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KI - Künstliche Intelligenz
Erschienen in: KI - Künstliche Intelligenz 3-4/2016

06.08.2016 | Review

Knowledge-Based Instrumentation and Control for Competitive Industry-Inspired Robotic Domains

verfasst von: Tim Niemueller, Sebastian Zug, Sven Schneider, Ulrich Karras

Erschienen in: KI - Künstliche Intelligenz | Ausgabe 3-4/2016

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Abstract

Autonomy is an increasing trend in manufacturing industries. Several industry-inspired robotic competitions have been established in recent years to provide testbeds of comprehensible size. In this paper, we describe a knowledge-based instrumentation and control framework used in several of these competitions. It is implemented using a rule-based production system and creates the task goals for autonomous mobile robots. It controls the environment’s agency using sensor data from processing stations and instructs proper reactions. The monitoring and collection of various data allows for an effective instrumentation of the competitions for evaluation purposes. The goal is to achieve automated runs with no or as little human intervention as possible which would allow for more and longer lasting runs. It provides a general framework adaptable to suit many scenarios and is an interesting test case for knowledge-based systems in an industry-inspired setting.
Vorheriger Artikel An Object Template Approach to Manipulation for Humanoid Avatar Robots for Rescue Tasks
Nächster Artikel The Role of Focus in Advanced Visual Interfaces

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Fußnoten
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RoCKIn and RockEU2 also have domestic service robot branches, which are not further detailed for the sake of brevity.
 
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Technical Challenges in RoboCup are tasks in addition to the main task used to foster development of new capabilities in a league.
 
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The source of the RoboCup Industrial Referee Box is available at https://​github.​com/​robocup-industrial/​rci-refbox.
 
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Metadaten
Titel
Knowledge-Based Instrumentation and Control for Competitive Industry-Inspired Robotic Domains
verfasst von
Tim Niemueller
Sebastian Zug
Sven Schneider
Ulrich Karras
Publikationsdatum
06.08.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
KI - Künstliche Intelligenz / Ausgabe 3-4/2016
Print ISSN: 0933-1875
Elektronische ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-016-0438-8

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