|dc.contributor.author||Underwood, James Patrick||-|
|dc.description.abstract||This thesis is concerned with the algorithms and systems that are required to enable
safe autonomous operation of an unmanned ground vehicle (UGV) in an unstructured
and unknown environment; one in which there is no speci c infrastructure to assist
the vehicle autonomy and complete a priori information is not available.
Under these conditions it is necessary for an autonomous system to perceive the
surrounding environment, in order to perform safe and reliable control actions with
respect to the context of the vehicle, its task and the world. Speci cally, exteroceptive
sensors measure physical properties of the world. This information is interpreted to
extract a higher level perception, then mapped to provide a consistent spatial context.
This map of perceived information forms an integral part of the autonomous UGV
(AUGV) control system architecture, therefore any perception or mapping errors
reduce the reliability and safety of the system.
Currently, commercially viable autonomous systems achieve the requisite level of
reliability and safety by using strong structure within their operational environment.
This permits the use of powerful assumptions about the world, which greatly simplify
the perception requirements. For example, in an urban context, things that look
approximately like roads are roads. In an indoor environment, vertical structure
must be avoided and everything else is traversable. By contrast, when this structure
is not available, little can be assumed and the burden on perception is very large. In
these cases, reliability and safety must currently be provided by a tightly integrated
The major contribution of this thesis is to provide a holistic approach to identify and
mitigate the primary sources of error in typical AUGV sensor feedback systems (comprising
perception and mapping), to promote reliability and safety. This includes an
analysis of the geometric and temporal errors that occur in the coordinate transformations
that are required for mapping and methods to minimise these errors in real systems. Interpretive errors are also studied and methods to mitigate them are presented.
These methods combine information theoretic measures with multiple sensor
modalities, to improve perceptive classi cation and provide sensor redundancy.
The work in this thesis is implemented and tested on a real AUGV system, but the
methods do not rely on any particular aspects of this vehicle. They are all generally
and widely applicable. This thesis provides a rm base at a low level, from which
continued research in autonomous reliability and safety at ever higher levels can be
|dc.publisher||University of Sydney||en_AU|
|dc.publisher||Faculty of Engineering & Information Technologies||en_AU|
|dc.publisher||School of Electrical and Information Engineering||en_AU|
|dc.publisher||Australian Centre for Field Robotics||en_AU|
|dc.rights||The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.||en_AU|
|dc.subject||Autonomous Ground Vehicles||en_AU|
|dc.title||Reliable and safe autonomy for ground vehicles in unstructured environments||en_AU|
|dc.type.pubtype||Doctor of Philosophy Ph.D.||en_AU|
|Appears in Collections:||Sydney Digital Theses (Open Access)|