Please use this identifier to cite or link to this item:
|Title: ||Probabilistic Human-Robot Information Fusion|
|Authors: ||Kaupp, Tobias|
|Keywords: ||human-robot interaction|
|Issue Date: ||Mar-2008|
|Publisher: ||University of Sydney.|
|Abstract: ||This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human
and robotic information sources have the potential to build complex world models, essential for both automated and human decision making.
In this work, humans and robots are regarded as equal team members who interact and
communicate on a peer-to-peer basis. Human-robot communication is addressed using
probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations
with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into
the machine representation via Human Sensor Models. A common mathematical framework
for humans and robots reinforces the notion of true peer-to-peer interaction.
Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans
and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion.
The second application domain adds decision making to the human-robot task. Rational
decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the
adjustable autonomy system experimentally. Results from two experiments are reported: a
quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability.|
|Rights and Permissions: ||The author retains copyright of this thesis.|
|Type of Work: ||PhD Doctorate|
|Appears in Collections:||Sydney Digital Theses (Open Access)|
This work is protected by Copyright. All rights reserved. Access to this work is provided for the purposes of personal research and study. Except where permitted under the Copyright Act 1968, this work must not be copied or communicated to others without the express permission of the copyright owner. Use the persistent URI in this record to enable others to access this work.
|kaupp08_phdthesis.pdf||5.66 MB||Adobe PDF|
Items in Sydney eScholarship Repository are protected by copyright, with all rights reserved, unless otherwise indicated.