type of manipulator is done according to the Denavit-Hartenberg parameters: 4 , r 2 , and r 3 . To reduce the number of parameters and simplify the problem, will be considered d 2 =1 and r 3 =0.…”

Section: Mathematical Modeling Of the Robotic Manipulatormentioning

confidence: 99%

“…Ceccarelli and Lanni [8] presented a suitable formulation for the workspace that can be used in the design of manipulators, which was formulated as a multi-objective optimization problem using the workspace volume and robot dimensions as objective functions. Bergamaschi et al [3,4] studied the design of manipulators with three-revolute joints (3R) using an optimization problem that takes into account the characteristics of the workspace. The optimization problem is formulated considering the workspace volume as the objective function.…”

Section: Introductionmentioning

confidence: 99%

“…In robotic manipulators, a fundamental characteristic that must be taken into account in the dimensional design is the volume of their workspace. It is crucial to calculate the workspace and its boundaries with perfect precision, because they influence the dimensional design, the manipulator's positioning in the work environment, and its dexterity to execute tasks [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study Using Shuffled Complex Evolution and Differential Evolution Applied to Robotic Manipulator Design

Brandão¹,

Doricio²,

Lobato³

et al. 2014

Proceedings of 10th World Congress on Computational Mechanics

View full text Add to dashboard Cite

show abstract

“…Hence, the manipulator design have been stated as an optimization problem where optimization techniques such as, heuristic algorithms [15], [16], [17], [18], [19], [20] and gradient based algorithms [21], have been used. Nevertheless, if the optimization problem is nonlinear or discontinuous one, gradient based algorithms are not suitable to solve the problem because they converge to local minima near the initial condition (sensitive to initial condition) [22], [23], then the design solution will perform poorly. So, it is important to have an algorithm that efficiently search in the design space to obtain a feasible solution, i.e., to obtain a set of parameters that describe the system and meet the design requirements.…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of Parallelogram Five-bar Manipulator for Dexterous Workspace by using ELEMAEF in Differential Evolution

Villarreal-Cervantes¹,

Cruz-Mucio²,

Portilla-Flores³

2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

Abstract:The kinematic design of mechanism is an important stage in the design methodology. A dexterous workspace for a manipulator is an outstanding characteristic that must be considered in it. Hence, a mono-objective constraint optimization problem (MOCOP) for the kinematic design of a manipulator with three revolute joints (3R robot), that fulfils a defined dexterous workspace, is formulated. The MOCOP is solved by proposing a mechanism in the differential evolution (DE) algorithm called exhaustive local exploitation mechanism with adaptive scale factor (ELEMAEF). This mechanism exhaustively exploits a local region in the search space with the information of the base and the difference vectors of good trial vector, in an attempt to generate better individuals in the same direction. In addition, the ELEMAEF guides the evolution of the population toward a better zone without sacrificing the search capabilities of the DE algorithm. A comparison of the DE algorithm with and without the ELEMAEF for this particular design problem is presented. The use of the ELEMAEF gives a superior performance in the DE algorithm.

show abstract

Comparative study of SQP and metaheuristics for robotic manipulator design

Cited by 26 publications

References 12 publications

Design Optimization of a Robot Manipulator Using TLBO and ETLBO Algorithms

Design Optimization of a Robot Manipulator Using TLBO and ETLBO Algorithms

A Comparative Study Using Shuffled Complex Evolution and Differential Evolution Applied to Robotic Manipulator Design

Optimum Design of Parallelogram Five-bar Manipulator for Dexterous Workspace by using ELEMAEF in Differential Evolution

Contact Info

Product

Resources

About