Design optimization of a cable actuated parallel ankle rehabilitation robot: A fuzzy based multi-objective evolutionary approach

Prashant K. Jamwal, Shahid Hussain

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Robotic devices can be potentially used to assist physical therapy treatments in order to restore musculoskeletal system malfunctions owing to neurological disorders. Cable actuated parallel robots, despite their obvious benefits such as enhanced workspace, light weight, and flexibility, are not popularly used in ankle rehabilitation treatments, due to their complex mechanism and cable actuation issues. In order to address these issues, it is recommended to carry out robot design optimization. However, design synthesis of the cable actuated parallel ankle robot calls for multi-objective optimization (MOO), since there are multiple and conflicting objectives to achieve. To acquire more choices between actuator forces, overall stiffness of robot (which is crucial for a cable based ankle robot) and other vital design objectives, it is required to explore the extreme ends of the Pareto Front (PF) more carefully. Existing multi-objective evolutionary algorithms (MOEAs) normally focus on the convergence and may not provide solutions at the extremities of PF. Capitalizing on this improvement opportunity, this paper presents a fuzzy based MOEA, namely, biased fuzzy sorting genetic algorithm (BFSGA) which encourages solutions in the extreme zones of the PF. It is shown in this paper that using proposed method, diversity in the populations is supported and in the process wider trade-off choices of objectives can be obtained. During ankle robot design optimization, crisp objectives are defined as fuzzy objectives and competing solutions are provided an overall activation score (OAS). Subsequently OAS is used to assign a fuzzy dominance ranking to the design solutions. It is found that the BFSGA approach performs well in exploring the extreme zones of the Pareto front, which are normally overlooked by other MOEA such as NSGA-II due to their inherent mechanism.

Original languageEnglish
Pages (from-to)1897-1908
Number of pages12
JournalJournal of Intelligent and Fuzzy Systems
Volume31
Issue number3
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes

Fingerprint

Rehabilitation
Cable
Patient rehabilitation
Pareto Front
Cables
Robot
Robots
Multi-objective Evolutionary Algorithm
Parallel Robot
Extremes
Evolutionary algorithms
Sorting algorithm
Biased
Activation
Sorting
Diversity Methods
Genetic Algorithm
Genetic algorithms
Chemical activation
Musculoskeletal system

Cite this

@article{d7157d0ec581467db05379485d0c591d,
title = "Design optimization of a cable actuated parallel ankle rehabilitation robot: A fuzzy based multi-objective evolutionary approach",
abstract = "Robotic devices can be potentially used to assist physical therapy treatments in order to restore musculoskeletal system malfunctions owing to neurological disorders. Cable actuated parallel robots, despite their obvious benefits such as enhanced workspace, light weight, and flexibility, are not popularly used in ankle rehabilitation treatments, due to their complex mechanism and cable actuation issues. In order to address these issues, it is recommended to carry out robot design optimization. However, design synthesis of the cable actuated parallel ankle robot calls for multi-objective optimization (MOO), since there are multiple and conflicting objectives to achieve. To acquire more choices between actuator forces, overall stiffness of robot (which is crucial for a cable based ankle robot) and other vital design objectives, it is required to explore the extreme ends of the Pareto Front (PF) more carefully. Existing multi-objective evolutionary algorithms (MOEAs) normally focus on the convergence and may not provide solutions at the extremities of PF. Capitalizing on this improvement opportunity, this paper presents a fuzzy based MOEA, namely, biased fuzzy sorting genetic algorithm (BFSGA) which encourages solutions in the extreme zones of the PF. It is shown in this paper that using proposed method, diversity in the populations is supported and in the process wider trade-off choices of objectives can be obtained. During ankle robot design optimization, crisp objectives are defined as fuzzy objectives and competing solutions are provided an overall activation score (OAS). Subsequently OAS is used to assign a fuzzy dominance ranking to the design solutions. It is found that the BFSGA approach performs well in exploring the extreme zones of the Pareto front, which are normally overlooked by other MOEA such as NSGA-II due to their inherent mechanism.",
keywords = "Ankle robot, Design optimization, Fuzzy methods, Parallel mechanism, Rehabilitation",
author = "Jamwal, {Prashant K.} and Shahid Hussain",
year = "2016",
month = "1",
day = "1",
doi = "10.3233/JIFS-16030",
language = "English",
volume = "31",
pages = "1897--1908",
journal = "Journal of Intelligent and Fuzzy Systems",
issn = "1064-1246",
publisher = "IOS Press",
number = "3",

}

TY - JOUR

T1 - Design optimization of a cable actuated parallel ankle rehabilitation robot: A fuzzy based multi-objective evolutionary approach

AU - Jamwal, Prashant K.

AU - Hussain, Shahid

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Robotic devices can be potentially used to assist physical therapy treatments in order to restore musculoskeletal system malfunctions owing to neurological disorders. Cable actuated parallel robots, despite their obvious benefits such as enhanced workspace, light weight, and flexibility, are not popularly used in ankle rehabilitation treatments, due to their complex mechanism and cable actuation issues. In order to address these issues, it is recommended to carry out robot design optimization. However, design synthesis of the cable actuated parallel ankle robot calls for multi-objective optimization (MOO), since there are multiple and conflicting objectives to achieve. To acquire more choices between actuator forces, overall stiffness of robot (which is crucial for a cable based ankle robot) and other vital design objectives, it is required to explore the extreme ends of the Pareto Front (PF) more carefully. Existing multi-objective evolutionary algorithms (MOEAs) normally focus on the convergence and may not provide solutions at the extremities of PF. Capitalizing on this improvement opportunity, this paper presents a fuzzy based MOEA, namely, biased fuzzy sorting genetic algorithm (BFSGA) which encourages solutions in the extreme zones of the PF. It is shown in this paper that using proposed method, diversity in the populations is supported and in the process wider trade-off choices of objectives can be obtained. During ankle robot design optimization, crisp objectives are defined as fuzzy objectives and competing solutions are provided an overall activation score (OAS). Subsequently OAS is used to assign a fuzzy dominance ranking to the design solutions. It is found that the BFSGA approach performs well in exploring the extreme zones of the Pareto front, which are normally overlooked by other MOEA such as NSGA-II due to their inherent mechanism.

AB - Robotic devices can be potentially used to assist physical therapy treatments in order to restore musculoskeletal system malfunctions owing to neurological disorders. Cable actuated parallel robots, despite their obvious benefits such as enhanced workspace, light weight, and flexibility, are not popularly used in ankle rehabilitation treatments, due to their complex mechanism and cable actuation issues. In order to address these issues, it is recommended to carry out robot design optimization. However, design synthesis of the cable actuated parallel ankle robot calls for multi-objective optimization (MOO), since there are multiple and conflicting objectives to achieve. To acquire more choices between actuator forces, overall stiffness of robot (which is crucial for a cable based ankle robot) and other vital design objectives, it is required to explore the extreme ends of the Pareto Front (PF) more carefully. Existing multi-objective evolutionary algorithms (MOEAs) normally focus on the convergence and may not provide solutions at the extremities of PF. Capitalizing on this improvement opportunity, this paper presents a fuzzy based MOEA, namely, biased fuzzy sorting genetic algorithm (BFSGA) which encourages solutions in the extreme zones of the PF. It is shown in this paper that using proposed method, diversity in the populations is supported and in the process wider trade-off choices of objectives can be obtained. During ankle robot design optimization, crisp objectives are defined as fuzzy objectives and competing solutions are provided an overall activation score (OAS). Subsequently OAS is used to assign a fuzzy dominance ranking to the design solutions. It is found that the BFSGA approach performs well in exploring the extreme zones of the Pareto front, which are normally overlooked by other MOEA such as NSGA-II due to their inherent mechanism.

KW - Ankle robot

KW - Design optimization

KW - Fuzzy methods

KW - Parallel mechanism

KW - Rehabilitation

UR - http://www.scopus.com/inward/record.url?scp=84985011853&partnerID=8YFLogxK

U2 - 10.3233/JIFS-16030

DO - 10.3233/JIFS-16030

M3 - Article

VL - 31

SP - 1897

EP - 1908

JO - Journal of Intelligent and Fuzzy Systems

JF - Journal of Intelligent and Fuzzy Systems

SN - 1064-1246

IS - 3

ER -