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https://www.nist.gov/programs-projects/agility-performance-robotic-systems

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PROJECTS/PROGRAMS

# Agility Performance of Robotic Systems

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## Summary

The application of robotics in manufacturing is hindered by their lack of agility, including their large changeover times for new tasks/products and their limited reusability.  Manufacturers have difficulty addressing this because of the complexity of robot systems, the lack of understanding of robot capabilities, and the absence of measurement science and tools to assess and assure the robot’s agility. Manufacturers have often avoided and/or accepted the agility limitations since robots are primarily used in large companies for large lot size production, and changeover is performed infrequently and manually., The key idea for this project is to develop the measurement science which will enable manufacturers to assess and assure the agility performance of their robot systems. This includes robot agility performance metrics, test methods, information models, and planning approaches – all of which will be validated using a combined virtual and real testing environment. This will allow manufacturers to gain more value from their robots by more rapidly reconfiguring and re-tasking robot systems, make robots more accessible to small and medium organizations, and provide large organizations greater efficiency in their operations. Any company that is currently deploying or planning to deploy robot systems will benefit because they will be able to accurately predict the long-term value or benefit by understanding the agility performance of their robot systems. NIST is uniquely qualified to address this because of our experience in robot performance characterization, information modeling standards, agility methodologies, and the combined real and virtual manufacturing robotics testbeds.

## Description

**Objective**

Deliver robot agility performance metrics, test methods. information models, data sets, and planning approaches that will enable manufacturers to more easily and rapidly reconfigure and re-task robot systems.

**What is the Technical Idea?**

The new technical idea for this project is to develop the measurement science enabling manufacturers to assess and assure the agility performance of their robot systems. This framework will include 1) robot agility performance metrics and test methods, 2) information models, 3) data sets, and 4) planning approaches. With this, the following will be possible:
                  - Using the test methods and metrics, manufacturers will be able to determine a robotic system’s ability to handle new assembly operations.
                  - Using the test methods and metrics, manufacturers will be able to quantify a robot’s performance when confronted with unexpected events or failures.
                  - Using the information models and planning approaches, manufacturers will be able to quickly and easily re-task their robots when new assembly operations are needed. Based on current situations, it often takes an order of magnitude longer to program and configure the robot than the task would take to complete by hand.
                  - Using the information models and planning approaches, manufacturers will be able to assess the robot’s ability to cope with part and environmental variations. Many subcomponents have component-by-component or lot-by-lot variations. Today’s high-precision robotic systems are not able to adapt automatically to such variations.
                  - Using the information models, the robots will be able to enumerate and make explicit the necessary knowledge for achieving rapid re-tasking and being agile. Manufacturers will be able to answer critical questions such as: “What does the robot need to know?”, “When does it need to know it?”, and “How will it get that knowledge?”
                  - Using data sets, manufacturers will be able to train AI systems that will allow robots to learn new tasks.

**What is the Research Plan?**

The project will focus heavily on manufacturing assembly applications, but also begin to explore other manufacturing domains that have significant importance to our customers, such as order fulfillment and logistics.

This project will focus on standards and performance metrics. We will build upon our previous work in knowledge representation efforts in IEEE by contributing to more focused standards, such as the representation of robot tasks. Standard planning languages, such as the Canonical Robot Command Language, will continue to be improved and made available through the ROS Industrial Consortium. All of the standards developed in this project will be applied to industrially-relevant scenarios and realized in the Robot Agility Lab. These proof-of-concept implementations will help to validate the standards being developed and will demonstrate approaches by which industry can implement them.

Robot agility test methods and performance metrics will be emphasized and will be validated through the Agile Robotics for Industrial Automation Competition (ARIAC). Once they reach a suitable level of maturity, they will be standardized within a relevant standards organization.

The use of simulation will be a large focus, initially by serving as a gateway to make the Robot Agility Lab available to the public as a testbed, allowing researcher to explore novel robot agility approaches and allowing the project to validate performance evaluation approaches. ROS interfaces, through the Gazebo simulation package, will allow external researchers to try out their robot agility approaches before they implement them remotely in the Robot Agility Lab.

Artificial Intelligence approaches will play a significant role in the project. Initial AI approaches applied to dynamic re-planning and reasoning over formal knowledge representations will be expanded into machine learning techniques applied to perception and path planning, with the goal of designing experiments that will yield AI training data. Focus areas will include, but not be limited to, learning from teach tables, optimizing action sequences for shorter run times, and making the robots easier to configure/program for novices.

## Major Accomplishments

Some recent accomplishments in the area of Agility Performance of Robotic Systems include:
                  - CRCL specification released and widely available on ROS-I distribution.
                  - An IEEE Industrial Ontology Study Group approved by IEEE and convened to advance the development of the industrial ontology.

**Selected Publications**

Software Tools for XML to OWL Translation (July 2015)

Towards robust assembly with knowledge representation for the planning domaindefinition language (PDDL) (June 2015)

An Ontology Based Approach to Action Verification for Agile Manufacturing (January 2014)

A Simulated Sensor-based Approach for Kit Building Applications (December 2013)

A Literature Review of Sensor Ontologies for Manufacturing Applications (October 2013)

Inferring Intention Through State Representations in Cooperative Human-RobotEnvironments (July 2013)

Metrics and Test Methods for Industrial Kit Building (June 2013)

Metrics and Test Methods for Industrial Kit Building (May 2013)

A Sensor Ontology Literature Review (April 2013)

An Industrial Robotic Knowledge Representation for Kit Building Applications (October 2012)

Functional Requirements of a Model for Kitting Plans (March 2012)

Applied AIManufacturingRobotics in manufacturingAgility and adaptability

## Organizations

                          - NIST Headquarters
                            - Laboratory Programs
                              - Engineering Laboratory
                                - Intelligent Systems Division
                                  - Cognition and Collaboration Systems Group

## NIST Staff

Craig I. Schlenoff

Murat Aksu

Brian Antonishek

Anthony Downs

William Harrison

## Contact

                    -  Craig I. Schlenoff  craig.schlenoff@nist.gov      (301) 975-3456   

## Project Status

## Related NIST Projects

Measurement Science for Manufacturing Robotics

Embodied AI and Data Generation for Manufacturing Robotics

Grasping, Manipulation, and Contact Safety Performance of Robotic Systems

Mobility Performance of Robotic Systems

Perception Performance of Robotic Systems

Performance of Human-Robot Interaction

Tools for Collaborative Robots within SME Workcells

## Other Projects

Agile Robotics for Industrial Automation Competition (ARIAC) Workshop

Robotic Grasping and Manipulation for Assembly

A-Unmanned Ground Vehicles Research and Standard Test Methods

Exoskeletons and Exosuits Research and Standard Test Methods

Mobile Manipulator Performance Measurement Data

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

Calibration and Registration Tools

Data and Results from Three Peg-in-Hole Experiments for Improving Insertion Tasks

Improving Positional Accuracy for Robotic Assembly Tasks

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Program videos

**Blog Post**

Helping Robots Be Less Robotic

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