• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.360-365
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• 2013

The paper introduces dynamic generation technique of foot trajectories using CPG(Central Pattern Generator). In this approach, the generated foot trajectories can be changeable according to variable outputs of CPG in various environments, because they are given as mapping functions of the output signals of the CPG oscillators. It enables to provide an adaptable foot trajectory for environmental change. To demonstrate the effectiveness of the proposed approach, experiments on humanoid robot Nao is executed in the Webot simulation. The performance and motion features of CPG based approach is analyzed.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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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.

• The Journal of Korean Physical Therapy
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• v.14 no.4
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• pp.131-146
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• 2002

In the last years, it has become possible to regain some locomotor activity in patients with incomplete spinal cord injury (SCI) through intense training on a treadmill. The ideas behind this approach owe much to insights derived from animal studies. Many studies showed that cats with complete spinal cord transection(spinalized animals) can recover locomotor function. These observations were at the basis of the concept of the central pattern generator located at spinal level. The neural system responsible for the locomotor restoration in both cats and humans is thought to be located at spinal level and is referred to as the central pattern generator(CPG). The evidence for such a spinal CPG in human is emphasis on some recent developments which support the view that there is a human spinal CPG for locomotion. An important element in afferent inputs for both spinal injured cats and humans is the provision of adequate sensory input related locomotor, which can possibly activate and/or regulate the spinal locomotor circuitry This review article deals with the afferent control of the central pattern generator. Furthermore, the application of adequate afferent inputs related locomotor for stroke patients will be able to facilitate locomotion ability, which is automatic, cyclic, rhythmic. These insights can possibly contribute to a better therapeutic approach for the rehabilitation of gait in patients with stroke.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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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.

• The Journal of Korean Physical Therapy
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• v.17 no.1
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• pp.108-122
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• 2005

The gait training strategy in very important things for central nervous system(CNS) injury patients. There are many method and strategy for regaining of the gait who had CNS injury. A human being has central pattern generator(CPG) is spinal CPG for locomotion. It is a neural network which make the cyclical patterns and rhythmical activities for walking. Sensory input from loading and hip position is essential for CPG stimulation that makes the central neural rhythm and pattern generating structure. From sensory input, the proprioceptive information facilitate proximal muscles that controlled in voluntarily from cortical level and visual and / or acoustical information facilitate distal muscles that controlled voluntarily from subcortical level. Gait training method can classify that is functional level and structural level. Functional level includ level surface gait, going up and down the stair. It is important to facilitate a guide tempo in order to activate the central pattern generators. During the functional test or functional activities, can point out the poor period in gait that have to be facilitate in structural level. There are many access methods with patient position and potentiality. The methods are using of rhythmic initiation, replication and combination of isotonic with standing position. Clinically using it on weight transfer onto the stance leg, loading response, loading response and pre-swing, terminal stance, up and downwards stairs.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2420-2426
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• 2015

This paper proposes an evolutionary optimization approach for optimal hopping of humanoid robots. In the proposed approach, the hopping trajectory is generated by a central pattern generator (CPG). The CPG is one of the biologically inspired approaches, and it generates rhythmic signals by using neural oscillators. During the hopping motion, the disturbance caused by the ground reaction forces is compensated for by utilizing the sensory feedback in the CPG. Posture control is essential for a stable hopping motion. A posture controller is utilized to maintain the balance of the humanoid robot while hopping. In addition, a compliance controller using a virtual spring-damper model is applied for stable landing. For optimal hopping, the optimization of the hopping motion is formulated as a minimization problem with equality constraints. To solve this problem, two-phase evolutionary programming is employed. The proposed approach is verified through computer simulations using a simulated model of the small-sized humanoid robot platform DARwIn-OP.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.870-875
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• 2008

The main objective of this paper is to control a trajectory tracking of the fish-mimetic robot by CPG (Central Pattern Generator), which is biological approach. CPG is biological neural networks that generate rhythmic movements for locomotion of animals, such as walking, running, swimming and flying. Animals show marvelous ability of autonomous dynamic adaptation for an unsteady fluid dynamic environment or various environments. So, we propose the 3-DOF CPG controller to track the trajectory of the fish robot in plane motion. The conformity of the proposed control algorithm is validated by simulation for a fish robot model, which is made by a commercial dynamic package.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.151-152
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• 2009

본 논문에서는 진화연산 기법중 GP(Genetic Programming)를 이용한 관절좌표계 상에서의 걸음새 자동생성 기법과 생물체의 신경발생 신호 원리를 이용한 CPG(Central Pattern Generator) 기법을 구현한다. 바이올로이드로 구성된 4족 보행로봇에 대하여 Webots기반의 ODE 시뮬레이션을 통해 접근 기법들에 대한 최적화를 수행하고 결과를 비교 분석한다. 그리고 구해진 시뮬레이션과 결과를 실제 로봇에 대해서 각 동작을 실행시켜 보면서 CPG와 GP 기반 걸음새 방식 실제적인 성능 및 특성도 고찰한다.