• 대한전기학회논문지
• /
• 제39권10호
• /
• pp.1121-1232
• /
• 1990

A hierarchical neural network structure is described for robot PTP trajectory planning. In the first level, the multi-layered Perceptron neural network is used for the inverse kinematics with the back-propagation learning procedure. In the second level, a saccade generation model based joint trajectory planning model in proposed and analyzed with several features. Various simulations are performed to investigate the characteristics of the proposed neural networks.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1989년도 하계종합학술대회 논문집
• /
• pp.679-684
• /
• 1989

In this paper, we describe the PTP notion of robot manipulators by neural networks. The PTP motion requires the inverse kinematic redline and the joint trajectory generation algorithm. We use the multi-layered Perceptron neural networks and the Error Back Propagation(EBP) learning rule for inverse kinematic problems. Varying the number of hidden layers and the neurons of each hidden layer, we investigate the performance of the neural networks. Increasing the number of learning sweeps, we also discuss the performance of the neural networks. We propose a method for solving the inverse kinematic problems by adding the error compensation neural networks(ECNN). And, we implement the neural networks proposed by Grossberg et al. for automatic trajectory generation and discuss the problems in detail. Applying the neural networks to the current trajectory generation problems, we can refute the computation time for trajectory generation.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
• /
• pp.20-23
• /
• 1996

This paper deals with the flexible manipulator with rotational and translational degrees of freedom, which has an arm of time-varying length with the prismatic joint. The tracking control problem of the flexible manipulator is considered. First we design the controller of the 2-type robust servo system based on the finite horizon optimal control theory for the trajectory planned as a discontinuous velocity. Next, to reduce the tracking error, we use the method of the dynamic programming and of modifying the reference trajectory in time coordinate. The simulation results show that the dynamic modeling is adequate and that the asymptotic stabilization of the flexible manipulator is preserved in spite of nonlinear terms. The PTP control error has been reduced to zero completely, and the trajectory tracking errors are reduced sufficiently by the proposed control method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권7호
• /
• pp.2321-2338
• /
• 2021

Compared with vehicle trajectories, pedestrian trajectories have stronger degrees of freedom and complexity, which poses a higher challenge to trajectory prediction tasks. This paper designs a mode to divide the trajectory of pedestrians at a traffic intersection, which converts the trajectory regression problem into a trajectory classification problem. This paper builds a deep model for pedestrian trajectory prediction at intersections for the task of pedestrian short-term trajectory prediction. The model calculates the spatial correlation and temporal dependence of the trajectory. More importantly, it captures the interactive features among pedestrians through the Attention mechanism. In order to improve the training speed, the model is composed of pure convolutional networks. This design overcomes the single-step calculation mode of the traditional recurrent neural network. The experiment uses Vulnerable Road Users trajectory dataset for related modeling and evaluation work. Compared with the existing models of pedestrian trajectory prediction, the model proposed in this paper has advantages in terms of evaluation indicators, training speed and the number of model parameters.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
• /
• pp.65-68
• /
• 1995

This paper describes trajectory generation of a riobot arm by self-organizing neural networks. These neural networks are based on competitive learning without a teacher and this algorithm which is suitable for problems in which solutions as teaching signal cannot be defined-e.g. inverse dynamics analysis-is adopted to the trajectory generation problem of a robot arm. Utility of unsupervised learning algorithm is confirmed by applying the approximated solution of each joint calculated through learning to an actual robot arm in giving the experiment of tracking for reference trajectory.

• 한국콘텐츠학회논문지
• /
• 제15권8호
• /
• pp.46-52
• /
• 2015

공연장의 거대한 무대 세트나 장치에 움직임이 부여되면, 다양한 극적 효과를 연출할 수 있다. 본 논문에서 제안하는 프로그래밍 가능한 다축 체인 호이스트 서버 시스템은 이러한 연출의 자동화를 가능하게 한다. 본 시스템에는 PTP 기반의 임의 궤적 생성 방법, 다축 실시간 통신 제어 방법, 그리고 4 단계의 순차적인 안전 진단 알고리즘이 적용 되었으며, 호이스트들은 콘솔의 동기 제어에 의하여 시나리오의 연출 순서에 따라 자동적으로 움직인다. 12대 체인호이스트로 구성된 멀티 체인 호이스트 서버 시스템에 대한 1 ton 부하 및 대칭-비대칭 배치 부하에 대한 실험을 통해서 우수성을 검증하였다. 아울러 K-POP 공연에서 극적효과를 연출하는데 성공적으로 적용되었다.