A Reconfigurable Multirobot Cooperation Workcell for Personalized Manufacturing

Abstract

Most robotic systems designed for mass manufacturing are optimized for a specific type of product. They generally lack the ability to adapt to low-volume customized products. In this article, we present a system based on a modular design for manufacturing personalized medical stent graft implants. The concept is based on learning-by-demonstration by integrating real-time 3-D vision, multirobot collaboration, and personalization to guide the robots to learn and execute tasks continuously with adaptation to different implant geometry. The system is optimized to generate customized and collision-free paths for efficient object manipulation and task completion. We show that the system is generalizable to different stent graft designs and the proposed multirobot system can seamlessly work together with high efficiency without collisions. The results have also suggested its usability for other manipulation tasks, especially for flexible production of customized products where bimanual or multirobot cooperation is required. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this article is the problem of automatic sewing of personalized stent grafts (a tailor-made artificial vessel). Existing personalized stent grafts are mostly hand sewn, which is time consuming and often undersupplied. Automating such process can significantly improve the production and this requires a sewing system that can handle different designs. This article suggests a new schema to design a robotic system to handle personalized designs. The first methodology is modularized design to separate the task into a repetitive part and a personalized part, each handled by a module. The second methodology is to find the best relative pose between the modules such that the robots can complete their task within their working space and with minimum motion. This ensures that a stent graft can be sewn feasibly and with the lowest cost. Computational results show this approach can find optimal solution for different personalized stent grafts and preliminary on robot experiment verifies that this approach is feasible. Please note that this approach is not limited to sewing personalized stent graft. The schema can be applied to solve similar problem of customized product motion planning and system design.