ARM Quick Start Technology Projects

Advanced Robotics for Manufacturing (ARM) is proud to announce the awardees of its first round of project funding to strengthen U.S. manufacturing. Separate from ARM’s first official project call, these projects were selected upon ARM’s inception to begin generating timely impact on the national manufacturing landscape and serve as examples of ARM’s mission.

ARM has awarded $2.8 million in funding to four project teams, with the teams contributing approximately $4 million in cost-share, totaling close to $7 million in overall investment. Each project’s outcome will generate impact on the national manufacturing ecosystem by aligning with one or more of ARM’s four strategic goals: asserting U.S. leadership in advanced robotics manufacturing; empowering American workers to compete with low-wage workers abroad; lowering the technical, operational, and economic barriers for companies to adopt robotic technologies; and aiding in the creation and sustenance of valuable manufacturing jobs.

 

Smart Companion Robot for Automotive Assembly

Principal Investigator: Clemson University

Team Members: Siemens, BMW, and Yaskawa

Description: Few industrial robot deployments have made it into automotive final assembly because they tend to be expensive, inflexible, and cumbersome. The goal of this project is to demonstrate the viability of an intelligent mobile manipulator robotic system, the Smart Companion Robot (SCR), to assist and augment human associates in automotive final assembly, where intensive manual manipulation remains the mainstay. The SCR is intended to first, provide cognitive assist by delivering the right part and tool at the right place at the right time, eliminating the risk of incorrect parts being assembled, and enhancing quality in high-mix assembly environments; and second, provide physical assist, by helping the transport of medium-heavy parts from subassembly areas to reduce worker fatigue/repetitive injuries. Such capability addresses two variations of mass customization – high volume/low mix space, such as build to stock, and high volume/high mix, such as build to order.

Robot Assistant for Composites Manufacturing
Principal Investigator: Rensselaer Polytechnic Institute
Team Members: GE Global Research, Southwest Research Institute, IEEE GlobalSpec, Vistex, Fuzehub, SME, and Army Benet Laboratory
Description: This project will develop an operator-guided, semi-automatic assembly process using industrial robots integrated with multiple sensors. The goal of the project is to improve manufacturing productivity by enhancing the operator’s capabilities through advanced robotics, and appropriately applying the technologies that capitalize on the strengths of the robotics (e.g. precise manipulation) and the operator (e.g. decision-making).

Robotic Sanding and Finishing
Principal Investigator: Lockheed Martin
Team Members: Texas A&M University and University of Southern California
Description: This project addresses the critical challenges of Robotic Sanding and Finishing (RSF) by focusing on the shortcomings of existing RSF systems, which are only applicable for low production volumes with simple part geometries, and high production volumes in which trajectories are manually generated and tediously refined. The objective of this project is to develop a robotic sanding system that is easily reconfigurable and an order of magnitude lower in cost than currently available systems. This project will make critical advancements necessary in planning, control, and sensing to integrate such a system. This system will be compatible with a wide variety of robotic manipulators and will be built using open architecture.

iWired: Intelligent Wire Harness Robotically Assembled
Principal Investigator: United Technologies Research Center
Team Members: University of Connecticut and ABB
Description: The objective of this project is to use perception-enabled collaborative robotics that leverage dexterous manipulation for specific assembly processes of wire harnesses, which includes non-rigid parts and process-associated uncertainties. The application is for modern aerospace production plants, with the goal to improve quality and performance by increasing the automation of wire harness assembly through design, development, and testing of a perception-aided collaborative robotic solution for manipulating a wide variety of small and flexible parts.

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