Due to its excellent chemical and mechanical properties, silicone sealing has been widely used in many industries. Currently, the majority of these sealing tasks are performed by human workers. Hence, they are susceptible to labor shortage problems. The use of vision-guided robotic systems is a feasible alternative to automate these types of repetitive and tedious manipulation tasks. In this paper, we present the development of a new method to automate silicone sealing with robotic manipulators. To this end, we propose a novel neural path planning framework that leverages fractional-order differentiation for robust seam detection with vision and a Riemannian motion policy for effectively learning the manipulation of a sealing gun. Optimal control commands can be computed analytically by designing a deep neural network that predicts the acceleration and associated Riemannian metric of the sealing gun from feedback signals. The performance of our new methodology is experimentally validated with a robotic platform conducting multiple silicone sealing tasks in unstructured situations. The reported results demonstrate that compared with directly predicting the control commands, our neural path planner achieves a more generalizable performance on unseen workpieces and is more robust to human/environment disturbances.

由于其优异的化学和机械性能，硅胶密封圈已被广泛应用于许多行业。目前，这些密封任务中的大部分由人工完成。因此，他们很容易受到劳动力短缺问题的影响。使用视觉引导的机器人系统是自动化这些类型的重复和繁琐的操作任务的可行替代方案。在本文中，我们介绍了一种使用机器人操纵器自动进行硅胶密封的新方法的开发。为此，我们提出了一种新颖的神经路径规划框架，该框架利用分数阶微分通过视觉进行稳健的接缝检测，并利用黎曼运动策略来有效地学习密封枪的操作。可以通过设计一个深度神经网络来分析计算最佳控制命令，该网络可以根据反馈信号预测密封枪的加速度和相关的黎曼度量。我们的新方法的性能通过在非结构化情况下执行多项硅胶密封任务的机器人平台进行了实验验证。报告的结果表明，与直接预测控制命令相比，我们的神经路径规划器在看不见的工件上实现了更普遍的性能，并且对人/环境干扰更鲁棒。