• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1996년도 춘계학술대회논문집
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• pp.313-319
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• 1996

Human posture prediction and motion simulaiton methods try to solve inverse kinematic problems based on the optimization concept. It is of great concern to develop an optimization method which soloves complicated optimization models in an efficient way in order for the models to be biomechanically sound. In this study, a new optimization method for posture prediction, which is named the Complex Method, is presented. The Complex Method demonstrates more flexibility in a way that it can deal with various forms of objective functions with constraints. This is because the method is a function-value-based approach. A two-eimensional whole-body lifting task was selected as an example of posture prediction, and a comparison study with te incrementation method was conducted in order to evaluate the accuracy of the Complex Method.

• 한국산업융합학회 논문집
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• 제22권2호
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• pp.87-93
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• 2019

This research deals with posture optimization for humanoid robot against external forces using genetic algorithm and neural network. When the robot takes a motion to push an object, the torque of each joint is generated by reaction force at the palm. This study aims to optimize the posture of the humanoid robot that will change this torque. This study finds an optimized posture using a genetic algorithm such that torques are evenly distributed over the all joints. Then, a number of different optimized postures are generated from various the reaction forces at the palm. The data is to be used as training data of MLP(Multi-Layer Perceptron) neural network with BP(Back Propagation) learning algorithm. Humanoid robot can find the optimal posture at different reaction forces in real time using the trained neural network include non-training data.

• Journal of Electrical Engineering and Technology
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• 제10권6호
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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.

• 한국지능시스템학회논문지
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• 제24권4호
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• pp.450-456
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• 2014

휴머노이드 로봇은 인간-로봇 상호작용에 가장 효과적인 로봇 플랫폼이지만 20개 이상의 관절로 구성되어 있을 만큼 복잡한 구조여서 전통적인 역기구학적 방법으로 안정되면서도 정교한 자세를 생성하기가 어렵다. 본 논문에서는 고속 연산최적화 기법인 Particle Swarm Optimization 기법을 사용해서 앞쪽 지면에 놓인 물체를 단측지지 상태로 상체를 굽혀서 왼팔이나 오른팔로 집는 고난도의 자세를 생성하고, 이를 상용 휴머노이드 로봇 플랫폼에 적용하여 검증함으로써 제안 된 방법의 적용 가능성을 확인한다.

• 제어로봇시스템학회논문지
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• 제14권10호
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• pp.1029-1037
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• 2008

When a humanoid robot pushes an object with its force, it is essential to adequately control its posture so as to maximize the surplus torque far all joints. For such purpose, this study proposes a method to find an optimal posture of a humanoid robot using a genetic algorithm in such a way that the surplus torque for all joints is maximized. In this study, pushing motion of an 18-DOF humanoid robot is considered. When the robot takes a cooperative motion to push an object, the palms and soles are assumed to be fixed at the object and ground respectively, and are subjected to sense the reaction force from the object and the ground. Then, the torques for all joints are calculated and reflected to fitness function of the genetic algorithm. To verify the effectiveness of the proposed method, a number of simulations with different fitness functions are carried out. The simulation result shows that the proposed method can be adopted to find optimized posture in cooperative motion of a humanoid robot.

• 한국산업융합학회 논문집
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• 제26권1호
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• pp.113-119
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• 2023

In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.

• 한국운동역학회지
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• 제34권2호
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• pp.71-80
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• 2024

Objective: This study aimed to explore the brain connectivity between brain and biomechanical variables by exploring motion recognition through FFT (fast fourier transform) analysis and AI (artificial intelligence) focusing on quiet standing movement patterns. Method: Participants included 12 young adult males, comprising university students (n=6) and elite gymnasts (n=6). The first experiment involved FFT of biomechanical signals (f_CoP, f_AJtorque and f_EEG), and the second experiment explored the optimization of AI-based GRU (gated recurrent unit) using f_EEG data. Results: Significant differences (p<.05) were observed in frequency bands and maximum power based on group and posture types in the first experiment. The second study improved motion prediction accuracy through GRU performance metrics derived from brain signals. Conclusion: This study delved into the movement pattern of upright standing posture through the analysis of bio-signals linking the cerebral cortex to motor performance, culminating in the attainment of motion recognition prediction performance.

• 로봇학회논문지
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• 제18권3호
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• pp.241-250
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• 2023

This paper describes the posture stabilization control of a bipedal transformer robot being developed for military use. An inverted pendulum model with a rectangular that considers the robot's inertia is proposed, and a posture stabilization moment that can maintain the body tilt angle is derived by applying disturbance observer and state feedback control. In addition, vertical force and posture stabilization moments that can maintain the body height and balance are derived through QP optimization to obtain the necessary torques and vertical force for each foot. The roll and pitch angles of the IMU sensor attached to the robot's feet are reflected in the ankle joint to enable flexible adaptation to changes in ground inclination. Finally, the effectiveness of the proposed algorithm in posture stabilization is verified by comparing and analyzing the difference in body tilt angle due to disturbances and ground inclination changes with and without algorithm application, using Gazebo dynamic simulation and a down-scale test platform.

• 한국군사과학기술학회지
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• 제14권6호
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• pp.965-973
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• 2011

Small unmanned ground vehicles(SUGVs) are typically operational on unstructured environments such as crashed building, mountain area, caves, and so on. On those terrains, driving control can suffer from the unexpected ground disturbances which occasionally lead turnover situation. In this paper, we have proposed an algorithm which sustains driving stability of a SUGV as preventing from turnover. The algorithm exploits potential field method in order to determine the stability of the robot. Then, the flipper and manipulator posture of the SUGV is optimized from local optimization algorithm known as gradient descent method. The proposed algorithm is verified using 3D dynamic simulation, and results showed that the proposed algorithm contributes to driving stability of SUGV.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.345-348
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• 1997

In this paper, a motion control algorithm is developed using a fuzzy control and the optimization of performance function, which makes a robot arm avoid an unexpected obstacle when the end-effector of the robot arm is moving to the goal position. During the motion, if there exists no obstacle, the end-effecter of the robot arm moves along the pre-defined path. But if there exists an obstacle and close to the robot arm, the fuzzy motion controller is activated to adjust the path of the end-effector of the robot arm. Then, the robot arm takes the optimal posture for collision avoidance with the obstacle. To show the feasibility of the developed algorithm, numerical simulations are carried out with changing both the positions and sizes of obstacles. It was concluded that the proposed algorithm gives a good performance for obstacle avoidance.