Fabric control for feeding into an automated sewing machine

The importance of automating the garment manufacturing process has been understood since the early 1980s. However, in spite of millions of dollars spent on research, three decades later, the industry is still far from achieving a fully autonomous process. Previous work on fabric control in automated sewing focused on the control of only a single sheet of fabric using an industrial manipulator with an overhead vision system. These methods did not meet the accuracy and robustness requirements of the sewing process with respect to fabric position and fabric tension. To address these issues, a new method for fabric control in automated sewing is described. It uses the current feed mechanism on sewing machines, feed dogs, but modifies them to be servo-controlled. These servo controlled actuators, servo dogs, individually control two sheets of fabric before the fabric reaches the needle and during the sewing process. The servo dogs actuate the fabric 180o out of phase with the sewing needle, providing incremental control of the fabric when the needle is out of the fabric. To achieve this type of control successfully for automated sewing, the servo dogs have been designed for short displacement, high acceleration motions using a cable drive system powered by voice coil motors. Feedback of fabric position has been determined to be necessary and is to be provided by a thread-tracking vision system. This thesis outlines the general design of the system and discusses a prototype used to validate the design, and describes experiments performed to examine how the fabric will behave with the use of this type of actuation method.M.S.Committee Chair: Book, Wayne; Committee Co-Chair: Dickerson, Steve; Committee Member: Jayaraman, Sundaresa

A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space

We are developing a system for human-robot communication that enables people to communicate with robots in a natural way and is focused on solving problems in a shared space. Our strategy for developing this system is fundamentally data-driven: we use data from multiple input sources and train key components with various machine learning techniques. We developed a web application that is collecting data on how two humans communicate to accomplish a task, as well as a mobile laboratory that is instrumented to collect data on how two humans communicate to accomplish a task in a physically shared space. The data from these systems will be used to train and fine-tune the second stage of our system, in which the robot will be simulated through software. A physical robot will be used in the final stage of our project. We describe these instruments, a test-suite and performance metrics designed to evaluate and automate the data gathering process as well as evaluate an initial data set.Comment: 2017 AAAI Fall Symposium on Natural Communication for Human-Robot Collaboratio

Can an Engineering Competition Catalyze Curriculum Innovation?

This article describes the ongoing efforts of a multidisciplinary group of faculty at an undergraduate institution to form a team and compete in the IBM AI XPRIZE competition. We describe the advantages and disadvantages of faculty participation in major engineering competitions over more traditional professional activities at undergraduate engineering institutions. Our discussion is focused on the benefits to three major groups: undergraduate students, faculty, and academic institutions. We use examples from our one year of experience in the competition and from the literature to illustrate these benefits. Already observed benefits from the competition include increased student engagement, development and introduction of a new minor in cognitive science, the purchase of a state-of-the-art robot and a deep learning server, enhanced multidisciplinary collaboration among faculty, and heightened awareness among administrators of the growing importance of artificial intelligence (AI) technologies. Results of a student survey regarding their involvement in with the team are presented

Simultaneous control of coupled actuators using singular value decomposition and semi-nonnegative matrix factorization

This thesis considers the application of singular value decomposition (SVD) and semi-nonnegative matrix factorization (SNMF) within feedback control systems, called the SVD System and SNMF System, to control numerous subsystems with a reduced number of control inputs. The subsystems are coupled using a row-column structure to allow mn subsystems to be controlled using m+n inputs. Past techniques for controlling systems in this row-column structure have focused on scheduling procedures that offer limited performance. The SVD and SNMF Systems permit simultaneous control of every subsystem, which increases the convergence rate by an order of magnitude compared with previous methods. In addition to closed loop control, open loop procedures using the SVD and SNMF are compared with previous scheduling procedures, demonstrating significant performance improvements. This thesis presents theoretical results for the controllability of systems using the row-column structure and for the stability and performance of the SVD and SNMF Systems. Practical challenges to the implementation of the SVD and SNMF Systems are also examined. Numerous simulation examples are provided, in particular, a dynamic simulation of a pin array device, called Digital Clay, and two physical demonstrations are used to assess the feasibility of the SVD and SNMF Systems for specific applications.PhDCommittee Chair: Book, Wayne; Committee Member: Feron, Eric; Committee Member: Park, Haesun; Committee Member: Sadegh, Nader; Committee Member: Ueda, Ju