• Title/Summary/Keyword: learning trajectory

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Robot PTP Trajectory Planning Using a Hierarchical Neural Network Structure (계층 구조의 신경회로망에 의한 로보트 PTP 궤적 계획)

  • 경계현;고명삼;이범희
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.39 no.10
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    • pp.1121-1232
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    • 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.

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Learning the Covariance Dynamics of a Large-Scale Environment for Informative Path Planning of Unmanned Aerial Vehicle Sensors

  • Park, Soo-Ho;Choi, Han-Lim;Roy, Nicholas;How, Jonathan P.
    • International Journal of Aeronautical and Space Sciences
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    • v.11 no.4
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    • pp.326-337
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    • 2010
  • This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

A Study on the PTP Motion of Robot Manipulators by Neural Networks (신경 회로망에 의한 로보트 매니퓰레이터의 PTP 운동에 관한 연구)

  • Kyung, Kye-Hyun;Ko, Myoung-Sam;Lee, Bum-Hee
    • Proceedings of the KIEE Conference
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    • 1989.07a
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    • pp.679-684
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    • 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.

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Adaptability Improvement of Learning from Demonstration with Particle Swarm Optimization for Motion Planning (운동계획을 위한 입자 군집 최적화를 이용한 시범에 의한 학습의 적응성 향상)

  • Kim, Jeong-Jung;Lee, Ju-Jang
    • Journal of the Korean Society of Industry Convergence
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    • v.19 no.4
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    • pp.167-175
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    • 2016
  • We present a method for improving adaptability of Learning from Demonstration (LfD) strategy by combining the LfD and Particle Swarm Optimization (PSO). A trajectory generated from an LfD is modified with PSO by minimizing a fitness function that considers constraints. Finally, the final trajectory is suitable for a task and adapted for constraints. The effectiveness of the method is shown with a target reaching task with a manipulator in three-dimensional space.

Design of DNP Controller for Robust Control Auto-Systems (DNP에 의한 자동화 시스템의 강인제어기 설계)

  • 김종옥;조용민;민병조;송용화;조현섭
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 1999.11a
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    • pp.121-126
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    • 1999
  • In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

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Iterative Learning Control for Industrial Robot Manipulators (반복 학습 알고리즘을 이용한 산업용 로봇의 제어)

  • Ha, Tae-Jun;Yeon, Je-Sung;Park, Jong-Hyeon;Son, Seung-Woo;Lee, Sang-Hun
    • Proceedings of the KSME Conference
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    • 2008.11a
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    • pp.745-750
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    • 2008
  • Uncertain dynamic parameters and joint flexibility have been problem to control robot manipulator precisely. Hence, even if the controller tracks the desired trajectory well with the feedback of the motor encoders, it is hard to achieve the desired behavior at the end-effector. In this paper, robot trajectory is taught by a general heuristic iterative learning control (ILC) algorithm in order to reduce tracking error of the tool center point (TCP) and the results of tracking with 6 DOF industrial robot manipulator are presented. The performance is verified based on ISO 9283.

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Design of DNP Controller for Robust Control of Auto-Equipment Systems (자동화 설비시스템의 강인제어를 위한 DNP 제어기 설계)

  • ;趙賢燮
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.13 no.2
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    • pp.187-187
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    • 1999
  • in order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment system is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulation are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

A computed-error-input based learning scheme for multi-robot systems

  • Kuc, Tae-Yong
    • 제어로봇시스템학회:학술대회논문집
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    • 1995.10a
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    • pp.518-521
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    • 1995
  • In this paper, a learning control problem is formulated for cooperating multiple-robot manipulators with uncertain system parameters. The commonly held object is also assumed to be unknown and the multiple-robots themselfs experience uncertain operating conditions such as link parameters, viscous friction parameters, suctions, actuator bias, and etc. Under these conditions, the learning controllers designed for learning of uncertain parameters and robot control inputs for multiple-robot systems are shown to drive the multiple-robot manipulators to follow the desired Cartesian trajectory with the desired internal forces to the unknown object.

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En-route Trajectory Prediction via Weighted Linear Regression (가중선형회귀를 통한 순항항공기의 궤적예측)

  • Kim, Soyeun;Lee, Keumjin
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.24 no.4
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    • pp.44-52
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    • 2016
  • The departure flow management is the planning tool to optimize the schedule of the departure aircraft and allows them to join smoothly into the overhead traffic flow. To that end, the arrival time prediction to the merge point for the cruising aircraft is necessary to determined. This paper proposes a trajectory prediction model for the cruising aircraft based on the machine learning approach. The proposed method includes the trajectory vectored from the procedural route and is applied to the historical data to evaluate the prediction performances.

Learning Optimal Trajectory Generation for Low-Cost Redundant Manipulator using Deep Deterministic Policy Gradient(DDPG) (저가 Redundant Manipulator의 최적 경로 생성을 위한 Deep Deterministic Policy Gradient(DDPG) 학습)

  • Lee, Seunghyeon;Jin, Seongho;Hwang, Seonghyeon;Lee, Inho
    • The Journal of Korea Robotics Society
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    • v.17 no.1
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    • pp.58-67
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    • 2022
  • In this paper, we propose an approach resolving inaccuracy of the low-cost redundant manipulator workspace with low encoder and low stiffness. When the manipulators are manufactured with low-cost encoders and low-cost links, the robots can run into workspace inaccuracy issues. Furthermore, trajectory generation based on conventional forward/inverse kinematics without taking into account inaccuracy issues will introduce the risk of end-effector fluctuations. Hence, we propose an optimization for the trajectory generation method based on the DDPG (Deep Deterministic Policy Gradient) algorithm for the low-cost redundant manipulators reaching the target position in Euclidean space. We designed the DDPG algorithm minimizing the distance along with the jacobian condition number. The training environment is selected with an error rate of randomly generated joint spaces in a simulator that implemented real-world physics, the test environment is a real robotic experiment and demonstrated our approach.