Disturbed Behaviour in Co-operating Autonomous
Robot
Robert Ghanea-Hercock
David P Barnes
BT Laboratories, Intelligent Systems Research,
Martlesham Heath, Ipswich, IP5 3RE, UK.
Dept. of Electronic & Electrical Eng.,
University of Salford, Salford, UK.
ghanear@info.bt.co.uk
d.p.barnes@eee.salford.ac.uk
ABSTRACT
Balancing the conflicting demands imposed by a dynamic world
on an autonomous robot requires a significant degree of
adaptability. This paper describes a multi-layer control system for
two co-operating mobile robots, which uses fuzzy logic to adapt
the relative importance of a set of reactive behaviours. The fuzzy
system therefore acts as an arbiter, which smoothly interpolates
control between conflicting behaviours. This allows the robots to
successfully navigate out of local potential minima.
The adaptive mechanism itself is also modified by an array of
vectors generated from an on-line analysis of the activity of each
fuzzy rule. From recent work on neural dynamics [Kelso, 17] the
strategy is to consider the control system as a dynamic structure,
and to achieve adaptivity through maintaining it in a disturbed or
stressed phase condition. This is achieved by monitoring the
matrix of fuzzy rules, and triggering a suppression of rules which
are driving the system into a stable state. We propose that for an
autonomous agent in an unstructured environment maintaining a
state of dynamic instability within the control system increases the
probability of the agent reaching its goal.
Keywords: Autonomous Robots, Adaptive, Fuzzy Logic.
1. INTRODUCTION
The field of autonomous robots has advanced significantly over
the past decade, since the advent of reactive or bottom-up
methods advocated by Brooks and Arkin [8,3]. Recent
developments however, have now emphasised a hybrid approach,
which aims to integrate the task planning capacity of traditional
A.I based architectures with reactive control methods, [12,25].
This approach appears to offer the best of both philosophies, in
providing an agent with strategic and local task achieving
abilities.
In this paper however, we investigate the possibility of
maximising the behavioural competence of autonomous robots as
work by our group [4] has indicated that for a hybrid method to be
efficient a planning agent or reflective system requires the robot to
adapt to complex local events, which require an intelligent local
management of its behaviours.
Such a robot is therefore a self-controlling system and the critical
design issue is related to selecting the appropriate motivation. By
definition it also means that we will be trading control over our
robots for flexibility and ability to survive in the real world. We
are therefore working towards the goal of increasing the autonomy
of our robots, which will improve their competence at negotiating
difficult environments. Section 3.3 considers the role of
motivation in facilitating autonomous decision making as part of
an adaptive fuzzy mechanism.
This paper therefore presents two hypotheses; firstly whether
using an adaptive FAM surface can increase the competence of a
mobile robot while navigating through its environment, and
second whether this system can be transferred onto physical
mobile robots.
Section 1.1 considers the importance of adaptivity in autonomous
robots, while section 2 briefly reviews the field of behaviour
based control. Section 3 then describes the methods involved in
our adaptive Fuzzy control system and its dynamics. Section 4
and 5 cover the implementation and results, with our conclusion
in section 6 .
1.1 Adaptivity
In the context of autonomous robots the ability to adapt is
essential if the robot is to shift from being an automaton, driven
by a set of behaviours, to acquiring more life like properties which
allow it to select appropriate combinations of behaviour.
For an autonomous agent the use of fuzzy logic to control its set
of possible behaviours provides several advantages. The primary
advantage is the ability to trace the inference paths through the
fuzzy control system, which provides a window onto an agent’s
internal state as it negotiates an environment. This feature of
Fuzzy systems is crucial in enabling the method of dynamic
analysis presented in this paper, as it allows the control system to
selectively tune each fuzzy rule according to the interactions of
the robots set of goals. This mechanism therefore provides the
robots with a means of selecting and balancing multiple
conflicting behaviours, on the basis of its stored short term
memory.
1.2 Disturbed but not shaken!
The key concept of this work is taken from recent evidence within
neuroscience, which supports the concept of a brain as a dynamic
system, rather than as a computer [17] and neurons as adaptive
systems [32], rather than passive switches. This shift in
perspective has major consequences for the way we approach
robot and animat design. Many existing autonomous robot
control structures use animat style reasoning, with various
combinations of genetic algorithms and neural networks [2,19 &
24]. However there has been little consideration of the dynamics
of the processes active within the robot, one exception being the
work by Beer [6]. The new perspective suggests we should be
more focused on the dynamic nature of natural neural systems,
which are in a state of constant activity. Section 3.3 outlines the
specific method used in this work to enhance the adaptivity of our
robots by dynamically modulating the fuzzy rule base.
1.3 Problem domain
The problem domain is based on the handling and transportation
of hazardous materials, such as in nuclear plant decommissioning.
The overall goal of this project was therefore to achieve a hybrid
multi-agent control architecture. This will allow the translation of
high-level user requests, through a planning agent, into low level
sequences of behaviours, which are radio transmitted to several
‘autonomous’ robots. The robots then work individually or
collectively to perform the sequence of behaviours they received,
(see [4] for details of the planning system). Figure 1. shows our
two co-operating robots ‘Fred & Ginger’, which were used to test
the fuzzy control system. Each robot operates as a separate
system, and is also capable of working co-operatively to transport
materials, (sec. 4 gives further details).
This scenario represents a serious real-world application of
multiple co-operating autonomous robots, which offers significant
challenges to the application of animat or adaptive strategies. It
was also a useful test of the applicability of transfering simulated
results to physical robots, which was successfully performed with
the results of this work.
2.0 BEHAVIOURS - PRIMARY CONTROL
Since the paradigm shift in robotics generated by [8] advocation
of layered reactive control, a substantial amount of work has
developed based on such ‘Behaviour’ oriented methodolgy.
Almost immediately however it was realised that both reflective
and reactive processes were needed to control autonomous agents
acting in the real world, [3]. This dual strategy has become
identified as the ‘hybrid’ approach, and examples include the
work by [1, 12, 24, 25, and 2]. Another viewpoint sees such
systems as essentially ‘hierarchical’ in design and creates a
division by function of the overall control system into separate
levels each of which is concerned with some aspect of
deliberation or reactive response.
While such hybrid approaches offer solutions to the overall task
domain of autonomous robots, within the reactive component
there are still significant problems to be resolved. This point is
stressed by Ashwin et al, in which they develop solutions via
genetic algorithms:
“Further, as behaviours are added to the system and its
complexity increases, the interaction of the various behaviours
often becomes difficult to predict and debug.” [2].
This paper offers a solution based on a Fuzzy logic control system
which aims to provide an automatic method of adjusting the
parameters of a set of primary reactive behaviours. This creates a
sub-hierarchy within our control architecture in which a
Reflective agent provides sets of task sequences to the cooperating robots and acts as the upper level in the overall control
hierarchy.
2.1 Behaviour Synthesis Architecture
The primary level of control within our system is a collection of
behaviours, which associate each sensory stimulus directly with a
motor response. The Behaviour Synthesis Architecture (B.S.A) is
a reactive control system which has been successfully applied to
the control of co-operating autonomous devices for an object
relocation task [5]. The problems of conflict resolution between
agents and their behaviours are resolved through a vector
synthesis mechanism. Each behaviour pattern is defined in terms
of a stimulus/response function. The respective functions have
associated stimulus/utility functions, which describe their
importance at any particular instance within a given dynamic
environment.
bpt = { rt = fr(st)}{u t= fu(st) }
(1)
In eq.1 rt is the particular motion response at time t and this is a
function, fr, of a given sensory stimulus, st. Associated to every
response is a measure of its utility or importance, ut. This
quantity is a function, fu, of the same sensory stimulus and the
values of rt and ut constitute a vector known as a utilitor.
Competing utilitors are resolved by a process of linear
superposition which generates a resultant utilitor, UX t where
m
ut , n . e
UXt =
j. r
t,n
n=1
(2)
and m equals the total number of related utilitors generated from
the different strategy levels. Given a resultant utilitor, a resultant
utility, uXt, and a resultant motion response, rX t are simply
obtained from:
uXt =
UXt
m
and
rXt = arg UXt
(3) & (4)
X corresponds to a particular degree of freedom, e.g. translate or
rotate, and the resultant motion response, rX t, is then executed by
the robot. From (2), it can be seen that generating a resultant
response from different behaviours within the architecture
constitutes a process of additive synthesis.
Figure 1. Fred and Ginger, autonomously transporting a section of pipe across the laboratory. On the top of each robot is a two d.o.f
manipulator mounted on a self-centring compliant x-y table.
The utility associated with each behaviour therefore provides an
ideal input with which to adaptively modify a behaviour’s
contribution to the control of the robot, from a higher control
level [15]. However, this utility function acts continuously and
normally only affects the specific behaviour it is associated with,
which is in contrast to the more complex concept of utility defined
in ethology or economics [23].
3.0 Fuzzy Logic in Control
wi  min{F (i )( x), F (i )( y)}
Since the founding work by Zadeh[31], the benefits of using a
qualitative inferencing process to control physical systems has
been clearly demonstrated [22, 10]. The advantages of encoding
control structures within fuzzy sets include: the ability to control
systems for which no mathematical model exists, ease of design,
an intuitive linguistic description of the system parameters and
modularity. The latter point means that we can trace the inference
path of each fuzzy rule, before the set of rules are combined in the
defuzzification process. A fuzzy controller is also normally very
robust and can tolerate major degradation of its rule structure,
[17] and insensitivity to noise or uncertainties in the control
inputs, which make it ideally suited to mobile robot control.
A Fuzzy control system works by encoding an experts knowledge
into a set of rules which are smoothly interpolated and the
resultant is defuzzified to give a crisp actuation output. Each rule
is specified as either a triangular, trapezoid or some other function
e.g. gaussian and assigned to some range of input variable.
Trapezoid functions were selected as these are computationally
efficient, which is a priority when used for real-time control
systems. The fuzzy rules normally take the form :
IF (x is A) and (y is B)
Fuzzy rules can be represented by a fuzzy associative memory
matrix, (FAM) [17]. In this system the base dimensions represent
the input variables and each FAM matrix entry represents an
output fuzzy set. There are typically 3 to 5 output membership
sets, e.g. negative large (NL), negative small (NS), zero (ZE),
positive small (PS), and positive large (PL). Using a FAM
representation, the weight for the ith FAM entry was calculated
using the minimum rule:
THEN
(z is C)
(5)
for x,y,z as linguistic variables representing inputs and outputs of
the fuzzy controller, and A,B and C are the terms of the variables,
in the universes of discourse X,Y, and Z.
(6)
where x and y represent the input dimensions of the FAM matrix;
If we define each output fuzzy membership set as:
Bi 
output fuzzy set
(7)
where the output ‘universe of discourse’ is composed of a finite
set of discrete values. Then the total defuzzified response for n
output membership sets is:
FT 
(  wi  Bi )
i
n
(  wi )
(8)
i 1
where n equals the number of active rules at a given time. The
values of wi for the total matrix form the weight array which will
be discussed in section 3.2. This is sometimes referred to as
‘height defuzzification’.
3.1 Hierarchies and Fuzzy Control
As outlined in section 2.0 decomposing the complex task
sequences required of autonomous robots into a hierarchy of
increasingly sophisticated control systems provides a powerful
method for increasing a robots degree of autonomy. This approach
is also well grounded in terms of its ethological relevance, as most
higher organisms exhibit some form of hierarchical control
structure to manage conflicting behaviour patterns. This is
highlighted by Lorenz who states the following in his discussion
of hierarchies in ethology, with specific reference to an insect’s
behaviour:
“..hierarchical organisation endows a sequence of behaviour
patterns with great plasticity and adaptability to changing
environmental conditions - despite the fact that all its subsystems,
as well as the interactions between them, are phylogenetically
programmed and modifications by learning, play hardly any
part..” [20, pp.201]
It is important to note that such hierarchies exist at many levels,
even within a single organism, and may form a nested sequence of
such structures. Internal conflict may occur between individual or
groups of behaviours, or between alternative action sequences.
3.1.1 Fuzzy Behaviour
Recent work by several groups has shown that fuzzy logic is a
useful tool for constructing complex multi-level control structures
in this field, [29, 26, and 7]. A fuzzy system can often bridge the
gap between low level behaviours and the necessary reflective
task oriented processes acting on the robot. By intelligently
managing the interactions of multiple primitive behaviours [29] a
fuzzy controller raises the basic competence level of an
autonomous robot which can greatly reduce the cognitive
workload on a planning system. An example being the ability to
recover from situations which commonly trap purely reactive
systems, such as box canyons [30].
Using the Behaviour Synthesis Architecture as a basis we
designed a fuzzy controller, which takes direct sensory input and
applies negative feedback on the utility of selected behaviours.
The contribution of each behaviour is therefore adjusted
dynamically to suit the current set of environmental stimuli. This
allows the robot to focus on its current response while
maintaining some awareness of its final goal. As the robot
approaches an obstacle, the importance of avoiding it increases
due to the utility - response generated by the B.S.A system, while
the fuzzy rule base responds by turning down the utility of
moving towards the beacon. The effect is quite similar to the work
by [26] in which a fuzzy logic controller modifies multiple
behaviours on the robot ‘Flakey’, where increasing proximity to
an obstacle has a “shading” effect on a reach goal behaviour.
behaviours were important at specific times during the agents
interaction with its environment.
3.2.1 Fuzzy State space
Since a fuzzy control system gives us complete knowledge of the
activity of every rule over time we can construct a dynamic
representation of the history of the robots interaction with its
environment. Work by Beer [6] has illustrated how considering
the agent-environment interaction in terms of a dynamical system
can help abstract the problem away from the details of sensors or
specific behaviour design, and give insight into the global
properties of such systems.
3.3 Dynamic Fuzzy Action Surface
3.3.1 Dynamic Instability
Based on a description of the fuzzy control system as a dynamic
process we propose that for a goal-seeking agent in an
unstructured environment, maintaining a state of dynamic
instability within the control structure increases the probability of
the agent reaching its goal. The concept draws on ethological
work which shows how repeated exposure to a conditional
stimulus without subsequent reinforcement leads to the loss of an
associated conditional response, i.e ‘extinction’ [23]. In addition,
work on the dynamics of neurological processes indicates that
such systems also exploit dynamic instability as a source of
flexible response, [16].
Hence an agent must be able to suppress behaviours which are no
longer producing a useful response, over a time frame which is
context dependent. As an example, if our two robots are
navigating a simple maze environment to a beacon and become
trapped, then the firing of the fuzzy rules associated with the input
variables of the FAM matrix indicates periodic activity as each
rule is repeatedly triggered, see fig 2.
3.2 Fuzzy Rule Analysis
As stated, a significant advantage of a Fuzzy system is that, unlike
artificial neural networks which sum their throughputs, a fuzzy
system sums outputs, hence the fuzzy rule base is modular and
can be dissected to trace the inference paths through its structure.
By storing the weight values associated with the firing of each
rule over time it is possible to build a memory of the activity of
each rule and hence its relative importance in controlling a given
system. If the fuzzy system is managing the agents primary
behaviours then the set of active weight values also contains
context dependent information, i.e. knowledge of which
Figure 2. Graph for one typical fuzzy rule output in which the
robots become trapped after 400 iterations of the simulator.
As we are aiming to create an autonomous robot we should
consider that from the robots perspective-making progress
towards the goal/beacon is its motivation for acting. Hence if no
progress is being made while firing a set of fuzzy rules then the
normal reward associated with those rules is removed. Some
means of suppressing these rules is therefore required which is
based on their relative activity as a function of time.
3.3.2 Weight Vectors
In order to increase the adaptive capacity of the fuzzy system
several parameters exist which can be used to dynamically modify
the fuzzy controller:



the scaling factors of the fuzzy variables,
the fuzzy sets representing the meaning of linguistic values,
the if-then rules, [11].
The first two cases represent ‘self-tuning’ controllers as they finetune an existing set of rules. In the last type, which modifies the
rule base itself, it is effectively a ‘self-organising’ controller and
often uses an artificial neural network to learn a set of rules from
sampled input-output data, [19]. The method selected for this
work falls in the ‘self-organising’ category as the output of each
fuzzy rule can be altered dynamically.
If we consider the fuzzy action surface pre-defined by each entry
of the FAM matrix as a rigid surface then by associating a vector
with each FAM weight we can modulate the control surface
according to the need to suppress individual rules. We can define
the operation by the following modification to equation 8:
(
FTA 
 w V  B )
i
i
i
i
n
(

(9)
wi )
i 1
Where V i is an adaptive vector triggered from an on-line
summation of each fuzzy rule’s activity, (at present this is a
constant value but can also by a function of the rules degree of
activity), and FTA is the adapted defuzzified output F . The
weight for each rule wi is stored in each iteration of the control
loop and after a predetermined time the total for each rule is
compared against a threshold value. If the rule exceeds this
threshold then its weight is suppressed for the duration of the next
control cycle. This suppression creates a new action surface,
which redirects the dynamic flow of the robot’s interaction with
its current environment. This method however requires an
empirical study to determine the optimum threshold value for a
given environment. An alternative approach is to use a functional
relationship between a rules activity and its degree of suppression,
which is based on ethological examples. Mc Farland & Bosser
[23] associate this relationship with a cost or utility analysis of a
behaviour and evidence suggests in many animals this obeys a
square power law. Hence the longer a behaviour (or fuzzy rule in
this case) is active the greater the cost it incurs for the agent.
4.0 IMPLEMENTATION
It was decided to use two B12 mobile robots as the basis for our
research platforms. These robots (obtained from Real World
Interface, Inc.) are omni-directional, controllable via an RS232
interface, have a comprehensive set of motion commands, and are
cost effective. However, to work towards the object relocation
task these required enhancement to the basic B12 platforms.
Superstructures have been designed and built to house additional
sensors, data acquisition, communications and secondary control
hardware and the primary computer system is a 75Mhz 486 pc
notebook. Fred and Ginger both possess the following sensors. A
multi-frequency ultrasonic system for obstacle proximity
detection, (multi-frequency was used to overcome multiple robot
acoustic interference). A self-centring, X-Y capture head which
uses optical linear encoders to obtain displacement data which
allows each robot to ‘feel’ the force exerted by the other robot.
They also have translate and rotate drive motor position encoders,
and a battery voltage level sensor. They use ultrasound navigation
sensors for locating active beacons, with a separate frequency for
the obstacle sensors. For communication between the robots and
an external agent there is a half-duplex radio link which can pass
sets of behaviours [4] to the robots and allows status information
to be returned to the agent.
4.2 Simulation
A dynamic simulation of the coupled mobile robots has been
created which runs the B.S.A and fuzzy control system. This
allowed rapid prototyping of the behaviour patterns and fuzzy
control system and has also been used to evolve fuzzy controllers
off-line [14]. Each simulated robot has an equivalent set of
sensors to the physical robot, with floating point representation.
The simulation model does not include sensor noise, or inertia as
the aim was to verify if the FAM matrices were dynamically
robust and could therefore be transferred to the real robot. The
simulation was implemented on a DX4 75Mhz 486 PC.
5.0 EXPERIMENTAL RESULTS
A series of comparative experiments were performed to test the
two hypotheses of this paper; firstly whether using an adaptive
FAM surface can increase the competence of a mobile robot while
navigating through a semi-structured environment, and second
whether this system can be directly transferred onto physical
mobile robots.
To test the first issue the simulation was run with a range of maze
type environments in which the space between the walls was
progressively decreased for the adaptive and non-adaptive
systems. The adaptive system could also successfully negotiate an
environment, which was 10% narrower than for the non-adaptive
case. In order to test whether any perturbation of the fuzzy action
surface could help the mobiles escape dynamic attractors we
added random noise to the FAM output from 5-10% of its total
value. No improvement in the robots ability to handle more
difficult environments was observed.
FT (equation 8)
b
a
y axis
x axis
Figure 3a. Rigid FAM weights surface for simulated robots; 3b. weights surface for physical robots, (x axis is scaled distance
to obstacles, y axis is absolute displacement of the x-y coupling table, z axis is degree of negative feedback on the utility of
navigating to the beacon).
FTA (equation 9)
a
b
y axis
x axis
Figure 4a. Modulated FAM weights surface for simulated robots; 4b. same weights surface acting on physical robots.
The FAM rule base was then transferred to the physical robots.
The major difference between simulated and real environment was
in the effective sensor range of the navigation beacon. In the real
world this only covered a quarter of the environment, using an
ultrasound signal, and the robots only possess two forward facing
beacon sensors. Hence the real robots often failed to recover the
beacon signal if they turned at an acute angle to the beacon.
However the robust noise tolerance of the fuzzy controller was
essential in allowing a direct transfer from simulation to physical
robots, with minimal mapping of simulated to real sensors.
Figures 3 and 4 show state space data with an interpolated surface
added to illustrate the similarity between the simulated and
physical robots fuzzy action surface. Figure 4 shows the effect of
adding the adaptive mechanism to the weights array. From the
surface plots in figure 4 we can infer that the adaptive mechanism
has an equivalent effect on both the simulated and physical robots
fuzzy action surface, by deforming the surface at points where a
particular rule exceeded the threshold for suppression.
In addition related work has demonstrated that useful fuzzy
controllers could be evolved offline using standard genetic
algorithms and transfered to the physical robots; details are
presented in [14].
In order to study whether the fuzzy control mechanism was
improving the ability of the real robots to escape from local
minima one robot attempted to negotiate the same environment,
both with and without a fuzzy control mechanism. A single robot
was used so that the interaction of only the obstacle avoidance
behaviour and navigate to beacon behaviour could be isolated.
Figures 5. and 6. show the results from two typical runs.
1 1
the robot is still detecting closely positioned walls until about 600
time steps).
In the second example of figure 6. the fuzzy system is coupled
into the behaviours. The robot was started from the same location
and after 200 time steps detects the beacon again. This time
however the fuzzy output is suppressing the rotate to beacon
behaviour, hence after only 30 time steps (between 210-240 time
steps) the robot turns away from the wall and beacon and
navigates out of the local minima.
7.0 CONCLUSION
0.5
0 0
0
100
200
300
400
500
Iterations of control loop
0
600
619
Figure 5. Plot of one beacon sensor response and the crisp output
of the FAM matrix, for a physical robot, (lower solid line is
beacon output, upper dashed line is FAM output).
1 1
By using a dynamically adaptive control system the navigational
ability of two co-operating autonomous robots was significantly
improved. The use of a fuzzy controller in managing a set of
primary reactive behaviours enhanced the robots ability to
navigate through a semi-structured environment. This method also
confirms the second hypothesis of the paper, by permitting the
direct transfer of a control system from a simulation to a physical
robot, even when the simulation is quite basic and is not closely
modelled on the sensors or dynamic properties of the physical
robot. The essential aspect of such a transfer is to take only the
higher levels of the control hierarchy (i.e. the fuzzy system in this
case), hence abstracting the sensor and actuator details out of the
process.
This is believed to be a novel method for dynamically modulating
a fuzzy logic based control surface, which may have generic
applications. However, the increase in autonomy naturally incurs
the cost of reducing our knowledge of the systems short-term
response. Future work aims to discover the underlying processes
by which an autonomous robot can best select fuzzy rules for
suppression.
0.5
0 0
Acknowledgements
This work was funded by the EPSRC
GR/J49785).
0
0
50
100
150
200
250
Iterations of control loop
300
(grant : REF No.
350
359
Figure 6. Plot of one beacon sensor response and the crisp output
of the FAM matrix, for a physical robot, (lower solid line is
beacon output, upper dashed line is FAM output).
Firstly, in figure 5. the output from the fuzzy control level was
disconnected from the behaviours. When the robot entered an area
where a wall blocked the route to the beacon, after 300 time steps
one of the front beacon sensors detects the beacon and the robot
rotates on to the beacon. It remained in this position even though
the proximity of a wall was causing the fuzzy system to produce a
maximum response (dashed line). After 500 time steps the beacon
was switched off and the robot then turned away from the wall
and navigated out of the area, (the fuzzy response remains high as
8.0 REFERENCES
[1] Albus J.S., Brains, Behaviour, and Robotics, Byte Books,
McGraw-Hill, Peterborough, 1981.
[2] Ashwin R., Arkin R., Boone G., & Pearce M., Using Genetic
Algorithms to Learn Reactive Control Parameters for
Autonomous Robotic Navigation, Adaptive Behaviour, vol.2
issue 3, pp 277-304 1994.
[3] Arkin, R.C The Impact of Cybernetics on the Design of a
Mobile Robot System: A Case Study, IEEE Transactions on
Systems, Man, and Cybernetics, vol. 20, No. 6, Nov/Dec 1990,
pp. 1245-1257.
[4] Aylett .R, Coddington .A, Barnes .D, Ghanea-Hercock .R, A
Hybrid Multi-Agent System for Complex Task Achievement by
Multiple Co-operating Robots, Proc. Multi-Agent Workshop,
Oxford 1995.
[5] Barnes, D., Advanced Robotics & Intelligent Machines, Ed.
J.O.Gray & D.G Caldwell. IEE Control Engineering Series 51,
pp.295-313, 1996.
[6] Beer. R.D, A dynamical systems perspective on agentenvironment interaction, Journal of Artificial Intelligence, 72
(1995) pp173-215.
[7] Bonarini A., Learning dynamic fuzzy behaviours from easy
missions., Proc. IPMU 96, Proyecto Sur Ediciones, Granada,S.,
pp. 1223-1228
[8] Brooks, R.A., 1986, A Robust Layered Control system for a
Mobile Robot, IEEE Journal of Robotics and Automation. RA-2,
No.1, pp14-23.
[9] Brooks. R.A, Intelligence without representation, Artificial
Intelligence. 47, 1991 p.139-159.
[10] Chiu S., & Togaii M., A Fuzzy Logic Programming
Environment for Real-Time Control, Int. Jounal of Approximate
Reasoning, 2 pp. 163-175, 1988.
[11] Driankov D., Hellendoorn H., & Reinfrank M., An
Introduction to Fuzzy Control, pub. Springer Verlag, Berlin 1996.
[12] Gat E., Integrating Reaction and Planning in a Heterogenous
Asynchronous Architecture for Controlling Real World Mobile
Robots., Proc. of the Tenth National Conf. on Artificial
intelligence (AAAI), 1992.
[13] Gat E. On the Role of Stored Internal State in the Control of
Autonomous Mobile Robots, AI Magazine, Spring 1993, AAAI
pp 64-73.
[14] Ghanea-Hercock R.A, & Barnes D. An Evolved Fuzzy
Reactive Control System for Co-operating Autonomous Robots,
Fourth Int.Conf. on Simulation of AdaptiveBehaviour, Cape Cod,
1996.
[15] Ghanea-Hercock .R, Barnes .D, Coupled Behaviours in the
Reactive Control of Cooperating Mobile Robots, International
Journal of Advanced Robotics, Japan, special issue on Learning
and Behaviours in Robotics, April 1996, vol. 10/2, pp161-177.
[16] Kelso S., Dynamic Patterns, The Self-Organisation of Brain
and Behaviour, MIT Press, Boston Cambridge, 1995.
[17] Kosko .B, Neural Networks and Fuzzy Systems, A
Dynamical Systems Approach to Machine Intelligence, Prentice
Hall Int. 1992.
[18] Kube C.R, & Zhang H., Collective Robotics: From Social
Insects to Robots, Adaptive Behaviour, Vol. 2, No. 2 pp180-218,
1994.
[19] Lin C. T., & Lee C.S.G, Neural-Network-based fuzzy logic
control and decision system, IEEE Trans. on Computers, vol. 40,
pp.1320-1336, 1991.
[20] Lorenz K.Z., The foundations of Ethology, Springer-Verlag
Pub. NewYork, 1981.
[21] Maes P., How to do the right thing, Connection Science
Journal, Vol.1, No.3., 1989.
[22] Mamdani E.H. Application of fuzzy Logic to Approximate
Reasoning Using Linguistic Synthesis., IEEE Trans. on
Computers C-26, pp1182-91.
[23] McFarland. D, and Bosser. T, Intelligent Behaviour in
Animals and Robots, MIT Press, Cambridge, 1993.
[24] Noreils F.R., Toward a Robot Architecture Integrating
Cooperation between Mobile Robots: Application to indoor
Environment, The Int. Journal of Robotics Research, Vol.12,
No.1., Feb. 1993 p79-98.
[25] Saffioti A., Some Notes on the integration of Planning and
Reactivity in Autonomous Mobile Robots, AAAI Spring
Symposium, CA, Mar. 1993, pp. 122-126.
[26] Saffioti A., Wesley L.P., Hierarchical Fuzzy-Based
Localization in Autonomous Mobile Robots, Proc. IFSA 1995.
[27] Steeles L., The Artificial Life Roots of Artificial Intelligence,
Journal of Artificial Life, vo. 1, no.1/2 MIT press, 1994.
[28] Surmann .H, Peters .L, Huser .J, A Fuzzy System for
Realtime Navigation of Mobile Robots, 19th Annual German
Conf. on AI , KI-95, pp. 170-172.Bielefeld.
[29] Tunstel E., Mobile Robot Autonomy via Hierarchical Fuzzy
Behaviour Control, Proc. 6th Intl Symp on Robotics &
Manufacturing, 2nd World Automation Congress, Montpellier,
France May 1996.
[30] Watanabe, Y., Pin. F.G, Sensor based navigation of a mobile
robot using automatically constructed fuzzy rules, Proceedings of
ICAR ‘93 the International Conference on Advanced Robotics,
Tokyo Japan, pp 81-87, 1993.
[31] Zadeh L., Fuzzy Sets, Journal of Information and Control,
Vol.8, pp. 338-353, 1965.