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https://dx.doi.org/10.48550/ar...
Article . 2023
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Hiding task-oriented programming complexity: an industrial case study

Authors: Enrico Villagrossi; Michele Delledonne; Marco Faroni; Manuel Beschi; Nicola Pedrocchi;

Hiding task-oriented programming complexity: an industrial case study

Abstract

The ease of use of robot programming interfaces represents a barrier to robot adoption in several manufacturing sectors because of the need for more expertise from the end-users. Current robot programming methods are mostly the past heritage, with robot programmers reluctant to adopt new programming paradigms. This work aims to evaluate the impact on non-expert users of introducing a new task-oriented programming interface that hides the complexity of a programming framework based on ROS. The paper compares the programming performance of such an interface with a classic robot-oriented programming method based on a state-of-the-art robot teach pendant. An experimental campaign involved 22 non-expert users working on the programming of two industrial tasks. Task-oriented and robot-oriented programming showed comparable learning time, programming time and the number of questions raised during the programming phases, highlighting the possibility of a smooth introduction to task-oriented programming even to non-expert users.

This article has been accepted for publication in the International Journal of Computer Integrated Manufacturing, published by Taylor & Francis

Country
Italy
Keywords

FOS: Computer and information sciences, Computer Science - Robotics, Intuitive robot programming; task-oriented programming; human-machine interaction; end-user robot programming, Computer Science - Human-Computer Interaction, task-oriented programming, Intuitive robot programming, Robotics (cs.RO), end-user robot programming, human-machine interaction, Human-Computer Interaction (cs.HC)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green