Grasping, Manipulation, and Contact Safety Performance of Robotic Systems

Summary

Recent advancements in collaborative robotic arms and end-effector technologies have the potential to accelerate the use of robotics for manufacturing operations. The force sensing and compliance capabilities used in collaborative robots to prevent injuries and enable them to work safely alongside human workers in manufacturing environments lend themselves to robotic assembly tasks. Additionally, robotic hands are emerging as a next generation end-effector technology with advanced force control and manipulation capabilities. These robotic components as well as improved calibration methods, artificial intelligence techniques, simplified programming interfaces, and ease of reconfiguration are all contributing to new ways of tackling the small parts production field especially for low-volume, high-mix manufacturing operations.

Description

To provide performance metrics, test methods and associated measurement tools to support next-generation robot systems having human-like dexterity and force control characteristics that enable tactual-based safe human collaboration and manufacturing tasks.

Technical Idea

This project will develop the measurement science to assure and advance the safety and performance of industrial arms and grasping mechanisms.  The force sensing and compliance capabilities used in collaborative robots to prevent injuries and enable them work safely alongside human workers as well as robotic hand end-effector technology with advanced force control and manipulation capabilities are proving to be excellent tools to support manufacturing tasks.  Examples include use of passive or active compliance and force control for insertions in assembly, or for adapting to part geometries while performing surfacing operations.   
The project will continue longstanding work and leadership within existing standards bodies focusing on robot safety, specifically ISO and RIA.  Using unique testbed setups, the project will support the current collaborative robot safety standards through the development of analytic tools and artifacts that can validate PFL performance of robot arms and end-of-arm-tooling, as well as investigate the use of injury criteria instead of pain for guiding safety thresholds.  The project will also continue convening and leading an IEEE working group of robotic hand, grasping, and manipulation researchers to identify metrics and prototype test methods and benchmarks to advance the state-of-the-art.  New tools and artifacts for characterizing robotic system arm and hand performance will be investigated.  

Research Plan

To support developers and users of emerging collaborative robot arms and end-effector technologies by: providing measurement tools and associated test methodologies to assess grasping and assembly characteristics; develop calibration techniques to support collaborative robot accuracy and re-tasking operations in, and to develop test methods  to support collaborative robots safety standards.

1. Develop measurement science in the form of metrics, test methods and artifacts to support next-generation robots designed for collaborative operation that implement 1) part grasping and manipulation, where advanced robotic arms, coupled with multi-fingered robotic hands, closely mimicking human dexterity, must fully constrain a part for maximum stability and ease of manipulation without the aid of custom fixturing; 2) assembly operations, where parts are oriented, aligned, inserted and fastened to result in a final product; and 3) surfacing operations, where passive or active compliance are used to adapt to part geometry.
2. Research sensor technologies and develop test methods to optimize the kinematics and dynamics of collaborative robots to: 1) improve their accuracy for manufacturing tasks using a range of measurement techniques with supporting testbed implementations and verification measures; and 2) formulate robot specific Artificial Intelligence (AI) foundations to be used for automatic modifications that can improve its performance.
3. Develop test methods, mathematical models and artifacts to support both international and national robot safety standards for the assessment of the injury potential to humans by robots designed for collaborative operation.