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Combining adjustable autonomy and shared control as a new platform for controlling robotic systems with ROS on TurtleBot
Örebro University, School of Science and Technology.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Fully autonomous robotic systems can fulfill their functionality, without human interaction, but their efficiency is way lower, than a robotic system, which is teleoperated by a specialist. The teleoperation of robotic systems requires  continuously high attention from the operator, if this attention is taken away or reduced, the efficiency drops heavily. The combination of Adjustable Autonomy and Shared Control represent a promising approach, of how great efficiency could be maintained in a robotic system, with a minimum of human interaction.

 

The goal of this project is the re-implementation of the utilitarian voting scheme for navigation for usage with modern robotic platforms, as proposed in the publication "Experiments in Adjustable Autonomy" by Jacob W. Crandall and Michael A. Goodrich. This voting scheme combines a proposed direction, which is given by a human operator, with environmental sensor data to determine the best direction for a robots next movement.

 

The implemented prototype in this project was developed with ROS on TurtleBot and is processing the sensor data and calculating the best direction for the robot's movement in the same way, as the original prototype. Since the original setup consists of a Nomad SuperScout robot with sixteen sonar range finders, adjustments needed to be made, to run the same algorithm on a different setup. The  correct processing of the input data and estimation of the best direction was verified by pen and paper calculations. Finally, further ideas for improving the implemented prototype and usage in other scenarios were presented.

Place, publisher, year, edition, pages
2018. , p. 71
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-64637OAI: oai:DiVA.org:oru-64637DiVA, id: diva2:1178533
Subject / course
Computer Engineering
Supervisors
Examiners
Available from: 2018-01-30 Created: 2018-01-30 Last updated: 2018-03-26Bibliographically approved

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CiteExportLink to record
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  • apa
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