Performance Measures to Benchmark the Grasping, Manipulation and Assembly of Deformable Objects Typical to Manufacturing Applications

Published

November 21, 2022

Author(s)

Kenny Kimble, Justin Albrecht, Megan Zimmerman, Joseph A. Falco

Abstract

The National Institute of Standards and Technology is developing performance tests and associated test artifacts as a means to benchmark research in the area of robotic assembly. Sets of components consistent with mechanical assemblies including screws, gears, electrical connectors, wires and belts are configured for assembly or disassembly using a task board concept. Test protocols accompany the task boards and are designed to mimic low-volume, high-mixture assembly operations typical to small and medium sized manufacturers. The task boards include many non-rigid component operations representative of wire harness and belt drive assemblies to support benchmarking research in the area of grasping and manipulation of deformable objects, an area still considered to be and emerging research problem in robotics. A set of four primary task boards as well as competition task boards are presented as benchmarks along with scoring metrics and a method to compare robot system assembly times with human performance. Competitions are used to raise awareness to these benchmarks as tools to progress and compare research are described along with some of the team solutions in grasping and manipulating deformable objects. A task board prototype design that adds levels of difficulty and more closely resembles assembled products is also presented.

Citation

Frontiers in Robotics and AI

Pub Type

Journals

Download Paper

https://doi.org/10.3389/frobt.2022.999348

Local Download

Keywords

Grasping, Manipulation, Assembly, Performance Benchmarks, Deformable Objects

Citation

Kimble, K. , Albrecht, J. , Zimmerman, M. and Falco, J. (2022), Performance Measures to Benchmark the Grasping, Manipulation and Assembly of Deformable Objects Typical to Manufacturing Applications, Frontiers in Robotics and AI, [online], https://doi.org/10.3389/frobt.2022.999348, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935275 (Accessed December 12, 2024)