SBIR Phase II: Real-Time Roboting Grasping System

This Small Business Innovation Research (SBIR) Phase II research project will create an information-based robotic grasping framework to enable practical grasping of objects for any robotic manipulator and any robotic hand, or even multiple hands. Grasp algorithms are stored in an XML database organized in a tree structure that allows rapid access and uses intelligent caching for very large databases. When a new object is presented to the grasping system, best matches are found in the database and the corresponding algorithms are extrapolated to determine the best grasp for the new object. Shape, surface properties, and articulation are used for matching. The techniques support the grasping of moving objects that can be tracked with a vision-based system. For constructing the grasp database, human supervisors train new grasps by simply picking up objects and giving special cues. Collection devices, such as data gloves and machine vision systems, are used to collect the supervisor?s hand position and contact forces, and a learning module finds new grasps by coupling supervisory input with simulation-based optimization, using high-fidelity dynamic modeling. For optimization, control and configuration parameters (in end-effector space) are perturbed iteratively using nonlinear numerical optimization techniques. If successful the creation of a comprehensive grasping framework as proposed in this project will have broad impact to research, industry, and society. Traditional grasping systems require specialized coding for new tasks and new robots. The proposed system will facilitate specific instantiations of general grasping algorithms. Application to virtually any robot manipulator, any hand, and any object to be grasped will be possible. This unprecedented flexibility, coupled with advanced and innovative grasping algorithms will play a role in advancing general purpose robots (those that can do multiple tasks without reprogramming). Robots with the ability to grasp hold promise for industries with labor shortages. The agricultural industry, for instance, will use robotic grasping for harvesting. Grasping robots will work in dangerous environments. An example application is rescuing injured humans in dangerous situations. Next-generation robots will assist the disabled with intelligent manipulators that can open doors and pick up objects. Grasping robots will support manufacturing and warehouse businesses. The simulation capability that is part of this research will allow new grasping strategies to be tested safely in a virtual environment before being implemented and fielded.