• 제어로봇시스템학회논문지
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• 제10권9호
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• pp.833-839
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• 2004

This paper proposes a method that minimizes the consumed energy by searching the optimal locations of the mass centers of links composing of a biped robot using Real-Coded Genetic Algorithm. Generally, in order to utilize optimization algorithms, the system model and design variables must be defined. Firstly, the proposed model is a 6-DOF biped robot composed of seven links, since many of the essential characteristics of the human walking motion can be captured with a seven-link planar biped walking in the saggital plane. Next, Fourth order polynomials are used for basis functions to approximate the walking gait. The coefficients of the fourth order polynomials are defined as design variables. In order to use the method generating the optimal gait trajectory by searching the locations of mass centers of links, three variables are added to the total number of design variables. Real-Coded GA is used for optimization algorithm by reason of many advantages. Simulations and the comparison of three methods to generate gait trajectories including the GCIPM were performed. They show that the proposed method can decrease the consumed energy remarkably and be applied during the design phase of a robot actually.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1387-1391
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• 2003

The fundamental aim of this paper is to present a solution algorithm to achieve cooperative contour controlling, under joint acceleration constraint with maximum cooperative speed. Usually, the specifications like maximum velocity of cooperative trajectory are determined by the application itself. In resolving the cooperative trajectory into two complementary trajectories, an optimum task resolving strategy is employed so that the task assignment for each robot is fair under the joint acceleration constraint. The proposed algorithm of being an off-line technique, this could be effectively and conveniently extended to the existing servo control systems irrespective of the computational power of the controller implemented. Further, neither a change in hardware setup nor considerable reconfiguration of the existing system is required in adopting this technique. A simulation study has been carried out to verify that the proposed method can be realized in the generation of complementary trajectories so that they could meet the stipulated constraints in simultaneous maneuvering.

• 제어로봇시스템학회논문지
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• 제11권1호
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• pp.77-83
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• 2005

This paper deals with determination of motions of a humanoid robot using genetic algorithm. A humanoid robot has some problems of the structural instability basically. So, we have to consider the stable walking gait in gait planning. Besides, it is important to make the smoothly optimal gait for saving the electric power. A mobile robot has a battery to move autonomously. But a humanoid robot needs more electric power in order to drive many joints. So, if movements of walking joints don't maintain optimally, it is difficult for a robot to have working time for a long time. Also, if a gait trajectory doesn't have optimal state, the expected life span of joints tends to be decreased. To solve these problems, the genetic algorithm is employed to guarantee the optimal gait trajectory. The fitness functions in a genetic algorithm are introduced to find out optimal trajectory, which enables the robot to have the less reduced jerk of joints and get smooth movement. With these all process accomplished by a PC-based program, the optimal solution could be obtained from the simulation. In addition, we discuss the design consideration for the joint motion and distributed computation of the humanoid, ISHURO, and suggest its result such as the structure of the network and a disturbance observer.

• 한국산업융합학회 논문집
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• 제20권1호
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• pp.49-55
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• 2017

This study proposes a new approach to Control and trajectory generation of a 8 DOF human robot arm with computational complexity and singularity problem. To deal with such problems, analytical methods for a redundant robot arm have been researched to enhance the performance of research, we propose an analytical kinematics algorithm for a 8 DOF bipped dual robot arm. Using this algorithm, it is possible to generate a trajectory passing through the singular points and intuitively move the elbow without regarding to the end-effector pose. Performance of the proposed algorithm was verified by simulation test with various conditions. It has been verified that the trajectory planning using this algorithm.

• 한국항공우주학회지
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• 제49권4호
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• pp.263-272
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• 2021

본 논문은 외장 분리 풍동시험 기법을 적용한 전산유체해석을 통하여 획득한 데이터와 풍동시험을 통하여 획득한 데이터를 비교 연구한 것이다. 전산유체해석은 하모닉 방정식을 적용한 비정상해석 기법을 사용하여 수행하였으며, 비정상 해석으로부터 외장의 공력계수들과 6 자유도 외장 분리 시뮬레이션을 위한 공력 데이터베이스를 생성하였다. 해당 데이터베이스와 풍동시험 기반 데이터베이스를 이용한 외장의 분리 궤적 시뮬레이션 수행하였으며, 그 결과를 비행시험 결과와 비교하였다. 비교를 통하여 시뮬레이션의 적절성을 확인하였으며, 외장 분리 풍동시험 기법을 전산유체해석에 적용하여 획득한 외장 분리 공력 데이터베이스는 외장분리 궤적 시뮬레이션 적용에 타당함을 확인하였다.

• 자동차안전학회지
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• 제11권3호
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.969-981
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• 2018

Recent advances in machine learning approaches such as deep neural network and reinforcement learning offer significant performance improvements in generating detailed and varied motions in physically simulated virtual environments. The optimization methods are highly attractive because it allows for less understanding of underlying physics or mechanisms even for high-dimensional subtle control problems. In this paper, we propose an efficient learning method for stochastic policy represented as deep neural networks so that agent can generate various energetic motions adaptively to the changes of tasks and states without losing interactivity and robustness. This strategy could be realized by our novel trajectory search method motivated by the trust region policy optimization method. Our value-based trajectory smoothing technique finds stably learnable trajectories without consulting neural network responses directly. This policy is set as a trust region of the artificial neural network, so that it can learn the desired motion quickly.

• Transactions on Control, Automation and Systems Engineering
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• 제3권4호
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• pp.272-282
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• 2001

Uncertainties are the main reasons of deterioration of contour control of industrial articulated robot arm. In this paper, a high-precision contour control method was proposed to overcome some main uncertainties, such as torque saturation, system delay dynamics, interference between robot links, friction, and so on. Firstly, each considered factor of uncertainties was introduced briefly. Then proper realizable objective trajectory generation was presented to avoid torque saturation from objective trajectory. According to the model of industrial articulated robot arm, construction of Gaussian neural network controller with considering system delay dynamic, interference between robot links and friction was explained in detail. Finally, through the experiment and simulation, the effectiveness of proposed method was verified. Furthermore, based on the results it was shown that the Gaussian neural network controller can be also adapted for the various kinds of friction and high-speed motion of industrial articulated robot arm.

• 반도체디스플레이기술학회지
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• 제21권2호
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• pp.1-6
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• 2022

This computational investigation of micro-sized particle dispersion concerns the fume particle contamination over target surface in high-precision laser line machining process of semiconductor and display device materials. Employing the random sampling based on probabilistic fume particle generation distributions, the effects of sphericity for nonspherical fume particles are analyzed for the fume particle dispersion and contamination near the laser machining line. The drag coefficient correlation for nonspherical particles in a low Reynolds number regime is selected and utilized for particle trajectory simulations after drag model validation. When compared to the corresponding results by the assumption of spherical fume particles, the sphericity of nonspherical fume particles show much less dispersion and contamination characteristics and it also significantly affects the particle removal rate in a suction air flow patterns.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.401-412
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• 2008

This paper discusses a design methodology of cooperative path planning for dynamical multi-agent systems with spatial and temporal constraints. The cooperative behavior of the multi-agent systems is specified in terms of the objective function in an optimization formulation. The path of achieving cooperative tasks is then generated by the optimization formulation constructed based on a differential flatness approach. Three scenarios of multi-agent tasking are proposed at the cooperative task planning framework. Given agent dynamics, both spatial and temporal constraints are considered in the path planning. The path planning algorithm first finds trajectory curves in a lower-dimensional space and then parameterizes the curves by a set of B-spline representations. The coefficients of the B-spline curves are further solved by a sequential quadratic programming solver to achieve the optimization objective and satisfy these constraints. Finally, several illustrative examples of cooperative path/task planning are presented.