Kinematic sensitivity and workspace optimization of planar parallel mechanisms using evolutionary techniques

Daneshmand, Morteza; Saadatzi, Mohammad Hossein; Masouleh, Mehdi Tale

doi:10.1109/icrom.2013.6510138

Cited by 6 publications

(10 citation statements)

References 8 publications

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“…However, from the study conducted in [36], it is revealed that even an optimized 3-RPP or 3-RPP has a limited workspace. Therefore, they are excluded from the rest of the paper, and the remaining eight are considered.…”

Section: Review Of the Case-study: Moo Of The Point-displacement And mentioning

confidence: 99%

“…This assertion is investigated extensively and proved in [36,55,56,60,61]. Therefore, two separate performance indices are considered for the purpose of this paper, namely point-displacement and rotational kinematic sensitivity, which have been recently proposed to the end of alleviating the limitations of the above notorious indices [55].…”

Section: Review Of the Case-study: Moo Of The Point-displacement And mentioning

confidence: 99%

“…From the study conducted in [55], it can be inferred that, geometrically, kinematic sensitivity is the maximum displacement or rotation of the moving platform under a unit-norm change in the joint space, which, for a PPM, can be formulated as follows: Two types of norms, namely 2-and 1-norm, are frequently used in practice, since a geometric interpretation could be associated with them, which leads to four possible combinations of formulating the kinematic sensitivity, where [35,36] report that c = 1…”

Section: Review Of the Case-study: Moo Of The Point-displacement And mentioning

confidence: 99%

“…The workspace of PMs has been investigated from di erent perspectives in the literature [66]. In this paper, constant-orientation workspace is considered, which consists of the set of all the possible Cartesian coordinates of a given point of the mobile platform that can be reached for a prescribed orientation [36]. The area of the workspace can be calculated numerically using discrete integration, which can be formulated as follows [64]:…”

Section: Review Of the Case-study: Moo Of The Point-displacement And mentioning

confidence: 99%

“…Noticing that, as aforementioned, in most practical contexts, di erent objective functions are reported in non-homogeneous units or with di erent scales, using the foregoing strategy, the e ect of these complications is overcome, and the composite objective function is made robust against the changes of the scales by which the objective functions are represented, and each of them plays a role as signi cant as the others in guiding the algorithm through the search process. Besides, the practical reliability of the PPCOF is demonstrated via applying it to a use-case, namely, the MOO of the point-displacement and rotational kinematic sensitivity [34,35] and workspace of eight Planar Parallel Mechanisms (PPMs), and comparing its e ciency with that of NSGA-II, which has been implemented on the same case-study in [36].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

PPCOF: A Robust Proportion-Preserving Composite Objective Function for Scale-Invariant Multi-Objective Optimization

et al. 2017

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Abstract. This paper aims to introduce a proportion-preserving composite objective function for multi-objective optimization, namely, PPCOF, and validate its e ciency through demonstrating its applicability to optimization of the kinetostatic performance of planar parallel mechanisms. It exempts the user from both specifying preference factors and conducting decision-making. It consists of two terms. The rst one adds the normalized objective functions up, where the extrema result from single-objective optimization. To make the composite objective function steer the variations of the objective functions while preserving rational proportions between them, as the main contribution of the paper, it is sought that the normalized objective functions take closely similar values, to which end they are juxtaposed inside a vector, which is then scaled such that its Euclidean norm-2 is equal to that of the vector of all ones with the same dimensions. Then, the second term is constructed as the addition of penalty factors standing for the absolute value of the di erence between each element of the foregoing vector from 1. From the obtained results, with considerably smaller computational cost, the PPCOF obtains an optimal solution that is not dominated by any point from a set of Pareto-optimal solutions o ered by NSGA-II.

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Section: Review Of the Case-study: Moo Of The Point-displacement And mentioning

confidence: 99%

Section: Review Of the Case-study: Moo Of The Point-displacement And mentioning

confidence: 99%

Section: Review Of the Case-study: Moo Of The Point-displacement And mentioning

confidence: 99%

Section: Review Of the Case-study: Moo Of The Point-displacement And mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PPCOF: A Robust Proportion-Preserving Composite Objective Function for Scale-Invariant Multi-Objective Optimization

et al. 2017

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Real-time, automatic shape-changing robot adjustment and gender classification

Daneshmand

Aabloo

Özçinar

et al. 2015

SIViP

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A statistical weighted method for kinematic sensitivity analysis of parallel robots

Mehrafrooz

Mohammadi

Masouleh

2018

J Braz. Soc. Mech. Sci. Eng.

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Many efforts have been devoted to propose effective methods and indices of kinematic sensitivity, but all of them have considered equal proportion of errors in the actuated joint coordinates and generally entail several drawbacks. Therefore, all of previous studies conducted in the literature ignored the difference in sensitivity of manipulator to the active joints uncertainties. Solving this issue requires a deep understanding of mathematical modeling of kinematic sensitivity and its interpretations. In this paper, a synergy between kinematic concepts and statistics approaches is employed in order to propose a more reasonable index for the kinematic performance measurement of the robotic mechanical systems, with a more emphasis on parallel mechanisms.To this end, from kinematic stand point, the kinematic sensitivity is adopted as kinematic index performance. In turn, the so-called Sobol's method is used as the statistics framework of this paper. To do so, first, weighted kinematic sensitivity is introduced and its mathematical formulation is presented. Then, the application of the proposed method is illustrated by implementing the proposed method on two case studies, namely 3-RPR and 6-DOF Gough-Stewart parallel robots. Weighted kinematic sensitivity is calculated over the robot workspace, and consequently, weighted kinematic sensitivity and conventional methods and indices are compared. From the obtained results, it can be inferred that conventional methods predict sensitivity more conservative and the proposed method is more realistic and closer to the reality.

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Kinematic sensitivity and workspace optimization of planar parallel mechanisms using evolutionary techniques

Cited by 6 publications

References 8 publications

PPCOF: A Robust Proportion-Preserving Composite Objective Function for Scale-Invariant Multi-Objective Optimization

PPCOF: A Robust Proportion-Preserving Composite Objective Function for Scale-Invariant Multi-Objective Optimization

Real-time, automatic shape-changing robot adjustment and gender classification

A statistical weighted method for kinematic sensitivity analysis of parallel robots

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