International Journal of Advanced Robotic Systems, Vol. 3, No. 2 (2006)
ISSN 1729-8806, pp. 093-098 093


Robot-Animal Interaction:
Perception and Behavior of Insbot


Masoud Asadpour; Fabien Tâche; Gilles Caprari; Walter Karlen & Roland Siegwart
Autonomous Systems Lab (http://asl.epfl.ch), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne,
Switzerland
Corresponding author E-mail: masoud.asadpour@epfl.ch


Abstract: This paper describes hardware and behavior implementation of a miniature robot in size of a match box
that simulates the behavior of cockroaches in order to establish a social interaction with them. The robot is
equipped with two micro-processors dedicated to hardware processing and behavior generation. The robot can
discriminate cockroaches, other robots, environment boundaries and shelters. It has also three means of
communication to monitor, log, supervise the biological experiment, and detect the other robots in short range.
The behavioral model of the robot is a mixture of fusion in low-level and arbitration in high-level. In arbitration
level a stochastic state machine selects the proper subtask. Then in fusion level, that subtask is decomposed to a
hierarchy of sub-tasks. Each sub-task generates a potential field. The resultant force is then mapped to an action.
Keywords: micro robots, mixed-society, robot-animal interaction, behavior modeling


1. Introduction

Over the last decades, researchers in bio-inspired robotics
have mimicked animals to design hardware and software
structure of the robots. RobotV (Kingsley et al. 2003),
RHex (Sarnaly et al. 2001), Biobot (Delcomyn and Nelson
2000), HEL-roach (Kagawa and Kazerooni 2001) and the
hexapod micro-robot (Guozheng 2002) are examples of
legged-robots which have been mechanically inspired by
cockroaches. Some projects are inspired from the
behavior of cockroach and implemented their behavior
on micro robots (Jost et al. 2004; Garnier et al. 2005).
Some researches have developed hybrid robots by mixing
artificial and biological systems. PheGMot-III (Nagasawa
et al. 1999) uses real cockroach antennas as a chemical
sensor to follow pheromone tracks. Holzer and
Shimoyama (Holzer and Shimoyama 1997) designed a
system which controls the cockroach’s actuators by
electric stimulation.
Instead of building exactly the same mechanism as
animals our goal in short-term is to have robots which
integrate into animal societies, live inside the society and
interact with them. Focus of our work is in collective-
level. So there is no need to have the same appearance as
animals but the functionality of the robot must permit it
to integrate into their society and produce statistically the
same collective behaviors.
By “integration” we mean not only the animal’s behavior
is affected by the robots and the other animals but also
the robot’s behavior is affected through interaction with
the animals and the other robots in the mixed-society. In
fact every decision is made collectively by the whole
society so that a top-level observer would not see any
difference between the animal society and the mixed one.
In our model the animal is thus considered as a black box
and the important characteristics for our robot is to fit in
the mathematical model of collective interactions among
individuals involved in the group.
The long-term goal of the project, after the robots are
accepted by the society of animals, is to manipulate the
collective response of the society by modulating the
behavioral parameters of the robots. We hope then to
propose guidelines towards a general methodology for
performing such a control on mixed-societies.
Among the projects that are related to our work, we can
mention the Robot Sheepdog (Vaughan et al. 1997) that
controls a flock of ducks by moving them safely to a pre-
determined position. Also, the W-M6 rat-like robot (Ishii
et al. 2004) tries to create a symbiosis between creature
and robot by teaching a rat to push a lever to access a
food source. These projects are different from what we
are investigating in that their robots are not trying to
integrate into the society. Instead they are trying to affect
or supervise the society in a centralized manner.
Böhlen developed a robot (Böhlen 1999) that interacts
with three chickens in a cage. He manipulates some
techniques to mechanically reduce chickens' anxiety
towards the robot. The goal of the robot is to integrate
with chickens but does not try to affect their behavior.
Our work is a part of the LEURRE European project
which aims to study mixed-societies of animals and
robots. This multi-disciplinary project gathers the
competence of the biologists, ethologists, chemists and
engineers from different European universities:

International Journal of Advanced Robotic Systems, Vol. 3, No. 2 (2006)

094
Université Libre de Bruxelles, Université Paul Sabatier,
Université de Rennes and Ecole Polytechnique Fédérale
de Lausanne. Our team is mainly involved in design,
building and programming the robots and the tools
needed to manage them. Behavior of robots is
programmed according to the models developed by
biological researches. Preliminary tests run on a mixed-
society of cockroaches and robots. More experiments will
be done with other animals to verify the methodology.
In this paper we focus on the behavior generation issue
and describe how we implemented the aggregation
behavior of cockroaches on our insect-like robot, InsBots.
More details on hardware aspects are found in (Colot et
al. 2004; Tâche et al. 2005).
The paper is organized as follows: The required
functionality of the InsBot is summarized in the next
section. Then a short review on the perception of the
robot is presented. In section 4, the behavioral
architecture of the robot is explained in detail. Finally the
test results followed by conclusion and future works are
explained.

2. Functionality of InsBot

InsBot requirements do not specify that the robot should
look like a real cockroach. Instead it should:
• Behave like a real cockroach among its group.
• Get accepted by them as a congener.
• Be able to influence their collective behavior.
• Be equipped with monitoring and debug facilities
InsBot (Fig.1) is a 41x30x19 mm3 robot. Its rigid body is
composed of PCBs that allow mechanical and electronic
connections at once. It has a 190 mAh Li-Po battery that
allows autonomy of at least 3 hours (required for
biological experiments) and 2 miniature differential-drive
step-motors for locomotion. It weighs 17gr and can move
up to 5cm/s. It has several sensors and communication
tools:
• 12 x Infrared (IR) proximity sensors, 3 in each side.
They are placed at different heights to allow
discrimination of different objects. They are also
used for local communication between the robots.
• 2 x Photodiodes on top, for detection of the shelters.
• 1 x linear camera (102 pixels) in front to enhance
cockroach detection.
• 1 x IR receiver to remotely control the robot.
• 1 x radio transceiver (@868MHz) to communicate
with an external computer. It is used mainly for
debug or monitoring.
• 2 PIC18F6720 micro-processors (@16MHz) with 128K
program memory, 3840 byte SRAM data memory
and 1024 byte ROM.
One of the processors, called the “Hardware Processor”, is
connected to (almost all of) the hardware resources. It
prepares the sensory data by noise-filtering, scaling and
calibrating their values. This information is then
transmitted through a 400 KHz I2C bus to the “Behavior
Processor”, which hosts the behavioral algorithms.
To enhance the acceptance of the robot into the
cockroach's colony, it is covered by a paper impregnated
with cockroaches' pheromone (Fig. 1, right).

3. Perception

In this section we focus on the detection algorithms that
have been tuned for optimal perception of the
environment.

3.1. Experimental Setup
The perception methods described in this section have
been tuned for the particular setup shown in Fig.2. It is a
circular white plastic arena (1m diameter, 20cm high)
with an electrical fence to prevent the escape of the
cockroaches. The floor is composed of anti-vibrations
materials covered with a white paper. The paper is
changed after each experiment. The illumination is given
by 4 neon light bulbs with low IR emission to reduce the
interference with IR sensors.
There are two circular suspended shelters under which
the cockroaches aggregate. The shelters (called “dark”
and “bright” shelter hereafter) are composed of dark
plastic layers hanged at 5cm from the ground. To create
different levels of shadow different number of layers are
grouped.

Fig. 1. Left: InsBot without cover. Right: InsBots with
covers aggregated with cockroaches under a shelter
(©ULB)

Fig. 2. Experimental setup composed of neon light (3),
camera (4), electrical fence (6, 12), white plastic arena (7),
paper layer (8), phonic layer (9) and wooden layer (10).
Shelters are absent (©ULB).

Masoud Asadpour; Fabien Tâche; Gilles Caprari; Walter Karlen & Roland Siegwart / Robot-Animal Interaction: Perception and Behavior of InsBot
095
3.2. Calibration
Due to several facts a calibration phase should be
repeated once after each setup changes: First, the
program is running on multiple robots and robots are
slightly different in hardware devices. Then, the
inclination angle of proximity sensors is hard to adjust
precisely. They are not also perfectly placed at the same
height so they have different initial values. The floor
paper and its roughness highly affect the bottom sensors.
The illumination conditions vary in each experimental
setup and the amount of light under each shelter changes
as well. Orientation of the shelters varies in each setup. It
changes the gradient of light under the shelters.
The calibration procedure developed for proximity
sensors and shelters are activated via TV remote control
upon the user request. The computed calibration vectors
are saved in the EEPROM and loaded after each restart.
During regular process, these vectors are used to adjust
the value of the sensors and cut the noise off.

3.3. Object Detection
To behave like a real cockroach, the robot must first be
able to detect the relevant features of the experimental
setup. These features are the two heterogeneous shelters,
the living cockroaches, the surrounding wall and the
other robots.
For detection and differentiation of shelters, the light
intensity is measured by the two photodiodes mounted
on top of the robot. Then their value is compared with the
thresholds learned during the calibration procedure.
The cockroaches used in the mixed-society experiments
are Periplaneta Americana. They are 24-44 mm long and
shine red-brown. They have 6 legs and 2 long (around
3cm) antennas. Due to the dark color of their skin, they
are hardly detected by IR sensors from far distance. But
thanks to carefully sensor placement on the robot, the
calibration procedure and some heuristic rules, they can
be distinctively detected from 1.5cm distance.
There are 3 IR proximity sensors on each side of the robot
(Fig.1). The two lateral sensors are close to the ground
(called “bottom sensors” hereafter). The other one is
placed at top-center of each side (called “top sensor”
hereafter). Due to the shorter height of the cockroaches,
the top sensors receive less reflection than the bottom
sensors.
There are some situations where IR sensors can not
provide reliable information to well discriminate different
objects, especially when a cockroach is located along the
wall. In this situation using the linear camera helps us
reducing the misdetections of the cockroaches.
The difference between the values of the IR sensors
mounted in different heights is used also to detect the
wall. Due to the taller height of the wall comparing to the
cockroaches, the top sensor shows a value close to the
mean of the two bottom sensors.
Depending on the position and the orientation of other
robots, they can be seen as a wall or a cockroach. To
distinguish them a local communication protocol using IR
sensors as transceiver has been implemented. A scheduler
coordinates the use of the IR sensors as both proximity
sensor and communication media.

3.4. Local Communication
Local communication is the exchange of information
among the robots within a limited distance via their
infrared proximity sensors. The purpose is to declare the
presence of the robots to their neighbors. The transferring
message is a 6-bit data containing the unique ID of the
sender. Knowing the position of the sensor that receives
the signal and the proximity value of the IR sensors, the
robot can then indicate whether the around object is a
robot or not.
The low-level protocol is described in detail in (Tâche et
al. 2005). Since the communication baud rate is very low
the robots may not have the chance to communicate quite
often. Therefore it must be combined with software
solutions to provide a short term memory of the robots in
neighborhood.
The information that the robot extracts out of local
communication is saved in a log table. It is tagged with a
timestamp and the ID of the sensor that receives the
message (It roughly indicates the relative position of the
sender). The robot then has at its disposal, information
about when, where, and who has been around him.
The log table has a limited size. In case of experimenting
with a large group of robots it can hold only a part of the
signals. It should then hold only the fresh signals. If a
robot that is already registered in the table is detected at
another time, its corresponding record will be updated
with the most recent data. Otherwise the oldest record is
overwritten. A fixed time window of T seconds is used as
a criterion to specify the neighborhood region. Only the
robots that have been around within the last T seconds
are counted. The neighborhood range expands by setting
T to a bigger value.

4. Behavior

The control architecture of the robot is a behavior-based
controller (Arkin 1998) distributed on the two processors.
It consists of a collection of behaviors. Each behavior can
take inputs from the robot's sensors and/or from other
behaviors, and send outputs to the robot's actuators
and/or to other behaviors.
The behaviors are arranged in a hierarchy in which the
behaviors on the higher levels integrate or arbitrate the
ones on the lower levels. At the highest level a centralized
arbiter decides which behavior to execute. At the next
level the selected behavior activates one or more
behaviors from the lower levels. The decomposition
continues downward until the primitive behaviors in the
lowest layer. Therefore behavior coordination is
competitive at the highest level and cooperative at lower

International Journal of Advanced Robotic Systems, Vol. 3, No. 2 (2006)

096
levels. The final output is the result of the cooperation
among the activated behaviors.
The arbiter runs on the behavior processor. It is a finite
state machine that implements aggregation in mixed-
society. The remaining behaviors run on the hardware
processor. We call them reactive behaviors in the sense that
they map a stimulus directly to a response. They have
faster access to sensors and actuators. The running cycle
of the reactive behaviors is 10 times faster than the
centralized arbiter (50 vs. 500 ms).

4.1. Reactive Behaviors
Reactive behaviors are generated by means of the
potential field method (Arkin 1998; Reynold 1994). Each
behavior generates a potential field. Each potential field
maps the sensory space into the motor space through
attraction or repulsion force (rx, ry). The final velocity of
the robot corresponds to the resultant force (Rx, Ry),
which is the weighted sum of those force vectors. The
weights are specified empirically. The resultant force is
then transformed to the speed of the wheels.

Behavior Layers
The lowest layer deals with a specific sensor. This layer
provides the primitive behaviors. Each primitive
behavior assign different levels of attraction or repulsion
to the value of a sensor S, i.e. (rx, ry) = F(S). F can be
constant, binary, or proportional attraction/repulsion, or
combination of them.
The second layer deals with sides i.e. left, right, etc. Here
a group of primitive behaviors of the first layer are
combined to generate a force toward/from a direction. For
instance move-forward behavior is achieved by assigning a
constant attraction force to the front-side sensors.
The third layer deals with objects, i.e. wall, cockroach,
robot, or unknown object. For instance obstacle-avoidance
is left-avoidance behavior if an obstacle is detected at left
plus right-avoidance if an obstacle is detected at right, etc
(Fig.3, layers 2 and 3).
The fourth layer deals with a group of objects. This layer
composes collective behaviors like dispersion, cohesion,
watching, etc. For instance watching behavior is the result
of following all objects.
It is possible to add more layers and create more complex
behaviors. For instance light-search behavior is built from
wandering and light-attraction, where wandering is
composed of avoidance and move-forward.
As a summary, a typical architecture of behaviors for a
robot equipped with only IR proximity sensors is shown
in Fig. 3. To fit in one figure the types of distinguishable
objects with IR sensors were limited to robots, obstacles,
and ambient light. Also some of the behaviors in the 3
first layers are not shown.
Behavior layers are designed carefully to maintain
scalability and reusability of components both in higher
levels and even on another robot. We have implemented
the same architecture on Alice micro robots (Caprari
2003). It is scalable via adding more layers or modules.

Transformation of Force to Speed
Finally, the resultant force (Rx, Ry) is transformed to the
speed of the wheels. Inspired by the law of physics, we
can write the following relations for Fig.4:

'/ ( ) / 20
( ) / 20 . ' ( ).
L L k F R kRF R F F y xy LL R
F R kRR L F F L y xx RL Rτ
=
= +∑ = ⇒ = + ⇒
= −∑ = ⇒ = −
⎧⎧⎪ ⎪⎨ ⎨⎪ ⎪⎩ ⎩
(1)
The vector (FL, FR) is a force vector that must be
simulated by the wheels so that an observer perceives a
force vector (Rx, Ry) affecting the movement of the robot.
The force vector (FL, FR) is mapped to a feasible speed
vector (VL, VR). We used k=1 in our application. Bigger k
values create sharper turnings since Rx is magnified.

4.2. The Arbiter
The mathematical model of the mixed-aggregation asks
for a stochastic state machine that at each time step (here
500 ms) selects the next macro-action among move, turn,
and stop action set (Fig.5). These actions are mapped to
reactive behaviors in hardware processor. If the robot is
moving near periphery, the move action means wall-
Avoidance
Dispersion
DistributionGrouping
Light- Light+
F+
Aggregation
Inspection Wandering
Wall
Following
Shadow
Search
Light
Search
More
Layers
4
group
3
object
2
side
1
sensor S1+ S2+ S3+
Rob+ Rob-Rob± Obs+ Obs-Obs±
Attraction
Cohesion
Watching
F-B-R-L- R±L±
S1- S2- S3-
Fig. 3. An example of the behavioral architecture. Rob,
Obs, L, R, B, F , +, -, and ± stand for robot, obstacle, left,
right, back, and front, attraction, repulsion, and following
(combination of attraction and repulsion), respectively. It
is assumed that the typical robot has 3 sensors at each
side, where S1, S2, S3 are located at front. Weights and
some other details are not shown.
Ry
Rx
L L
L’
Fl Fr

Fig. 4. Converting resultant force to speed vector

Masoud Asadpour; Fabien Tâche; Gilles Caprari; Walter Karlen & Roland Siegwart / Robot-Animal Interaction: Perception and Behavior of InsBot
097










Fig. 5. The finite state machine for mixed-aggregation

following behavior and turn means escaping-from-wall. In
the center of the arena they mean regular obstacle-
avoidance and regular turning respectively.
Actions are selected based on a probability table. Entries
of the table assign a probability to each macro-action
based on the state of the robot, i.e. position, shelter type,
and number of cockroaches and/or robots around. The
probability table is extracted by extensive statistical data
gathering on real cockroaches using visual tracking
software and adapted to create similar behavior to the
cockroaches.

5. Results

Based on the discussed facts, the detection algorithm
combines the different responses of the top and bottom IR
sensors, the local communication and the linear camera to
distinguish the cockroaches from the arena walls and the
robots. Here we show some test results.

5.1. Cockroach and Wall Detection
Fig.6 displays the accuracy of the detection algorithm
implemented on the InsBot. These results were obtained
by manually analyzing 900 different situations of a movie
taken by the overhead camera and information of the
wireless communication interface. For cockroach
detection the distance is measured from the robot (body-
border) to the closest point of the cockroach body
(excluding legs and antennas).
Fig. 6 confirms that cockroaches are visible from 2.5cm,
but optimal detection is only reached when they get
closer to the robot. The better performance in front side is
due to introducing the linear camera. The dashed curve
represents the same result without the use of the linear
camera. It is clear that the detection accuracy is close to
detection in left/right side. This graphics also shows that
the walls are detectable at further distances than the
cockroaches with higher accuracy thanks to their better
reflective properties.
The rather poor performance of the cockroach detection
at even short distances comes from several facts: Firstly,
certain parts of the cockroach's body are less visible than
the others. Its head well reflects the IR signals, whereas
the rear side of its body composed of thin horizontal
wings reflects the IR signals upwards. Also some
positions around the robot are not well covered by IR
sensors. Better performance is achieved by adding the
linear camera as the graph with diamond marks shows.

5.2. Robot Detection
Robot detection mainly depends on the reliability of local
communication protocol which is rather difficult to
characterize. Communication rate depends on the
distance and the relative orientation of the robots. Fig. 7
(left side) shows communication rate between two robots.
Each point represents the number of the received
messages by a fixed robot from another one in different
distances and orientations during a 30s test. Brighter
points correspond to higher rates. Fig. 7 (right) shows the
success rate i.e. the percentage of correct messages out of
the received ones. The success rate is rather high (70-
100%) even where the baud rate is low.

6. Conclusion

In this paper details of the perception and the behavior
implementation of the miniature robot InsBot were
explained. Due to the limits in the size of the robot, and
the required long-time autonomy, the hardware parts and
processing algorithms have been highly optimized.
Different problems arisen from the imposed
simplifications and limited sensory information were
explained and the solutions were described.
The sensor fusion methods combined with heuristic rules
that came from our knowledge about the experimental
setup allowed the robots to have a good discrimination
among different objects in the environment. Cockroaches
Move
(forward move,
wall following) Turn
(turn in center,
Escape
from wall)
Stop
Random
DecisionPm Ps
PtΔT ΔT
ΔT

Fig. 6. Cockroach/wall detection accuracy vs. robot-
cockroach/wall distance

Fig. 7. Local communication between 2 robots. Left:
communication rate. Right: success rate

International Journal of Advanced Robotic Systems, Vol. 3, No. 2 (2006)

098
and walls are now detected using the IR proximity
sensors mounted in different heights around the robot.
To have less collisions and more friendly behavior with
cockroaches, a linear camera was introduced on the front
side of the robot that enhanced the detection quality.
A simple local-range communication protocol through IR
sensors was established for robot detection. However
more investigation is necessary to completely solve the
raised problems. Using local communication introduces
noise on the proximity value of sensors of other
surrounding robots. The noise disturbs the detection
procedure and we are working on appropriate filters to
reduce it.
We also explained the scalable and reusable architecture
of behaviors. The layers start from some primitive low-
level behaviors. The higher layers combine the behaviors
in lower layers and build new behaviors. We have
distributed the behavior layers between two processors,
hardware and behavior processors. The hardware
processor provides a library of reactive behaviors. The
behavior processor provides the possibility to combine
the behaviors in the library and compose more complex
even deliberative behaviors.
Biological experiments showed that the robots are
accepted by the colony of the cockroaches and that the
mixed-society of robots and cockroaches has statistically
close behavior to a pure cockroach society. The results of
the experiments will be submitted to biology conferences.


Acknowledgment
The LEURRE project (http://leurre.ulb.ac.be) is funded by the
Future and Emerging Technologies program (IST-FET) of the
European Community, under grant IST-2001-35506. The
information provided is the sole responsibility of the authors and
does not reflect the Community's opinion. The Community is not
responsible for any use that might be made of data appearing in
this publication. The Swiss participants to the project are
supported under grant 01.0573 by the Swiss Government.

7. References

Arkin, R.C. (1998). Behavior-based Robotics, MIT Press
Bell, W.J. & Adiyodi, K.G. (1982). The American
Cockroach, London: Chapman and Hall Ltd
Böhlen, R. (1999). A robot in a cage, Proceedings of IEEE
International Symposium on Computational
Intelligence in Robotics and Automation, Monterey,
CA
Caprari, G. (2003). Autonomous Micro-Robots:
Applications and Limitations, PhD Thesis n° 2753,
École Polytechnique Fédérale de Lausanne, Faculté
Sciences et Techniques de l'Ingénieur
Collot, A.; Caprari, G. & Siegwart, R. (2004). InsBot:
Design of an Autonomous Mini Mobile Robot Able to
Interact with Cockroaches, In Proceedings of the
International Conference on Robotics and
Automation, New Orleans, 2418-2423
Delcomyn, F. & Nelson, M.E. (2000). Architectures for a
biomimetic hexapod robot, Robotics and Autonomous
Systems 30, 5-15
Garnier, S.; Jost, C.; Jeanson, R.; Gautrais, J.; Asadpour,
M.; Caprari, G. & Theraulaz, G. (2005). Collective
Decision-Making by a Group of Cockroach-like
Robots, IEEE Swarm Intelligence Symposium,
Pasadena, CA
Guozheng, Y. & Yi D. (2002). A Novel Biomimetic
Hexapod Micro-robot, International Symposium on
Micromechatronics and Human Science
Holzer, R. & Shimoyama, I. (1997). Locomotion Control of
a Bio-Robotic System via Electric Stimulation, In
Proceedings of the International Conference on
Intelligent Robots and Systems, Grenoble, France
Ishii, H.; Nakasuji, M.; Ogura, M.; Miwa, H. & Takanishi,
A. (2004). Accelerating Rat's Learning Speed Using a
Robot - The robot autonomously shows rats its
functions, Proceedings of the 2004 International
Workshop on Robot and Human Interactive
Communication, Okayama, Japan
Jost, C.; Garnier, S.; Jeanson, R.; Asadpour, M.; Gautrais,
J. & Theraulaz,G.(2004).The embodiment of cockroach
behaviour in a micro-robot, Proceedings of 35th
International Symposium on Robotics, Paris, France.
Kagawa, N. & Kazerooni H., (2001). Biomimetic Small
Walking Machine, International Conference on
Advanced Intelligent Mechatronics, Como
Kingsley, D.A.; Quinn, R.D. & Ritzmann, R.E. (2003). A
Cockroach Inspired Robot With Artificial Muscles,
Proceedings of the 2nd International Symposium on
Adaptive Motion of Animals and Machines, Kyoto,
Japan
Mataric, M.J. (1999). Behavior-Based Robotics, MIT
Encyclopedia of Cognitive Sciences, Robert A. Wilson
and Frank C. Keil (Eds.), MIT Press, 74-77
Nagasawa, S.; Kanzaki, R. & Shimoyama, I. (1999). Study
of a Small Mobile Robot that uses Living Insect
Antennae as Pheromone Sensors, In Proceedings of
the 1999 International Conference on Intelligent
Robots and Systems, Kyongju, Japan, 555-560
Reynolds, C. W. (1999). Steering Behaviors For
Autonomous Characters, In the proceedings of Game
Developers Conference, San Jose, CA, 763-782
Saranli, U.; Buehler, M. & Koditscheck, D.E. (2001). A
Simple and Highly Mobile Hexapod Robot,
International Journal of Robotics Research, Vol. 20,
No.7, 616-631
Tâche, F.; Asadpour, M.; Caprari, G.; Karlen, W. &
Siegwart, R.(2005). Perception and Behavior of InsBot:
Robot-Animal Interaction Issues, Proceedings of IEEE
conference on Robotics and Biomimetics, Hong Kong
Vaughan, R.; Sumpter, N.; Henderson, J.; Frost, A. &
Cameron, S. (1998). Robot Control of Animal Flocks,
Proceedings of the Joint Conference on the Science
and Technology of Intelligent Systems, Gaithersburg,
MD