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Environment classification using Kohonenself-organizing maps
Kevin Burn and Geoffrey HomeCentre for Hybrid Intelligent Systems, School of Computing and Technology, University ofSunderland, UKE-mails: [email protected]; [email protected]
Abstract: This paper describes a new method for classifying three-dimensional environments in real time using
Kohonen self-organizing maps (SOMs). The method has been developed to enable autonomous underwater
vehicles (AUVs) to navigate without human intervention in previously unexplored subsea environments, but can
be generalized to unmanned aircraft equipped with appropriate sensors flying over unchartered terrains, or
spacecraft exploring remote planets, subject to appropriate pre-mission training. The method involves a fuzzy
comparison between a SOM created in real time using accumulated sensor data and a class atlas of SOMs derived
from previously trained and manually classified environments. This enables mission- and environment-appropriate
AUV navigation strategies to be selected in real time. Simulation results using real-world, three-dimensional
environment data acquired from digital elevation maps are presented, which demonstrate the potential of the method.
Keywords: Kohonen self-organizing maps, autonomous underwater vehicles, robot control architectures,terrain classification
1. Introduction
An autonomous underwater vehicle (AUV) is
an unmanned, untethered vehicle, which carries
its own power source and relies on an on-board
computer with mission-specific designed control
software to achieve its goals. The latter normally
consists of a series of pre-programmed instructions
that operate on data acquired by the vehicle
sensors, and the design of control system archi-
tectures is a major issue in AUV development
(Yuh, 1995; Arkin & Balch, 1997; Valavanis et al.,
1997). This is due both to the high-dimensional
sensor data and computationally intensive
processing required, and to the navigational
and control constraints imposed by hostile subsea
environments. Navigation and mission man-
agement are particularly crucial if AUVs are
to perform more sophisticated tasks in the
future, and the ability to navigate in previously
unknown, unstructured, large-scale environ-
ments is one of the most difficult tasks to
achieve. Techniques developed for smaller-
scale robotics in two-dimensional scenarios
are not usually transferable to natural, three-
dimensional (3D), global-scale topography
navigation in dynamic subsea environments.
The design ofAUV control systems is therefore
a significant challenge and numerous approaches
to AUV architecture design have been under-
taken over the years (Ridao et al., 1999, 2000;
Coste-Maniere & Simmons, 2000), including
those that have attempted to exploit intelligent
control techniques to improve the autonomy
and performance of AUVs (Kortenkamp et al.,
1998; Roberts & Sutton, 2006). In parallel,
many of these techniques have had a significant
impact in the related fields of both industrial
and artificially intelligent robotics (Ross, 1995;
Linkens & Nyongesa, 1996; Burn & Bicker,
Article _____________________________
98 Expert Systems, May 2008, Vol. 25, No. 2 c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd
2000; Murphy, 2000; Burn et al., 2003, 2005).
However, the ability of the current generation of
AUVs to navigate through unexplored subsea
terrains without human operator intervention
remains severely limited (Home, 2006).
This paper describes part of a new hybrid
AUV controller architecture termed CETUS
(Classification of Environment and Terrain
by Unsupervised Self-organizing maps), which
is being developed as part of a research pro-
gramme aimed at improving the capabilities of
survey class AUVs. The motivation is to develop
an intelligent AUV capable of self-navigation in
previously unknown subsea environments,
where no a priori maps exist and the AUV must
rely on sensor data acquired in real time in order
to select appropriate navigation strategies. At
the heart of the system is an artificial neural
network based method to classify environments
dynamically using a Kohonen self-organizing
map (SOM).
The SOMwas used for its topology preservation
capability and to discover the underlying struc-
ture of the topographical data, thus reducing
complex real-world topography to manageable
dimensions and permitting classification. In this
work, the essence of topographical classification
is the retention of spatial disposition of all its
features; therefore we have selected the SOM to
reduce dimensionality. An alternative approach
could be to use the Karhunen–Loeve transform
to extract features, but here we have used the
SOM to first reduce the data dynamically and
then to extract properties from that reduction by
using best matching units (BMUs) and k-means.
We do not preclude other dimensionality
reduction methods and hybrids but are satisfied
that the SOM has performed well in the dynamic
simulations. Statistical methods also exist, such as
generative topographical mapping, but as we
are working with discrete measurements in these
simulations such approaches are not required.
The simulation inputs are processed real-time
sonar returns obtained as the AUV traverses its
environment, and the outputs are navigation
strategy selectors. To achieve this, CETUS
compares the dynamically created SOM with
an atlas of pre-trained SOMs, created with the
aid of subjective human classification of real-
world terrain. This enables suitable navigation
strategies to be selected from a navigation beha-
viour atlas. The method is described in detail,
together with the results of simulation studies
aimed at validating the concept.
Some recent work by Ishii et al. (2002, 2004)
has also researched using the SOM in AUV
navigation, primarily for collision avoidance using
the ‘Twin-Burger’ vehicle. However, the present
paper addresses the strategic control level and
collision avoidance is handled by the Sunderland
Fuzzy Adaptive Controller, which enables prob-
ing of the environment rather than collision
avoidance. The simulations were run during a
six-year period starting from January 2000.
The paper is structured as follows. Section 2
outlines the Kohonen SOM and explains why it
is suitable for the application described. The
subjective classification of naturally formed
terrain is discussed in Section 3, together with a
description of digital elevation maps (DEMs),
which were chosen to facilitate the process and
enable realistic simulation studies to be under-
taken subsequently. The offline training of SOMs
based on this classification is also described
and the results of real-time simulations using
the technique are then presented in Section 4.
Finally, conclusions and future work are outlined
in Section 5.
2. Data classification
The Kohonen self-organizing map, or SOM, is a
computationally efficient neural network that
mimics the way the mammalian brain physically
maps sensory inputs. It is used in a wide variety
of applications, including dimensional clustering,
visualization, feature abstraction and data mining
(Kohonen, 2001; Malone et al., 2006). The basic
SOM, as shown in Figure 1, is used to map high-
dimensional data onto a lower-dimensional output
space in the formof a two-dimensional rectangular
grid, where the input layer is fully connected to
the output layer and the output neurons have
lateral connections to their neighbours.
The basic SOM learning algorithm can be
summarized as follows (Negnevitsky, 2002).
c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd Expert Systems, May 2008, Vol. 25, No. 2 99
When an input pattern is presented to the net-
work, each neuron in the output layer receives a
copy of the pattern, modified by the synaptic
weights between the input and output layers.
The neuron with the largest activation level
becomes the winner-takes-all neuron, the output
of which is set to one, and the outputs of all
other neurons are set to zero. The lateral con-
nections enable the SOM to learn competitively,
since the lateral connections in the output layer
produce excitatory or inhibitory effects, depend-
ing upon the distance from the winning neuron;
the so-called Mexican hat function is often used
to define this effect as a function of distance
from the winning neuron. The neighbourhood
size generally shrinks as training progresses,
until only one neuron is active. This property is
very important for topology preservation.
The standard competitive learning rule defines
the change Dwij applied to synaptic weight wij as
Dwij ¼ aðxi � wijÞ2 if neuron jwins the competitionDwij ¼ 0 if neuron j loses the competition
ð1Þ
where xi is the input signal and a is the learning
rate parameter (0rar1). The winning neuron
is that with the minimum Euclidean distance
between the input vectorX and weight vectorWj
such that
dmin¼ jjX�W j jjmin¼Xni¼ 1
ðxi � wijÞ2" #1=2
min
ð2Þ
where xi and wij are the ith elements of vectors X
andWj respectively.
The main characteristic of the SOM relevant
to this work is its topology-preserving mapping
of input data to output neurons, enabling it to
be used as a clustering, visualization and analysis
tool for high-dimensional data. Thus, for the
AUV application described here, the SOM was
identified as a computationally efficient means of
encoding the complex topological information
inherent in real-world environmental data, as
part of a novel intelligent navigation scheme
involving classification of environment and
terrain. In particular, a two-stage SOM archi-
tecture was used, where the first stage employed
an offline unsupervised mode and the second a
real-time unsupervised mode. Conceptually, the
scheme can be summarized as follows.
� Subjective human classification of real-world
environments is undertaken to produce a
limited number of generic and representa-
tive environmental categories, each of which
has been assigned specific navigation strategy
sets.
� SOM analysis of representative samples of
these classifications is performed, both to
visualize the high-dimensional content,
thereby improving the subjective descriptors
described above, and to establish and refine
SOM network modelling parameters.
� The trained SOMs are then used to establish
a class atlas of memory maps, to reside in the
AUV environment class memory.
� Real-time navigation with online SOM ana-
lysis is then possible, using data acquired
during an AUV mission. To achieve this
for a particular unchartered environment, a
dynamic, unsupervised SOM was created in
real time, using sensor data acquired during
an AUV mission in that environment. At
regular intervals, this is compared to the
pre-trained SOM in the class atlas using a
fuzzy logic based scheme to determine the
class(es) to which the environment belongs.
This enables dynamic classification of the
environment and the allocation of local and
global navigation strategy sets.
Since the SOM is robust and can be a computa-
tionally efficient network with light computational
demand, it was considered particularly appro-
priate for the real-time decision-making process
xInput layer
Output layer
w…
x…
Figure 1: Basic SOM architecture.
100 Expert Systems, May 2008, Vol. 25, No. 2 c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd
described above. With reference to Figure 1, it
was decided that to implement the method three
inputs were required: (x1, x2, x3), where (x1, x2)
are coordinate positions relative to a local frame
of reference in the area being analysed and x3 is
the elevation relative to a local zero, or datum,
reference. An overview of the complete naviga-
tion scheme is shown in Figure 2. Further details
of how this was achieved in simulation, using
realistic 3D topological data to represent subsea
terrains, follows in the next section.
3. Subsea environment classification
This paper is concerned principally with the design
of the intelligent navigation scheme shown in
Figure 2, and the simulation work to validate the
method. In order to achieve the latter and test the
potential of the proposed method, a major
problem that needed to be addressed was sourcing
real-world environmental data, with sufficient
detail and diversity to be used in the classification
process and simulation work. There was also a
requirement for enough data to allow both offline
classification and independent testing in the form
of real-time simulations in previously unclassified
and unexplored environments. In the literature,
there were no readily available benchmarking
tools cited for AUVs, i.e. in the form of bench-
mark environments for navigation, although
this was expected since there is no consensus
for benchmarking of robotic systems generally
(EURON Research Roadmaps, 2004). However,
a solution to the data problem was found in the
form of United States Geological Survey DEMs,
described in the following section.
3.1. Digital elevation maps
A DEM is a digitized map which is expressed in
matrix form and generally represents an area of
1.26 � 1.26 km2. In simple terms, it can be
thought of as an array where each element is a
coordinate position at which is stored an elevation
in metres. Thus, in principle, DEM[10, 2000]¼
Subjective (human)
classification of DEM database
SOM training Class atlas
Real-time SOM
(DEMSOM)
Comparator Mission map accumulator (DEM data)
∑
Navigation strategy
execution
Low level avoidance behaviours
AUV thruster commands
xxx
Offline
Real-time
Figure 2: Intelligent navigation scheme.
c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd Expert Systems, May 2008, Vol. 25, No. 2 101
300 means that at (x, y) coordinates (10, 2000)
the elevation is 300 m. In reality, the situation is
more complex, as the integer is coded and within
the DEM format there are numerous factors to
apply, which represent axis definitions, base
elevations, geometric projection and so on (US
Geological Survey, 1998). With suitable filters,
however, DEM data can be read into a large
array for subsequent manipulation, e.g. in
SOM data reduction and classification, or as a
real-world data source for simulated sonar during
experiments. Thus, in this research, where all
simulation was performed in a Matlab environ-
ment, such filters and associated manipulation
routines were written as Matlab ‘m’ code.
DEMs are a rich source of real-world data
and in this work 1000 DEM maps have been
collated and representative groups classified
by SOMs for use during mission simulations.
A ‘mission DEM’ of a type not previously
classified was then used as real-world data for
acquisition by the simulated sonar in order to vali-
date both the method and, in the wider context,
the AUV architecture under development. Such
acquired data represent what the AUV senses as
its universe and these data are dynamically classi-
fied and compared to the pre-mission knowledge
base. The resulting classification determines the
self-selected navigation behaviours, which in turn
determine the tracking and subsequent augmenta-
tion of the AUV’s universe.
Example DEMs, as displayed by 3D OpenGL
DEM visualization software 3DEM, are shown
in Figure 3, and a Matlab 3D representation is
shown in Figure 4. Prior to the SOM analysis of
the DEM data described below, it was decided
that only the relative vertical profile was required.
Accordingly, established DEM surveying datums
were removed and the data were reduced with
reference to highest numerical value as follows:
zðx; yÞ¼ zd� zdmin
ðzdmax � zdminÞð3Þ
where z(x, y) is the numeric value in the pre-
processed DEM map at grid (x, y); zd is the
DEM numeric value in the raw data at (x, y);
zdmin is the lowest point in the DEM training
map, or the lowest point during an AUV mis-
sion; zdmax is the highest point in the DEM
training map, or the highest peak during an
AUV mission.
Note that during the real-time experiments
zdmax and zdmin were by necessity moving datums,
since the AUV does not know their absolute
values for the entire area as it traverses unchar-
tered terrain. In contrast, this information is
available when training the pre-classified SOMs
offline. Therefore, the comparison between
real-time and pre-classified SOMs is based
entirely on features, without reference to their
absolute magnitudes.
3.2. Research methodology
The research methodology, outlined conceptually
in Section 2, is summarized as follows.
(a) An initial subjective analysis of representative
terrains based upon geological formation was
undertaken and potential AUV navigation
strategies were developed.
(b) A parent population of over 1000 DEMs
was created as the real-world database.
Half were subjectively analysed accord-
ing to visual features, as displayed by the
3DEM software. Directory structures
were created which filed similarly classed
DEMs, creating a hierarchical structure
for both the DEM classification and a
learning base for SOM analysis.
(c) A subsample was selected and analysed for
feature extraction using SOMs. This created
the parameters by which the real-time AUV
could compare its perceived environment
using a fuzzy inference process, and select
its navigation behaviour sets by comparing
dynamic SOMs (DEMSOM) with pre-
classified SOMs in the class atlas. Test
samples were also selected to experiment
with SOM behaviour and set real-time
SOM simulation parameters, such as dealing
with sparse matrices at the start of simula-
tion. This sampling enabled simulation
parameters to be set.
(d) Further environments were selected for
testing under simulation, some of which
were deemed to be multi-faceted, i.e. diffi-
102 Expert Systems, May 2008, Vol. 25, No. 2 c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd
cult to classify subjectively, as they
appeared to contain diverse features. As
such they were considered ideal candidates
for validating the method, as they had
never been classified and represented a
particular challenge to navigation.
(e) Dynamic simulations were launched
where the only data supplied were start
and finish points, plus the mission state-
ment ‘classify the area’. These simulations
on four selected environments were used
to test and demonstrate how effectively
the method could be used to dynamically
class a previously unknown environment,
using the comparison between the real-
time DEMSOM and the pre-classified
SOMs.
Key aspects relating to these steps are described
in more detail in the following sections.
(b)(a)
(c) (d)
Figure 3: Examples of DEMs: (a) plains; (b) crenulated; (c) ridge chain; (d) complex mountain.
c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd Expert Systems, May 2008, Vol. 25, No. 2 103
3.3. Subjective classification of environments
The first stage is a subjective, i.e. descriptive,
classification of different types of environment
and terrain, in order to link generic classes of
environments with potential navigation strate-
gies. This is an iterative process, which begins
with a review of geological processes involved
in the formation of different terrains. Classes
derived at this stage are further refined following
extensive DEM analysis and studies of SOMs
trained offline.
It is helpful to consider the geological processes
as this can provide an initial guide for creating
terrain classes. For example, mountainous terrain
can be created by continental plate collisions or by
isolated volcanic activity – in the first case a class
of ‘mountain chain’ can be assigned, and for the
latter ‘isolated peak’ is more appropriate. From
geological knowledge it is known that early
planetary formations are orogenic, i.e. volcanic
structures are created and entire mountain
ranges are up-thrust. This process creates the
higher, more complex topologies, whilst evolu-
tionary processes modify these profiles, creating
U-shaped valleys by glacial activity and forming
sedimentary river deltas. Secondary features can
also form by extraterrestrial activity such as
meteor impacts and these can be significant
features. On land, other significant features
occur at the boundaries of flat plains, or water-
laden areas, i.e. relatively flat areas, bounded by
more complex topography. The relative heights
and scales of all these features to the navigating
vehicle are major considerations. In addition,
the spatial disposition of such features is of
prime consideration; it is this disposition which
can be meaningfully clustered by the SOM and
classified by fuzzy logic.
The exercise provided a useful insight into
identifying which terrain features were signifi-
cant and which would subsequently be encoun-
tered when subjectively analysing the DEMs.
The purpose of this second stage of analysis was
to enable these preliminary class descriptors to
be refined, together with a more detailed assess-
ment of AUV navigation strategies to be adopted
when traversing different subsea environments. A
high degree of terrain repeatability was revealed
in the investigation, with master classes categor-
ized in terms of complexity of navigation. Four
master classes were identified at this stage,
examples of which are shown in Figure 3 and
presented in Table 1, together with an over-
view of appropriate navigation behaviours for
those classes.
Figure 4: 3D Matlab
representation of DEM
data.
104 Expert Systems, May 2008, Vol. 25, No. 2 c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd
3.4. SOM analysis of subjectively classified
environments
The purpose of training SOMs offline is to create
a class atlas database that can be used in the real-
time classification process. Thus, it provides
benchmark environments against which SOMs
created in real time can be compared, as an AUV
traverses an unknown environment. Addition-
ally, this offline training fulfils the following
important roles during the classification process.
� It enables high-dimensional DEMs to be visua-
lized in simplified form, which helps in the
refinement of the initial subjective classifica-
tions summarized in Table 1. In particular, it
facilitates the rapid identification of the
range and distribution of data clusters.
� The effects of missing data, sonar spread,
vehicle scaling, and of varying Kohonen
projection grid sizes and modelling para-
meters are investigated at this stage and
measures of artificial neural network accu-
racy are defined.
� It assists in the development of the fuzzy rule
base used to compare the real-time SOM
with those in the class atlas.
With respect to network size, there is clearly a
trade-off between speed of classification and the
effective retention of important environmental
features. During this investigation, experiments
were conducted using network sizes ranging
from 5 � 5 to 1200 � 1200 neurons. Typically,
SOMs in the range 5 � 5 to 10 � 10 neurons
took less than 20 seconds to converge during
Table 1: Environment master classes and navigation behaviours
Class Navigation behaviours
Plains Plains require a gentle undulating-amplitude algorithm such as sinusoidal or Fouriertransform with obstacle avoidance. A typical application would be searching for debris onthe seabed. The AUV maintains depth and heading in accordance with these algorithms,and monitors distances to features while maintaining a safe distance. In addition, surfaceprobing can assist to determine if material can be passed through, e.g. the feature may be astratified dense salt band, or could be solid seabed such as seabed built from alluvialdeposits in previous low current land regimes. Specific features can be mapped into themission such as object shapes and these can be used as behaviour modifiers. Pipeline routesand corridors would normally be defined in plains and pipeline tracking algorithms can beused for existing pipelines
Crenulated A crenulated surface, i.e. one that has castellations, which appear in plan as a toasted bunshape, is created from glacial action flattening the peaks of a mountain range. Anapplication in this terrain could be to search for alluvial deposited minerals that were laindown before flooding. In this case the strategy would be to map the course and size ofchannels whilst probing the surface for deposition features. The algorithm can include ameasure of ‘lawn mowing’ or weaving
Ridge chain The ridge chain is a linear feature formed from a single orogenic upheaval, forming a singleridge often as harder material than the original bedrock. The amplitude of the ridge chaincan vary significantly along the feature and such a feature could also be formed from dykeintrusions. If the interest is in the intrusion itself then the navigation behaviours wouldinclude a measure of semi-linear feature following in the x and y directions, with a varyingAUV elevation. When the terrain classification changes towards plains then clearly eitherthe extent of the ridge has been mapped or the AUV strategy requires correction bybacktracking
Complexmountain
A complex mountain consists of well-distributed peaks of numerous amplitudes andwavelengths. A mission tasking could be to find a time-efficient way to transit thetopography. This would then be a search for passes and the evaluation of the most efficientflight level required. Behaviours would include climb over and circumvent peaks and toexplore apparent openings, with backtracking to prevent trapping
c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd Expert Systems, May 2008, Vol. 25, No. 2 105
training, whilst at the other extreme those with
1200 � 1200 neurons took over 2 hours to con-
verge, rendering them impractical for real-time
operation. Importantly, it was also found that
essential features of the environment, in parti-
cular dominant peaks and troughs that are most
significant to vehicle navigation strategies, were
retained in relatively low-dimensional SOMs.
Ultimately, SOMs of 8 � 8 neurons were found
to be adequate in this application.
The unified distance matrix, or U-matrix, of
Ultsch and Siemon (1990) was used to assist in
the visualization of the different environment
classes. This is a SOM representation in which
shades of grey or colour are used to indicate
distance of connected neurons, and hence topo-
logically preserved relationships. Examples of
U-matrices derived in this work are shown
in Figure 5, where small distances are shown in
yellow=red (lighter shades in the figure) and
large distances in blue (dark shades). Thus, if
distances are small, this represents a cluster
of patterns of similar characteristics. Inter-
pretation of U-matrices can be challenging
U-matrix
U-matrix
SOM 25-Jan-2005
SOM 25-Jan-2005
1880
957
30.3
5450
2820
181
(a)
(b)
Figure 5: Examples of U-matrices: (a) steep plains bordering mountain peaks; (b) plains and single
isolated peak.
106 Expert Systems, May 2008, Vol. 25, No. 2 c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd
even though the data represented are much
more complex than the matrix itself. The means
used to classify the U-matrices here was to
group and compare U-matrices from environ-
ments which had already been subjectively
classified.
To aid the visualization and classification
processes, plots of BMU fuzzy isopleths were
generated, e.g. as shown in Figure 6. A BMU is
a neuron with a weight vector which is most
similar to the input, and by plotting BMUs the
best representation of the terrain is mapped.
Thus, by counting contours and calculating
areas, a numerical description can be formed.
Following this additional analysis, the main
classifications summarized in Table 1 were further
categorized into subclasses, as illustrated in
Table 2. Information derived from the BMU
Figure 6: Examples of fuzzy BMU plots: (a) steep plains bordering mountain peaks; (b) plains and
single isolated peak.
c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd Expert Systems, May 2008, Vol. 25, No. 2 107
plots, together with cluster ranges obtained
from U-matrices, are then used to create the
rule base for the fuzzy comparator, as described
in the following section.
We draw a distinction in Table 2 between pre-
classified reference environments and dynamic
classification during navigation. Table 2 is
grouped by increasing complexity of features;
however, during simulation, primary and secon-
dary refer to the discovered features, i.e. an
environment dominated by plains and isolated
peaks respectively would be primary¼plains,
secondary¼peaks, whilst peaks with plateau
areas would be primary¼peaks and second-
ary¼ plains, regardless of the cataloguing
used for reference. Navigation behaviours are
selected in accordance with the dynamic classifi-
cations and the mission objectives.
3.5. Fuzzy classifier
Despite many researchers describing a wide
range of SOM applications, relatively few have
reported how a SOM might be used for rule
extraction in an expert system (Malone et al.,
2006). However, in order to compare real-time
SOMs with their pre-trained counterparts in real
time, this type of analysis is required and it is not
a straightforward problem.
Several approaches were investigated during
the research, the most successful of which in
terms of potential for software automation is
in the analysis of data used for the BMU plots.
In particular, it was established that the data
enabled a differentiation between the classes and
subclasses shown in Table 2. Furthermore, it
was deduced experimentally that a high degree
of navigation consistency could be obtained
by employing a fuzzy inference procedure to
perform the required comparison between the
real-time and pre-trained SOMs.
A diagrammatic representation of the fuzzy
inference system adopted is shown in Figure 7
and its essential features are summarized as
follows. There are two inputs – BMU range
and number of BMU clusters, obtained from
the real-time SOM – which are input to four sets
of membership functions (MFs). These trape-
zoidal MFs were derived from the pre-mission
analysis of subjectively classed environments.
The number of MFs for a particular environment
class represents the expected number of clusters
for that class and their minimum, maximum and
mean values, so that their distribution in the input
space reflects corresponding BMU values deter-
mined during the offline analysis. This informa-
tion is summarized in Table 3.
A linguistic rule base was developed to reflect,
for example, the relative ‘peakiness’ of real-time
data, in terms of height and distribution for each
unknown environment being traversed. This
was linked to a corresponding set of output
MFs. A centroid-based method of defuzzifica-
tion was then employed to produce a crisp out-
put, which in practice was the most likely
class=subclass to which the environment being
traversed belongs.
4. Simulation results
In analysing simulation results it is important to
realize that the ultimate goal is to enable an
AUV to automatically navigate an unknown
environment, and development of a classifica-
Table 2: Primary and secondary environment
master classes
Identifier
Low relief (plains)C1 Flats and dipsC2 Flats
Undulating relief (crenulated)C3 Plains and flatsC4 Plains and flats preceding peak rangesC5 Plains and flats following peak rangesC6 Undulating plains
Peaks – low to medium (ridge chains)C7 Peaks, low, steep slopeC8 Peaks, medium, steep slopeC9 Peaks, medium, extreme slope
Peaks – high to extreme (complex mountain)C10 Peaks – high, steep slopeC11 Peaks – extreme, steep slopeC12 Peaks – extreme, extreme slopeC13 Peaks – extreme, following plateau
108 Expert Systems, May 2008, Vol. 25, No. 2 c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd
tion system is a part, although an important
part, of a holistic solution.
The system has been tested by performing
multiple simulated AUV missions over four
previously unknown environments. These are
shown in 3D topographical form in Figure 8.
Five missions were undertaken over four differ-
ent DEMs (DEM1 toDEM4), where each DEM
was judged to comprise a broad spectrum of
primary and secondary environmental classes.
Figure 9 illustrates the mission directions for
each of the DEMs. Thus, the simulated AUV,
modelled as a 1 m diameter sphere and equipped
with sonar to detect depth, was tasked with
exploring and classifying the environment by
broadly criss-crossing the domain in each of five
directions, whilst maintaining appropriate and
safe navigation strategies. All that was defined
were the start and end points, with the actual
mission track profile being determined by
selection of navigation behaviours, based on
dynamic classification of the environment.
These coordinates are only used for simulation
convenience, i.e. as a means of both starting and
finishing the simulation. In addition, although
not directly relevant to this paper, fundamental
modelling of the AUV kinematics and hydro-
dynamics were incorporated within the simu-
lated missions.
For comparative purposes, subjective pri-
mary and secondary classifications across the
entire domains were undertaken post mission
and are shown below in italics for each environ-
ment. Tables 4–7 are sample SOM classifica-
tions derived at 200 m increments during the
real-time simulations. These are representative
results reproduced for clarity, since for each
mission classifications were actually performed
at step increments of 20, which corresponded
BMU clusters
BMU range
Complex mountain
Ridge chain
Crenulated
Plains
FIS
Class 1 C1-C2
Class 2 C3-C6
Class 3 C7-C9
Class 4 C10-C13
Figure 7: Fuzzy inference system (FIS) for SOM comparison.
Table 3: SOM classification zones and MFs
Class MF BMU range Clusters
Plains PL1 520 590 70 8PL2 520 600 80 10PL3 540 590 50 6PL4 530 590 62 8PL5 527 593 66 8.0
Crenulated CR1 400 550 150 4CR2 480 580 100 6CR3 350 550 200 6CR4 480 600 120 7CR5 428 570 143 5.8
Ridge chains RC1 440 580 140 8RC2 350 550 200 6RC3 400 550 150 4RC4 300 550 250 6RC5 372 557 185 6.0
Complexmountain
CM1 400 550 150 4.0
c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd Expert Systems, May 2008, Vol. 25, No. 2 109
to a distance of 2 m and a time increment
of 20 seconds. Note that the primary class
identified during these experiments is shown
in bold.
These results show that the missions demon-
strate a high degree of navigation consistency, in
relation to both primary and secondary classes,
and the derived descriptors closely match the
subjective classifications. Also, from whichever
direction the mission is initiated, a similar clas-
sification is discovered. The actual classification
timeline in each case is fundamentally different,
however, as the data are acquired in a different
sequence, and the overall classification can
be best summed from the individual missions.
We have found that this type of sampling
means an environment can be classified without
mapping the entire area, although there may
be other terrain types which do not conform.
In addition the purpose of classification is to
drive the behaviour sets according to the mission
objective(s).
Environment 1, subjective classifications:
Primary class – peaks, high and extreme
with steep and extreme slopes (C10–C12)
Secondary class – peaks, low and medium
with steep and extreme slopes (C7–C9)
Environment 2, subjective classifications:
Primary class – undulating plains with low
and medium slope peaks (C6–C8)
Secondary class – flats and plains, isolated
peaks (C2–C3)
Environment 3, subjective classifications:
Primary class – flats and dips (C1–C2)
Secondary class – isolated distributed
peaks, medium and extreme, plateau (C9–
C13)
Figure 8: 3D topographical representation of experimental missions: (a) environment 1; (b) environment
2; (c) environment 3; (d) environment 4.
110 Expert Systems, May 2008, Vol. 25, No. 2 c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd
Environment 4, subjective classifications:
Primary class – peaks, high and extreme
with steep and extreme slopes (C10–C12)
Secondary class – undulating plains with
low, medium peaks (C6–C7)
A typical mission profile, plotted in 3D, is
shown in Figure 10, where the computed track is
overlain on theMatlab DEM plot. Note that the
lighter circles represent the points at which
a SOM was computed and are denoted as
‘SOMspots’.
+Y
5
3
2
1
4
+X
Figure 9: Mission directions.
Table 4: Simulation results, environment 1
Mission 200 400 600 800 1000 1200
1 C10 C11 C11 C11 C11 C122 C11 C10 C11 C11 C11 C103 C11 C11 C11 C10 C11 C104 C11 C11 C10 C11 C11 C75 C10 C11 C9 C11 C11 C11
Table 5: Simulation results, environment 2
Mission 200 400 600 800 1000 1200
1 C6 C8 C7 C6 C6 na2 C6 C7 C7 C7 C6 C6
3 C6 C7 C6 C7 C9 C104 C2 C2 C8 C8 C8 C85 C8 C10 C7 C6 C6 C10
Table 6: Simulation results, environment 3
Mission 200 400 600 800 1000 1200
1 C2 C5 C2 C3 C10 na2 C1 C1 C1 C1 C1 C1
3 C2 C2 C2 C10 C11 C124 C11 C13 C13 C13 C12 C125 C2 C6 C6 C7 C7 C7
Table 7: Simulation results, environment 4
Mission 200 400 600 800 1000 1200
1 C8 C7 C6 C8 C8 C102 C7 C8 C7 C6 C6 C63 C10 C10 C9 C10 C11 C11
4 C13 C12 C10 C9 C11 C115 C7 C10 C7 C7 C11 C10
c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd Expert Systems, May 2008, Vol. 25, No. 2 111
5. Conclusions
A novel method has been developed to enable
AUVs to dynamically classify subsea terrains
in previously unexplored environments. We
experimented with SOM network dimensions
and learning parameters prior to starting
simulations using Kohonen’s SOM_PAK. It
was discovered that the algorithms rapidly
converged unless a high-dimensional output
was specified. At the extremes an entire environ-
ment was processed with both the discrete map
size, i.e. a ratio of 1200:1200, and with 1200:5.
Only in the former case was the convergence
computationally expensive, whilst the latter pro-
vided a sufficiently low-dimensional output for
analysis and was within real-time convergence
criteria. A typical time-frame is shown in Figure 11
compared to a typical operating velocity of the
test AUV. The distinction is drawn between a
relatively slow exploration vehicle and a fast
moving projectile.
The method involves a two-part application
of Kohonen SOMs, which have been selected
for their rapid convergence and data dimension-
ality reduction properties. The first part applica-
tion is where SOMs are trained offline and used
to create a class atlas of environments that
have already been subjectively classified. In the
second part, dynamic SOMs are trained during
an AUV mission using real-time data. A fuzzy
comparator is employed to allow the AUV to
periodically classify its environment, by com-
paring the dynamic SOMwith the offline SOMs.
Various forms of SOM mapping were experi-
mented with, including map shape, map size and
initialization, before running the simulations.
A random initialization was finally selected
together with the default training parameters of
55 10
2m at 0.1m s = 20 seconds
AUV
SOM SlackSonar
Seconds
Figure 11: Dynamic SOM time-frame.
Figure 10: Typical
mission profile.
112 Expert Systems, May 2008, Vol. 25, No. 2 c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd
‘som_make’ within SOM_PAK, which proved
to be the most reliable for convergence. The
training structure in Matlab is initialized ran-
domly with a Gaussian neighbourhood function
and a radius of 0.5 � (map size). For the coarse
training phase the neighbourhood radius is
(initial radius)=4 with a final value of unity.
Two phases of training are used with epoch
lengths defined as 4*mpd and 16*mpd for the
coarse and fine periods, where mpd is defined as
(number of map units)=(training data length). Alearning rate of a¼ 0.5 is used for coarse train-
ing, with a value of a¼ 0.05 for fine training.
The method has so far been verified under
simulation, using simulated sonar returns from
real-world 3D environments provided by DEMs.
Whilst at an early stage of research and devel-
opment, the potential for the method has been
demonstrated, with the simulated AUV success-
fully classifying and navigating through unknown
terrains. The rapid learning and reliability of the
Kohonen SOM has been shown to be suitable
for real-time assessment of environments and
could be extended to include more dimensions,
such as colour and surface textures. Navigation
tracks themselves are influenced not only by
recognizing the environment but also by the
strategic mission parameters.
Significant work remains to be done and more
simulations with different combinations of terrain
and mission objectives are required, as well as
better methods of clustering and partitioning
the SOM representations. With experience
gained from such simulations, the knowledge
base of the operational vehicle can be augmen-
ted and developed into the ultimate objective
of an intelligent, autonomous self-navigating
and self-protecting vehicle which understands
its own limitations, and with launch-and-forget
as the ultimate objective.
The next phase of the research programme is
to test the algorithms with a prototype vehicle,
the development of which is currently being
planned. In pursuit of the overall goal of produ-
cing a fully autonomous, self-navigating AUV,
with SOM classifications linked to navigation
strategies and behaviours, future studies will
include
� further classification of master topographical
classes;
� development of navigation strategy behaviour
subsets, based on experimental results with
software simulation and a prototype AUV;
� development and experimentation with data
acquisition devices (principally sonar), and
pre-processing of raw data in a form readily
accessible by a prototype AUV.
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The authors
Kevin Burn
Kevin Burn received his BSc degree in mechanical
engineering from Newcastle University in 1984
and his MEng fromDurhamUniversity in 1986.
After working in the power industry for several
years he returned to Newcastle University as a
Research Associate and was awarded his PhD in
1994 for research into robotics and teleoperator
systems. He is currently a Senior Lecturer in
Computing and Engineering at the University
of Sunderland and has research interests in
robotics, control and intelligent systems.
Geoffrey Home
Geoffrey Home received his BSc degree in civil
engineering from Sunderland Polytechnic in 1972.
After working on bridges and other structural
engineering for seven years, he spent over 25 years
in the international oil and gas industry. Following
six years of part-time study he was awarded his
PhD in 2006 for research into autonomous
intelligent robotics. He is currently a part-time
researcher at Sunderland University and Lead
Facilities Engineer on the AKCO Kazakhstan
Kashagan project.
114 Expert Systems, May 2008, Vol. 25, No. 2 c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd