Neural reactive path planning with Riemannian motion policies for robotic silicone sealing. (June 2023) - Zhou, Peng; Zheng, Pai; Qi, Jiaming; Li, Chengxi; Duan, Anqing; Xu, Maggie; Wu, Victor; Navarro-Alarcon, David
Abstract: 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. Highlights: A novel two-camera path planning framework for robotic sealing is proposed. A fractional order -based approach is designed for robust seam detection. A reactive path planning is proposed combined with Riemannian motion policy . A detailed experimental validation with a robotic sealing platform is done.