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Human Robot Interaction for Autonomous Systems in Industrial Environments
Chalmers University of Technology. (AASS MRO Lab)ORCID iD: 0000-0002-8380-4113
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The upcoming new generation of autonomous vehicles for transporting materials in industrial environments will be more versatile, flexible and efficient than traditional Automatic Guided Vehicles (AGV), which simply follow pre-defined paths. However, freely navigating vehicles can appear unpredictable to human workers and thus cause stress and render joint use of the available space inefficient. This work addresses the problem of providing information regarding a service robot’s intention to humans co-populating the environment. The overall goal is to make humans feel safer and more comfortable, even when they are in close vicinity of the robot. A spatial Augmented Reality (AR) system for robot intention communication by means of projecting proxemic information onto shared floor space is developed on a robotic fork-lift by equipping it with a LED projector. This helps in visualizing internal state information and intents on the shared floors spaces. The robot’s ability to communicate its intentions is evaluated in realistic situations where test subjects meet the robotic forklift. A Likert scalebased evaluation which also includes comparisons to human-human intention communication was performed. The results show that already adding simple information, such as the trajectory and the space to be occupied by the robot in the near future, is able to effectively improve human response to the robot. This kind of synergistic human-robot interaction in a work environment is expected to increase the robot’s acceptability in the industry.

Place, publisher, year, edition, pages
2016. , p. 38
Keywords [en]
HRI, Human Robot Interaction, Spatial Augmented Reality, SAR, Logistics, Mobile Robots, Intention Communication
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-55277OAI: oai:DiVA.org:oru-55277DiVA, id: diva2:1071029
Subject / course
Computer Engineering
Presentation
2016-05-19, 17:32
Supervisors
Available from: 2017-06-16 Created: 2017-02-02 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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