• 한국정밀공학회지
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• 제15권11호
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• pp.137-144
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• 1998

In this paper we present our research dealing with the problem of tool deflection during the milling. We try to compensate the errors by considering a new tool trajectory. In order to determine the compensated tool trajectory, the problem is divided in three steps : cutting forces model, tool deflection model and trajectory compensation. Starting from experimental data, we determine a cutting forces model., which allows us to anticipate the tool deflection along one nominal path. In order to determine the compensated tool trajectory, we propose in this paper a method of path compensation, called “mirror method”. This method of tool path optimization allows to minimize errors due to tool deflection. Several examples are processed in simulations and validated experimentally.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 심포지엄 논문집 정보 및 제어부문
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• pp.75-76
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• 2008

In oder for the robot to walk with stability, trajectory generation method for the biped robot is important. In this paper proposed the genetic algorithm to optimize biped robot motion parameters. Because most of trajectory generation, the walking parameters determined arbitrarily. Formulating the constraints of the motion parameters, and the trajectory is derived by cubic spline function. Finally walking patterns are described through simulation studies. When the ZMP(zero moment point) and DSM(dynamic stability margin) are satisfied, the walking pattern is chosen.

• 한국정밀공학회지
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• 제13권6호
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• pp.122-127
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• 1996

The achievement of the optimal condition for the task of an industrial articulated robot used in many fields is an important problem to improve productivity. In this paper, a minimum-time trajectory for an articulated robot along the specified path is studied and simulated with a proper example. A general dynamic model of manipulator is represented as a function of path distance. Using this model, the velocity is produced as fast as possible at each point along the path. This minimum-time trajectory planning module together with the existing collision-free path planning modules is utilized to design the optimal path planning of robot in cases where obstacles present.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2004년도 한국우주과학회보 제13권2호
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• pp.285-289
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• 2004

An optimal interplanetary trajectory is presented for Human Outer Planet Exploration (HOPE) by using an advanced magnetoplasma spacecraft. A detailed optimization approach is formulated to utilize Variable Specific Impulse Magnetoplasma Rocket (VASIMR) engine with capabilities of variable specific impulse, variable engine efficiency, and engine on-off control. To design a round-trip trajectory for the mission, the characteristics of the spacecraft and its trajectories are analyzed. It is mainly illustrated that 30 MW powered spacecraft can make the mission possible in five-year round trip constraint around year 2045. The trajectories obtained in this study can be used for formulating an overall concept for the mission.

• 전기학회논문지
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• 제57권6호
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• pp.915-923
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• 2008

This paper focuses on optimal tuning of nonlinear parameters of a dual-input power system stabilizer(dual-input PSS), which can improve the system damping performance immediately following a large disturbance. Until recently, various PSS models have developed to bring stability and reliability to power systems, and some of these models are used in industry applications. However, due to non-smooth nonlinearities from the interaction between linear parameters(gains and time constants of linear controllers) and nonlinear parameters(saturation output limits), the output limit parameters cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures('trial and error' approach) have been used. Therefore, the steepest descent method is applied to implement the optimal tuning of the nonlinear parameters of the dual-input PSS. The gradient required in this optimization technique can be computed from trajectory sensitivities in hybrid system modeling with the differential-algebraic-impulsive-switched(DAIS) structure. The optimal output limits of the dual-input PSS are evaluated by time-domain simulation in both a single machine infinite bus(SMIB) system and a multi-machine power system in comparison with those of a single-input PSS.

• 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.

• 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.

• 대한전자공학회논문지
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• 제27권10호
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• pp.44-52
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• 1990

공간상에 움직여야할 길이 주어진 두 대의 로보트의 속도계획 문제를 두 단계의 부문제로 나누어 각각의 독립적으로 다루는 방법을 도입하여 최소시간 속도계획 방법을 제안하였다. 제안된 방법은 i) 로보트가 각자의 길을 따라 이동한 거리를 두 축으로하는 코오디네이션 공간이라는 2차원 공간을 구축하고 이공간상에서 충돌을 피할 수 있도록 코오드네이션 곡선을 선정하고, ii)선정된 곡선을 따라 최소시간을 보장하는 속도계획을 하는 두 단계의 문제중 두 번째 단계의 문제에 대해 로보트의 동력학 및 각 관절의 최대 회전속도등을 고려하여 최적해를 구하는 방법을 제안하였다. 또한 신경 최적화 회로망의 이론을 응용하여 간단한 반복 계산 알고리듬으로 최적해를 구하였다. 제안된 방법은 두 대의 SCARA형 로보트의 경로계획의 예로 시뮬레이션하여 유용성을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.903-921
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• 2024

Industry 5.0 puts forward higher requirements for smart cities, including low-carbon, sustainable, and people-oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.215-219
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• 2003

In this paper, we generate a trajectory minimized the energy gait of a biped robot for walking a staircase using genetic algorithms and apply to the computed torque controller for the stable dynamic biped locomotion. In the saggital plane, a 6 degree of freedom biped robot that model consists of seven links is used. In order to minimize the total energy efficiency, the Real-Coded Genetic Algorithm (RCGA) is used. Operators of genetic algorithms are composed of a reproduction, crossover and mutation. In order to approximate the walking gait, the each joint angle is defined as a 4-th order polynomial of which coefficients are chromosomes. Constraints are divided into equality and inequality. Firstly, equality constraints consist of position conditions at the end of stride period and each joint angle and angular velocity condition for periodic walking. On the other hand, inequality constraints include the knee joint conditions, the zero moment point conditions for the x-direction and the tip conditions of swing leg during the period of a stride for walking a staircase.