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Abstract: Traditionally, industrial robots have been used on mass production lines, where the same manufacturing operation is repeated many times. Many sectors of manufacturing such as aerospace, defense, ship building, mold and die making involve small production volumes and non-repetitive tasks. Currently, industrial robots are not used in such applications.  The use of robotic assistants can significantly improve human operator productivity in small production volume manufacturing and eliminate the need for human involvement in tasks that pose risks to human health. Recent advances in human-safe industrial robots present an opportunity for creating hybrid work cells, where humans and robots can collaborate in close physical proximities. This capability enables realizing systems that utilize the complementary strengths of humans and robots. Several low-cost robots have been introduced in the market over the last few years, making them attractive in many new manufacturing applications where robot utilization is not expected to be very high. This makes the idea of hybrid cells economically viable for small volume production. This presentation will describe computational foundations for creating robotic assistants for non-repetitive manufacturing tasks. We will begin with an overview of an integrated decision making approach that brings together concepts from perception, planning, control, and learning to realize robotic assistants that can aid human workers in manufacturing. Traditional off-line robot programming approaches cannot be used on non-repetitive tasks.  We will describe a new decision making approach based on the integration of real-time planning and perception for performing non-repetitive tasks using robots. There are many challenging tasks for which a simulation-based planning approach cannot be used to select the optimal process parameters. For such tasks, we will describe a new approach for robots to learn task parameters from self-exploration.  Both humans and robots can make errors in a hybrid cell, hence creating contingency situations. Unless handled promptly, a contingency situation may lead to significant operational inefficiencies. We will describe a decision making approach for detecting and managing contingencies.  Bin picking, assembly, and cleaning tasks will be used as illustrative examples to show how robots can be used on non-repetitive manufacturing tasks.

Bio: Dr. Satyandra K. Gupta is Smith International Professor in the Department of Aerospace and Mechanical Engineering at the University of Southern California. He is the founding Director of the Center for Advanced Manufacturing at the University of Southern California. He served as a program director for the National Robotics Initiative at the National Science Foundation from September 2012 to September 2014. Dr. Gupta’s interest is in the area of physics-aware decision making to facilitate automation. He is specifically interested in automation problems arising in Engineering Design, Manufacturing, and Robotics. He has published more than three hundred technical articles. He is a fellow of the American Society of Mechanical Engineers (ASME). Dr. Gupta has received several honors and awards for his research contributions. Representative examples include: a Young Investigator Award from the Office of Naval Research in 2000, a Robert W. Galvin Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers in 2001, a CAREER Award from the National Science Foundation in 2001, a Presidential Early Career Award for Scientists and Engineers (PECASE) in 2001, Invention of the Year Award at the University of Maryland in 2007, Kos Ishii-Toshiba Award from ASME Design for Manufacturing and the Life Cycle Committee in 2011, and Excellence in Research Award from ASME Computers and Information in Engineering Division in 2013. He received Distinguished Alumnus Award from Indian Institute of Technology, Roorkee in 2014. He has also received six best paper awards at conferences.

Date(s) - May 05, 2017
2:00 pm - 3:00 pm


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