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NIST Robotics Wares: What You Can Use or Get Involved With Now

Agile Robotics for Industrial Automation Competition

Description: Simulation (Gazebo)-based competition whose core focus is to test the agility of industrial robot systems, with the goal of enabling industrial robots on the shop floors to be more productive, more autonomous, and to require less time from shop floor workers. Agility is defined as failure identification and recovery, automated planning and re-planning, and the ability to swap in and out different brand robots.

State of Maturity: Year one of competition successfully completed, year two will likely be a prize competition with a $10K first prize and is expected to occur in May 2018

Web Page: http://www.nist.gov/ariac/

Contact: Craig Schlenoff, 301-975-3456, craig.schlenoff@nist.gov


Assembly Test Methods

Description: This web site houses a set of performance metrics, test methods and associated artifacts that are being developed to evaluate robotic assembly systems.   Test methods include task-level evaluation of gripper solutions using peg-in-hole artifacts and assembly fastener task boards for benchmarking different systems.

State of Maturity:  The metrics, test methods, and related artifacts are being disseminated to allow researchers, developers, and intended users of assembly robotic systems to experiment with them and provide feedback on how to improve the procedures.

Web Page:  https://www.nist.gov/programs-projects/performance-metrics-and-benchmarks-advance-state-robotic-assembly

Contact:  Joe Falco, 301-975-3455, joseph.falco@nist.gov


Canonical Robot Command Language

Description: The canonical robot command language (CRCL) is a robot-agnostic low-level messaging language for sending commands to, and receiving status from a robot. CRCL commands are executed by a low-level device controller. CRCL is intended primarily to provide commands that are independent of the kinematics of the robot that executes the commands.

State of Maturity: Used in NIST projects and at Georgia Tech Research Institute

Web Page: https://www.nist.gov/el/intelligent-systems-division-73500/canonical-robot-command-language-crcl

Contact: Fred Proctor 301-975-3425, fredrick.proctor@nist.gov


Core Ontology for Robotics and Automation

Description: A core ontology that specifies the main, most general concepts, relations, and axioms of robotics and automation (R&A), which is intended as a reference for knowledge representation and reasoning in robots, as well as a formal reference vocabulary for communicating knowledge about R&A between robots and humans.

State of Maturity: IEEE International Standard used by academic institutions and companies around the world

Web Page: https://standards.ieee.org/develop/wg/ORA.html

Contact: Craig Schlenoff, 301-975-3456, craig.schlenoff@nist.gov


Grasp Metrics and Test Methods

Description: This web site contains descriptions of metrics that can be used to characterize elemental performance of robotic hands, along with draft test methods that have been developed for these metrics.  Datasets collected from experiments using the draft test methods are available for downloads, as well as designs for replicating the testing artifacts.  

State of Maturity: The metrics, test methods, and related artifacts are being disseminated to allow researchers, developers, and intended users of advanced robotic hands to experiment with them and provide feedback on how to improve the procedures. 

Web Page: https://www.nist.gov/programs-projects/performance-metrics-and-benchmarks-advance-state-robotic-grasping

Contact: Joe Falco, 301-975-3455, joseph.falco@nist.gov


Performance Analytics Software

Description:  Downloadable software that can assist in evaluating data to analyze results from experiments.  Algorithms support analysis of attribute, ordinal, and continuous data.

State of Maturity:  The algorithms have been used within NIST to support performance evaluation tests and are considered to be mature.

Web Page: https://www.nist.gov/el/intelligent-systems-division-73500/performance-data-analytics

Contact: Joe Falco, 301-975-3455, joseph.falco@nist.gov


Datasets

 

Dataset:  3D Data for the Evaluation of Point-Based, Rigid Body Registration Error

Description:  Datasets to evaluate the performance of point-based, rigid-body registration may be downloaded from this site.  Registration is the process of transforming one coordinate frame to another coordinate frame. The datasets contain 3D position measurements from three instruments:  a laser tracker, a motion capture system (optical tracking system), and a large-scale metrology system. 

State of Maturity:  The datasets have been validated.

Web Page:  https://www.nist.gov/el/intelligent-systems-division-73500/3d-data-evaluation-point-based-rigid-body-registration-error

Contact: Gerri Cheok, 301-975-6074, cheok@nist.gov


Dataset: Mobile Manipulator Performance Measurement Data

Description:   This site contains data collected from experiments involving the positional accuracy of a mobile manipulator, comprised of an industrial robot arm mounted on an Automatic Guided Vehicle (AGV).   Reference (also referred to as “ground truth”) position measurements captured from an optical tracking system are included to enable comparisons. 

State of Maturity:  The datasets have been validated

Web Page: https://www.nist.gov/el/intelligent-systems-division-73500/mobile-manipulator-performance-measurement-data

Contact: Roger Bostelman, 301-974-3426, roger.bostelman@nist.gov


Dataset:  Peg-in-Hole Data

Description:   Data and results from three peg-in-hole experiments for improving insertion tasks may be downloaded from this site.    A method was developed to reduce the point-based registration error by restoring the rigid body condition (RRBC method).  Registration is the process of transforming one coordinate frame to another coordinate frame.  The coordinate frame from which points are transformed is called the working frame and the coordinate frame to which points are transformed is called the destination frame.  The RRBC method can be used to reduce the uncertainty of a hole location and thus, improve the success rate for insertion tasks.  Peg-in-hole experiments were conducted to quantify the level of improvement.

State of Maturity:   The datasets have been validated

Website: https://www.nist.gov/el/intelligent-systems-division-73500/peg-hole-data

Contact:   Gerry Cheok, 301-975-6074, cheok@nist.gov

Created October 31, 2017, Updated August 6, 2019