• 전기학회논문지
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• 제63권5호
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• pp.683-689
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• 2014

The paper introduces dynamic generation technique of foot trajectories for humanoid robots using CPG(Central Pattern Generator) and proposes adaptive walking method for slope terrains combining a feedback network. The proposed CPG based technique generates the trajectory of foot in the Cartesian coordinates system and it can change the step length adaptively according to the feedback information. To cope with variable slope terrains, the sensory feedback network in the CPG are designed using the geometry relationship between foot position and body center position such that humanoid robot can maintain its stability. To demonstrate the effectiveness of the proposed approach, the experiments on humanoid robot Nao are executed in the Webot simulation. The performance and motion features of the CPG based approach are compared and analyzed focusing on the adaptability in slope terrains.

• 한국지능시스템학회논문지
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• 제23권4호
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• pp.360-365
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• 2013

본 연구에서는 발끝 궤적을 미리 설계하지 않고, CPG(Central Pattern Generator)를 이용하여 동적으로 생성할 수 있는 기법을 제안한다. 생성된 발끝 궤적은 CPG 의 진동적인 출력에 따라 가변적인데, 이는 발끝 궤적이 CPG 진동적인 출력 신호의 맵핑 함수로 주어지기 때문이다. 이를 통해 환경에 적응적인 궤적을 생성할 수 있는 토대를 마련할 수 있다. 제안된 기법의 효율성을 검증하기 위해서, Webots 시뮬레이션을 통해 휴머노이드 로봇 Nao에 대한 실험을 수행하고, 성능과 동작 특성을 분석한다.

• 전기학회논문지
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• 제67권5호
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• pp.663-668
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• 2018

A combined CPG (Central Pattern Generator) based foot trajectory and GA (Genetic Algorithm) based joint compensation method is presented for adaptive humanoid walking. In order to increase an adaptability of humanoid walking for rough terrains, the experiment for rolling terrains are introduced. The CPG based foot trajectory method has been successfully applied to basic slops and variable slops, but has a limitation for the rolling terrains. The experiments are conducted in an ODE based Webots simulation environment using humanoid robot Nao to verify a stability of walking for various rolling terrains. The proposed method is compared to the previous CPG foot trajectory technique and shows better performance especially for the cascade rolling terrains.

• 전기학회논문지
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• 제64권7호
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• pp.1040-1046
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• 2015

This paper introduces GP(Genetic Programming) based color detection model for an object detection of humanoid robot vision. Existing color detection methods have used linear/nonlinear transformation of RGB color-model. However, most of cases have difficulties to classify colors satisfactory because of interference of among color channels and susceptibility for illumination variation. Especially, they are outstanding in degraded images from robot vision. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various environments in robot vision for real humanoid Nao.

• 전기학회논문지
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• 제65권9호
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• pp.1551-1556
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• 2016

A combined CPG (Central Pattern Generator) based foot trajectory and GP (Genetic Programming) based joint compensation method is presented for the adaptive humanoid walking. The CPG based foot trajectory methods have been successfully applied to basic slops and variable slops with slow rates, but have a limitation for the steep slop terrains. In order to increase an adaptability of humanoid walking for the rough terrains, a GP based joint compensation method is proposed and combined to the CPG (Central Pattern Generator) based foot trajectory method. The experiments using humanoid robot Nao are conducted in an ODE based Webots simulation environmemt to verify a stability of walking for the various aslope terrains. The proposed method is compared to the previous CPG foot trajectory technique and shows better performances especially for the steep varied slopes.

• 로봇학회논문지
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• 제10권2호
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• pp.90-95
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• 2015

The main goal of e-learning systems is just-in-time knowledge acquisition. Rule-based e-learning systems, however, suffer from the mesa effect and the cold start problem, which both result in low user acceptance. E-learning systems suffer a further drawback in rendering the implementation of a natural interface in humanoids difficult. To address these concerns, even exceptional questions of the learner must be answerable. This paper aims to propose a method that can understand the learner's verbal cues and then intelligently explore additional domains of knowledge based on crowd data sources such as Wikipedia and social media, ultimately allowing for better answers in real-time. A prototype system was implemented using the NAO platform.

• ETRI Journal
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• 제40권4호
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• pp.511-521
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• 2018

The ability to realize human-motion imitation using robots is closely related to developments in the field of artificial intelligence. However, it is not easy to imitate human motions entirely owing to the physical differences between the human body and robots. In this paper, we propose a work chain-based inverse kinematics to enable a robot to imitate the human motion of upper limbs in real time. Two work chains are built on each arm to ensure that there is motion similarity, such as the end effector trajectory and the joint-angle configuration. In addition, a two-phase filter is used to remove the interference and noise, together with a self-collision avoidance scheme to maintain the stability of the robot during the imitation. Experimental results verify the effectiveness of our solution on the humanoid robot Nao-H25 in terms of accuracy and real-time performance.