Human-robot spatial interaction using probabilistic qualitative representations

Dondrup, Christian (2016) Human-robot spatial interaction using probabilistic qualitative representations. PhD thesis, University of Lincoln.

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Item Type:Thesis (PhD)
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Abstract

Current human-aware navigation approaches use a predominantly metric representation
of the interaction which makes them susceptible to changes in the environment. In order
to accomplish reliable navigation in ever-changing human populated environments, the
presented work aims to abstract from the underlying metric representation by using Qualitative
Spatial Relations (QSR), namely the Qualitative Trajectory Calculus (QTC), for
Human-Robot Spatial Interaction (HRSI). So far, this form of representing HRSI has been
used to analyse different types of interactions online. This work extends this representation
to be able to classify the interaction type online using incrementally updated QTC
state chains, create a belief about the state of the world, and transform this high-level
descriptor into low-level movement commands. By using QSRs the system becomes invariant
to change in the environment, which is essential for any form of long-term deployment
of a robot, but most importantly also allows the transfer of knowledge between similar
encounters in different environments to facilitate interaction learning. To create a robust
qualitative representation of the interaction, the essence of the movement of the human in
relation to the robot and vice-versa is encoded in two new variants of QTC especially designed
for HRSI and evaluated in several user studies. To enable interaction learning and
facilitate reasoning, they are employed in a probabilistic framework using Hidden Markov
Models (HMMs) for online classiffication and evaluation of their appropriateness for the
task of human-aware navigation.
In order to create a system for an autonomous robot, a perception pipeline for the
detection and tracking of humans in the vicinity of the robot is described which serves
as an enabling technology to create incrementally updated QTC state chains in real-time
using the robot's sensors. Using this framework, the abstraction and generalisability of the
QTC based framework is tested by using data from a different study for the classiffication
of automatically generated state chains which shows the benefits of using such a highlevel
description language. The detriment of using qualitative states to encode interaction
is the severe loss of information that would be necessary to generate behaviour from it.
To overcome this issue, so-called Velocity Costmaps are introduced which restrict the
sampling space of a reactive local planner to only allow the generation of trajectories
that correspond to the desired QTC state. This results in a
exible and agile behaviour
I
generation that is able to produce inherently safe paths. In order to classify the current
interaction type online and predict the current state for action selection, the HMMs are
evolved into a particle filter especially designed to work with QSRs of any kind. This
online belief generation is the basis for a
exible action selection process that is based on
data acquired using Learning from Demonstration (LfD) to encode human judgement into
the used model. Thereby, the generated behaviour is not only sociable but also legible
and ensures a high experienced comfort as shown in the experiments conducted. LfD
itself is a rather underused approach when it comes to human-aware navigation but is
facilitated by the qualitative model and allows exploitation of expert knowledge for model
generation. Hence, the presented work bridges the gap between the speed and
exibility
of a sampling based reactive approach by using the particle filter and fast action selection,
and the legibility of deliberative planners by using high-level information based on expert
knowledge about the unfolding of an interaction.

Keywords:Computer science, Robots
Subjects:H Engineering > H670 Robotics and Cybernetics
G Mathematical and Computer Sciences > G440 Human-computer Interaction
Divisions:College of Science > School of Computer Science
ID Code:28665
Deposited On:05 Sep 2017 09:49

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