Agile Robotics for Industrial Applications: Editorial

Published

March 24, 2021

Author(s)

Craig I. Schlenoff, Zeid Kootbally, Erez Karpas

Abstract

Advances in automation have provided for sustained productivity increases and manufacturing growth over the past decade. Sustaining this growth will require automation to become more agile and flexible, enabling the automation of tasks that require a high degree of human dexterity and the ability to react to unforeseen circumstances. Applying robots is one promising approach, but their traditional program-by-teaching model takes considerable time, requires extensive expertise, and does not lend itself to tasks that require adaptability. This has limited robots to high-volume, repetitive operations and precluded them from low-volume, time critical, and flexible projects. Off-line programming of robots is possible, similar to the computer-aided manufacturing (CAM) method widely used for machine tools. However, the poor accuracy of robots compared with machine tools limits them to jobs with low tolerance requirements, or requires additional methods such as calibration, modeling, and external sensing to improve their accuracy. These methods increase the upfront cost of a robotic system. However, advances and cost reduction in sensing technologies (especially laser scanning) have brought robot systems into the price range of even small-to-medium enterprises. In addition, use of end-of-arm tools (EOAT) has given integrators the ability to provide faster turnaround time and utilize the same infrastructure in a high-mix, low-volume environment.

Citation

Robotics and Computer-Integrated Manufacturing

Volume

71

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.rcim.2021.102162

Local Download

Keywords

ARIAC, robot agility, manufacturing, industrial

Citation

Schlenoff, C. , Kootbally, Z. and Karpas, E. (2021), Agile Robotics for Industrial Applications: Editorial, Robotics and Computer-Integrated Manufacturing, [online], https://doi.org/10.1016/j.rcim.2021.102162, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931897 (Accessed November 20, 2024)