A Multirobot Cooperation Framework for Sewing Personalized Stent Grafts

Abstract

This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multirobot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multirobot cooperation is required.

Data Management in Industry 4.0: State of the Art and Open Challenges

“…Tracking control and correction: [164], [174], [175] [167], [180], [182] [184], [186], [190] [191] Collaboration: [179], [187] Mobile Robots Localization: [165], [169], [172] Navigation: [166], [168], [171] [173], [181], [188] Collaboration: [176]- [178], [185] Other operations: [163], [183], [192] to be one of the most efficient control schemes for complex industrial processes due to the difficulty in obtaining accurate mathematical control models [155] and to the frequent existence of nonlinearities and stochastic disturbances [156]. In fact, data delivery latency is among the most active topics in the NCS field recently.…”

Visual Guidance and Automatic Control for Robotic Personalized Stent Graft Manufacturing

“…Yu Guo, Miao Sun, Frank Po Wen Lo, Benny Lo detection [9]. However, the stereo camera could often lose its focus on the constantly moving needle in the sewing workspace and could not provide very precise localization of the needle, as it has a fixed and relatively short focal length.…”

A Learning-Driven Framework with Spatial Optimization For Surgical Suture Thread Reconstruction and Autonomous Grasping Under Multiple Topologies and Environmental Noises

“…To initialize the surgical knot tying, extensive investigations have been put on automated stitching/piercing tasks, covering the research areas of collaborative hardware developments for robotic suturing [11][12] [13], semantic segmentation of surgical tools [14], task planning and execution via learn-by-demonstrations [15] and supervised learning [16], interaction analysis between rigid instrument and soft tissue [17] [18]. All these pioneering studies provided potential solutions to knotty challenges in automated suturing task [19].…”