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Neural network Reinforcement Learning for visual control of robot manipulators

机译:用于机器人操纵器视觉控制的神经网络强化学习

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It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-leaming and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.
机译:众所周知,机器人视觉伺服控制中的大多数关键问题都与考虑测量和建模误差的系统性能分析有关。本文提出了一种新型的基于神经网络强化学习的机器人操纵器智能视觉伺服控制器的开发与性能评估。通过在基于视觉的控制方案中实施机器学习技术,使机器人能够在线改善其性能并适应环境中不断变化的条件。通过不同的视觉控制场景,开发并测试了两种不同的时差算法(Q学习和SARSA)与神经网络相结合。采用代表性学习样本的数据库,以加快神经网络的收敛速度和机器人行为的实时学习。此外,视觉伺服任务分为两步以确保特征的可见性:第一步,使用神经网络强化学习控制器进行机器人的对中行为,而第二步涉及在传统图像之间切换控制基于视觉伺服和神经网络强化学习,可实现机械手的接近行为。通过在两个控制步骤中独立定义图像特征的关注区域,可以实现机器人运动的校正。为了呈现所开发系统关于校准误差,建模误差和图像噪声的鲁棒性,开发了各种仿真。此外,提出了与传统的基于图像的视觉伺服的比较。在具有低成本视觉系统的机器人操纵器上进行的实际实验证明了该方法的有效性。

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