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Finding Pareto solutions of design parameter of run-ning robot leg using soft computing

Title
Finding Pareto solutions of design parameter of run-ning robot leg using soft computing
Translated Title
소프트 컴퓨팅을 이용한 달리기 로봇 다리 디자인 매개변수의 파레토 근 구하기
Authors
Yu, Byeong Gi
DGIST Authors
Yu, Byeong Gi; Moon, Sang Jun; Kwon, Oh Seok
Advisor(s)
Moon, Sang Jun
Co-Advisor(s)
Kwon, Oh Seok
Issue Date
2015
Available Date
2015-01-12
Degree Date
2015. 2
Type
Thesis
Keywords
OptimizationCo-designspring-mass modelNondominated sort genetic algorithm (NSGA) IIK-means clustering최적화동시 디자인스피링 질량 모델비지배 분류 유전알고리즘 IIK 평균 알고리즘
Abstract
In this thesis, using running leg model, co-design method will be suggested. This method will show hardware optimization considering software parameter. Using nondominated sort genetic algorithm II, K-means clustering, and pre-knowledge, optimization was performed. NSGA II is nonlinear global search method to find global minimum. Initializing population, evaluation, selection, crossover, and mutation are basic principles to avoid local minimum for multi objective function optimization problems. K-means clustering is method to extract important feature or compress data. Pre-knowledge is used to suggest evaluation equation using intuitive method about stability and performance. System validation was performed to validate suggested optimization process and find important design parameter. Using calculated design parameter, Conforming validation of design parameters was also performed, and hypothesis supported considering hardware and software simultaneously, and optimizing robot leg hardware could help controller in semi parallel design process. Though used method and model were simple and restrict, it showed support for importance of co-design using design parameter of running robot leg. ⓒ 2015 DGIST
Table Of Contents
1. INTRODUCTION 1-- 1.1 Background 1-- 1.2 Objective and Problem 2-- 1.3 Hypothesis 5-- 2. METHODS 10-- 2.1 Leg behavior model 10-- 2.2 Genetic algorithms 16-- 2.3 K-means clustering 24-- 2.4 Optimization process 26-- 3. RESULTS 28-- 3.1 System validations 28-- 3.1.1 System validations (1) – two variables 28-- 3.1.2 System validations (2) – three variables (initial phase: flight) 36-- 3.1.3 System validations (3) – three variables (initial phase: stance) 44-- 3.1.4 System validations (4) – five variables 51-- 3.2 Hypothesis validation 60-- 4. DISCUSSION 70-- 4.1 Discussion 70-- 5. CONCULSION 72-- 5.1 Conclusion 72-- APPENDICES 74-- Appendix 1 – Results of optimization 74-- System validations (1) – two variables 74-- System validations (2) – three variables (no additional energy) 75-- System validations (3) – three variables (additional energy) 77-- System validations (4) – five variables 79-- Appendix 2 – Simulation code (Matlab code) 80-- Running simulator 80-- NSGA II 85-- K-means clustering 85-- REFERENCES 86
URI
http://hdl.handle.net/20.500.11750/405
http://dgist.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001914397
DOI
10.22677/thesis.1914397
Degree
Master
Department
Robotics Engineering
University
DGIST
Files:
Collection:
Robotics EngineeringThesesMaster
Brain and Cognitive SciencesLaboratory of Neuronal Cell DeathETC

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