17
Environment classification using Kohonen self-organizing maps Kevin Burn and Geoffrey Home Centre for Hybrid Intelligent Systems, School of Computing and Technology, University of Sunderland, UK E-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 of AUV 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

Environment classification using Kohonen self-organizing maps

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Page 1: Environment classification using Kohonen self-organizing maps

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

Page 2: Environment classification using Kohonen self-organizing maps

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).

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Page 3: Environment classification using Kohonen self-organizing maps

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

Page 4: Environment classification using Kohonen self-organizing maps

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.

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Page 5: Environment classification using Kohonen self-organizing maps

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

Page 6: Environment classification using Kohonen self-organizing maps

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

Page 7: Environment classification using Kohonen self-organizing maps

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

Page 8: Environment classification using Kohonen self-organizing maps

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

Page 9: Environment classification using Kohonen self-organizing maps

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

Page 10: Environment classification using Kohonen self-organizing maps

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.

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Page 11: Environment classification using Kohonen self-organizing maps

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

Page 12: Environment classification using Kohonen self-organizing maps

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

Page 13: Environment classification using Kohonen self-organizing maps

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

Page 14: Environment classification using Kohonen self-organizing maps

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

Page 15: Environment classification using Kohonen self-organizing maps

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

Page 16: Environment classification using Kohonen self-organizing maps

‘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.

References

ARKIN, R.C. and T. BALCH (1997) AuRA: principlesand practice in review, Journal of Experimental andTheoretical Artificial Intelligence, 9 (2), 175–188.

BURN, K. and R. BICKER (2000) Development of anon-linear force controller using fuzzy logic techni-ques, Journal of Systems and Control Engineering,214 (1), 197–206.

BURN, K., M. SHORT and R. BICKER (2003) Adaptiveand nonlinear fuzzy force control techniques appliedto robots operating in uncertain environments,Journal of Robotic Systems, 20 (7), 391–400.

BURN, K., G. HOME, M. SHORT and R. BICKER (2005)A software tool for automating the design of robotfuzzy force controllers, Robotica, 23 (2), 247–256.

COSTE-MANIERE, E. and R. SIMMONS (2000) Architec-ture, the backbone of robotic systems, Proceedings ofthe IEEE International Conference on Robotics andAutomation, New York: IEEE, 67–72.

EURON RESEARCH ROADMAPS (2004) Key Area 1on Research Coordination, European Robotics Re-search Network, IST-2000-26048, 3.10 UnderwaterSystems.

HOME, G. (2006) A hybrid intelligent architecture forAUVnavigation, PhD thesis, University of Sunderland,UK.

ISHII, K., S. NISHIDA, K. YANO and K. WATANABE

(2002) A navigation system for an underwatervehicle using the self-organizing map, Proceedingsof ISOPE 2002, Kyushu, Japan: ISOPE, 284–289.

ISHII, K., S. NISHIDA and T. URA (2004) A self-organiz-ing navigation system for an underwater robot,Proceedings of ICRA 2004, New Orleans, LA: IEEE,4466–4471.

KOHONEN, T. (2001) Self Organizing Maps, SpringerSeries in Information Sciences 30, Berlin: Springer.

KORTENKAMP, D., R.P. BONASSO and R. MURPHY

(1998) Artificial Intelligence andMobile Robots, CaseStudies of Successful Robot Systems, Boston, MA:MIT Press.

LINKENS, D.A. and H.O. NYONGESA (1996) Learningsystems in intelligent control: an appraisal of fuzzy,neural and genetic algorithm control applications,IEE Proceedings, Part D, 143 (4), 367–386.

c� 2008 The Authors. Journal Compilation c� 2008 Blackwell Publishing Ltd Expert Systems, May 2008, Vol. 25, No. 2 113

Page 17: Environment classification using Kohonen self-organizing maps

MALONE, J., K. MCGARRY, S. WERMTER and C.BOWERMAN (2006) Data mining using rule extrac-tion from Kohonen self-organizing maps, NeuralComputing and Applications, 15 (1), 9–17.

MURPHY, R.R. (2000) Introduction to AI Robotics,Boston, MA: MIT Press.

NEGNEVITSKY, M. (2002) Artificial Intelligence: A Guideto Intelligent Systems, Harlow: Addison Wesley.

RIDAO, P., J. BATLLE, J. AMAT and G.N. ROBERTS

(1999) Recent trends in control architectures forautonomous underwater vehicles, Journal of SystemsScience, 30 (9), 1033–1056.

RIDAO, P., J. YUH, J. BATLLE and K. SUGIHARA (2000)On AUV control architecture, Proceedings of theIEEE=RSJ International Conference on Robots andSystems, New York: IEEE, Vol. 2, pp. 855–860.

Roberts, G.N. and R. Sutton (eds) (2006) Advances inUnmanned Marine Vehicles, IEE Control Series,Stevenage: IEE Press.

ROSS, T.J. (1995) Fuzzy Logic with Engineering Appli-cations, New York: McGraw-Hill.

ULTSCH, A. and H.P. SIEMON (1990) Kohonen’s selforganizing feature maps for exploratory data analy-sis, Proceedings of the International Conferenceon Neural Networks, INNC90, Dordrecht: KluwerAcademic, 305–308.

US GEOLOGICAL SURVEY (1998) Standards for DigitalElevation Models – Parts 1–3, US Department of theInterior, National Mapping Division.

VALAVANIS, K.P., D. GRACANIN, M. MATIJASEVIC, R.KOLLURU and G. DEMETRIOU (1997) Controlarchitectures for autonomous underwater vehicles,IEEE Journal of Control Systems, 17 (6), 48–64.

YUH, J. (ed.) (1995) Underwater Robotic Vehicles: De-sign and Control, Albuquerque, NM: TSI Press.

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