An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, allowing an easy application of this technique. This work presents a deep learning approach for solving the perception problem within the visual servoing scheme. An artificial neural network is trained using the supervision coming from the knowledge of the controller and the visual features motion model. In this way, it is possible to give a geometrical interpretation to the estimated visual features, which can be used in the analytical law of the visual servoing. The approach keeps perception and control decoupled, conferring flexibility and interpretability on the whole framework. Simulated and real experiments with a robotic manipulator validate our approach.
翻译:越来越多的非专业机器人用户需要易于使用的机器。在视觉蒸发方面,清除清晰图像处理正在成为一种趋势,便于应用这一技术。这项工作为在视觉蒸发计划内解决感知问题提供了深层次的学习方法。利用控制器和视觉特征运动模型知识的监管,对人工神经网络进行了培训。这样,有可能对视觉特征的估计进行几何解释,可用于视觉蒸发的分析法中。该方法保持了感知和控制的分解,赋予了整个框架的灵活性和可解释性。机器人操纵器的模拟和真实实验证实了我们的方法。