• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1136-1139
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• 1996

For on-line trajectory planning such as teleoperation it is desirable to keep good manipulability of the robot manipulators since the motion command is not given in advance. To keep good manipulability means the capability of moving any arbitrary directions of task space. An optimization process with different manipulability measures are performed and compared for a redundant robot system moving in 2-dimensional task space, and gives results that the conventional manipulability ellipsoid based on the Jacobian matrix is not good choice as far as the optimal direction of motion is concerned.

• 제어로봇시스템학회논문지
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• 제13권11호
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• pp.1137-1145
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• 2007

Palletizing tasks are necessary to promote efficient storage and shipping of boxed products. These tasks, however, involve some of the most monotonous and physically demanding labor in the factory. Thus, many types of robot palletizing systems have been developed, although many robot motion commands still depend on the teach pendant. That is, the operator inputs the motion command lines one by one. This is very troublesome and, most importantly, the user must know how to type the code. We propose a new GUI(Graphic User Interface) for the palletizing system that is more convenient. To do this, we used the PLP "Fast Algorithm" and 3-D auto-patterning visualization. The 3-D patterning process includes the following steps. First, an operator can identify the results of the task and edit them. Second, the operator passes the position values of objects to a robot simulator. Using those positions, a palletizing operation can be simulated. We chose a widely used industrial model and analyzed the kinematics and dynamics to create a robot simulator. In this paper we propose a 3-D patterning algorithm, 3-D robot-palletizing simulator, and modified trajectory generation algorithm, an "overlapped method" to reduce the computing load.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.814-819
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• 2007

Palletizing task is well-known time consuming and laborious process in factory, hence automation is seriously required. To do this, artificial robot is generally used. These systems however, mostly user teaches the robot point to point and to avoid time-variable obstacle, robot is required to attach the vision camera. These system structures bring about inefficiency and additional cost. In this paper we propose task-oriented trajectory generation algorithm for palletizing. This algorithm based on $A^{*}$ algorithm and slice plane theory, and modify the object dealing method. As a result, we show the elapsed simulation time and compare with old method. This simulation algorithm can be used directly to the off-line palletizing simulator and raise the performance of robot palletizing simulator not using excessive motion area of robot to avoid adjacent components or vision system. Most of all, this algorithm can be used to low-level PC or portable teach pendent

• 한국산업융합학회 논문집
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• 제21권3호
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• pp.125-131
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• 2018

In this paper, we propose a homogeneous multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments with moving obstacles using multi-ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as real experiments with mobile robot, AmigoBot. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• 대한전기학회논문지
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• 제39권4호
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• pp.403-413
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• 1990

A collision free trajectory control for multiple mobile robots in obstacle-resident workspace is proposed. The proposed method is based on the concept of neural optimization network which has been applied to such problems which are too complex to be handled by traditional analytical methods, and gives good adaptibility for unpredictable environment. In this paper, the positions of the mobile robot are taken as the variables of the neural circuit and the differential equations are derived based on the performance index which is the weighted summation of the functions of the distances between the goal and current position of each robot, between each pair of robots and between the goal and current position of each robot, between each pair of robots and between obstacles and robots. Also is studied the problem of local minimum and of detour in large radius around obstacles, which is caused by inertia of mobile robots. To show the validity of the proposed method an example is illustrated by computer simulation, in which 6 mobile robots with mass and friction traverse in a workspace with 6 obstacles.

• 한국산학기술학회논문지
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• 제16권4호
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• pp.2370-2378
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• 2015

본 논문에서는 로봇 매니퓰레이터의 토크 최소화를 위한 궤적계획을 위해 라그랑지 보간법을 이용한 Algorithm을 제안하였다. 이를 위해 로봇 매니퓰레이터의 위치에 대한 구속조건이 주어지고 안정성이 보장되어야 한다. 라그랑지 보간법의 Runge's 현상을 회피하기 위해 Chebyshev 보간점을 이용하여 시간 보간점을 설정하였고, 이에 대응하는 최적각도를 찾아내어 라그랑지 보간법을 이용한 매끄러운 관절의 각도, 속도, 가속도 궤적을 얻을 수 있다. 로봇 매니퓰레이터의 토크 소비 최적화를 위한 성능지표를 선정하였으며, 계산된 궤적을 통해 이 성능지표가 최소값을 가지도록 반복 계산하는 과정을 거친다. 이를 통해, 토크와 성능지표를 최소화 시키는 최적의 궤적을 얻을 수 있으며, 로봇 매니퓰레이터가 작업을 수행하기 위한 움직임의 안전성을 보장한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.310-316
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• 1986

The problem of finding an allowable object trajectory for a cooperating two-robot system is investigated. The method proposed in this paper is based on reformulating the problem as a nonlinear optimization problem with equality constants in terms of the joint variables. The optimization problem is then solved numerically on a computer. The solution automatically gives the corresponding joint variable trajectories as well, thus eliminating the need for solving the inverse kinematic problem. The method has been succesfully applied to an experimental system.

• EDISON SW 활용 경진대회 논문집
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• 제6회(2017년)
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• pp.548-552
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• 2017

본 논문에서는 얀센 메커니즘을 활용한 보행 로봇의 궤적을 최적화 하기 위한 알고리즘을 연구하였다. 궤적의 최적화 목표는 지면에 닿는 시간이 길고 지면에 평행하며 빠른 이동을 위해 넓은 보폭을 생성 하는 것으로 두었다. 초기 값은 Edison design의 m.sketch를 사용하여 결정하였고 최적화 과정에서는 MATLAB을 사용하였으며 가능한 빠른 계산이 가능한 것에 초점을 두고 알고리즘을 작성하였다. 최적화된 결과 값에서는 지면에 닿는 궤적의 범위와 보폭의 크기, 궤적의 높이가 가장 큰 값을 결정하였다.

• 로봇학회논문지
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• 제18권2호
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• pp.143-154
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• 2023

In this study, a fast motion planning method for the swing motion of a 6x6 wheel-legged robot to traverse large obstacles and gaps is proposed. The motion planning method presented in the previous paper, which was based on trajectory optimization, took up to tens of seconds and was limited to two-dimensional, structured vertical obstacles and trenches. A deep neural network based on one-dimensional Convolutional Neural Network (CNN) is introduced to generate keyframes, which are then used to represent smooth reference commands for the six leg angles along the robot's path. The network is initially trained using the behavioral cloning method with a dataset gathered from previous simulation results of the trajectory optimization. Its performance is then improved through reinforcement learning, using a one-step REINFORCE algorithm. The trained model has increased the speed of motion planning by up to 820 times and improved the success rates of obstacle crossing under harsh conditions, such as low friction and high roughness.