NSF Award
1142277
This Small Business Innovation Research (SBIR) Phase I project proposes that haptic robots offer effective and<br/>attractive solutions to the common tasks of Bin-Picking and Kitting, which currently require human workers. A<br/>popular application area is programming an industrial robot to work with specific powertrain parts. The<br/>Problem: Bin-picking robots can only manipulate strong parts with simple geometries in structured or semistructured<br/>configurations; machine vision deals poorly with overlapping objects and unstable and unpredictable<br/>loads or grips. The Opportunity: Haptic systems can compliment machine vision systems using tactile sensory<br/>data to cope with overlapping objects and unstable grip conditions. The Solution: A robotic hand/arm<br/>equipped with unique biomimetic tactile sensors to make a platform for developing grip algorithms for parts<br/>picking and kitting. Innovations include develop algorithms for slip-detection and force-cone grip adjustment;<br/>developing adaptive algorithms using tactile feedback to improve grip pre-shaping; validating algorithms with<br/>physical powertrain parts; and challenging algorithms with real-world uncertainties that are difficult to detect<br/>using machine vision.<br/><br/>The broader impact/commercial potential of this project will result in robots that have more humanlike haptic<br/>capabilities. Currently robots lack tactile sensing and rely on specialized component feeders and gripping tools<br/>to handle individual objects with predetermined gripping features. These items add cost, occupy space, and<br/>consume time, especially when many different objects must be handled. This puts robots at a significant<br/>disadvantage to human workers who use their hands dexterously to handle an unlimited variety of objects.<br/>Nevertheless, robots are best suited to handle tasks that are highly repetitive or dangerous to humans. When<br/>robots handle hazardous materials or manufacturing tasks, they relieve humans of these burdens while often<br/>yielding increased productivity and cost-savings. At the end of Phase I we will have developed a proof of<br/>concept so that our Phase II research can integrate the system into an industrial environment. By addressing the<br/>picking of objects with errors in object pose, position and part-to-part interaction, haptic robots will transform<br/>this important part of the manufacturing process. Such technology will have widespread impact on many<br/>aspects of manufacturing and automation.
