• Journal of Institute of Control, Robotics and Systems
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• v.14 no.6
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• pp.573-579
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• 2008

This paper introduces a new approach to develop a fast gait for quadruped robot using GP(genetic programming). Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multidimensional space. Several recent approaches have focused on using GA(genetic algorithm) to generate gait automatically and shown significant improvement over previous results. Most of current GA based approaches used pre-selected parameters, but it is difficult to select the appropriate parameters for the optimization of gait. To overcome these problems, we proposed an efficient approach which optimizes joint angle trajectories using genetic programming. Our GP based method has obtained much better results than GA based approaches for experiments of Sony AIBO ERS-7 in Webots environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.555-561
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• 2008

This Paper introduces new approach to develop fast and reliable gaits for quadruped robot using GA(genetic algorithm). Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multidimensional space. Recent approaches have problems to select proper parameters which are not known in advance and optimize more than ten to twenty parameters simultaneously. In our approach, the effects of major gait parameters are analysed and used to guide the search more efficiently. The experiments of Sony AIBO ERS-7 in Webots environment indicate that our approach is able to produce much improved results in fast velocity and reliability.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.389-394
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• 2014

Two evolutionary gait generation methods for Cartesian and Joint coordinates space are compared to develop a fast locomotion for quadruped robots. GA(Genetic Algorithm) based approaches seek to optimize a pre-selected set of parameters for the locus of paw and initial position in cartesian coordinates space. GP(Genetic Programming) based technique generate few joint trajectories using symbolic regression in joint coordinates space as a form of polynomials. Optimization for two proposed methods are executed using Webots simulation for the quadruped robot which is built by Bioloid. Furthermore, simulation results for two proposed methods are analysed in terms of different coordinate spaces.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.749-754
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• 2009

Two gait generation methods using GP(genetic programming) and CPG(Central Pattern Generator) are compared to develop a fast locomotion for quadruped robot. GP based technique is an effective way to generate few joint trajectories instead of the locus of paw positions and lots of stance parameters. The CPGs are neural circuits that generate oscillatory output from a input coming from the brain. Optimization for two proposed methods are executed and analysed using Webots simulation for the quadruped robot which is built by Bioloid. Furthermore, simulation results for two proposed methods are experimented in real quadruped robot and performances and motion features of GP and CPG based methods are investigated.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.248-250
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• 2008

본 논문은 GP(Genetic Programming)을 이용한 4족 보행 로봇의 새로운 걸음새 생성 방식에 대해 소개한다. 4족 보행로봇의 걸음새 생성문제는 다양한 파라미터를 통시에 최적화해야 하는 매우 어려운 문제이다. 본 논문에서는 GP를 기반으로 관절좌표계에서 로봇의 관절 궤석을 직접 제어하는 방식을 사용한다. 이는 기존의 특정한 형태의 발끝의 자취를 사용하는 방법들에 비해 효율적이며 구조적으로 제한피지 않는 특징을 가진다. 또한, 새로운 트리구조기반의 GP 연산자의 적용을 통해 더 좋은 결과를 얻을수 있었다.