Quantitative Analysis of Distributed Control Paradigms for RobotSwarms

Trung Dung Ngo

Abstract— Given a task of designing controller for mobilerobots in swarms, one might wonder which distributed controlparadigms should be selected. Until now, paradigms of robotcontrollers have been within either behaviour based controlor neural network based control, which have been recognizedas two mainstreams of controller design for mobile robots.However, in swarm robotics, it is not clear how to determinecontrol paradigms. In this paper we study the two controlparadigms with various experiments of swarm aggregation.First, we introduce the two control paradigms for mobilerobots. Second, we describe the physical and simulated robots,experiment scenario, and experiment setup. Third, we presentour robot controllers based on behaviour based and neuralnetwork based paradigms. Fourth, we graphically show theirexperiment results and quantitatively analyse the results incomparison of the two methods. Closing remarks conclude thepaper.

I. INTRODUCTION

Swarm robotics is a new study field compromising of biol-

ogy and robotics. This study leads to mutual understanding of

organism ontology and organizational complexity in biology

as well as emergence and embodiment in multi-agent robotic

systems. A bio-inspired robot control is usually synthesized

based on inspiration of social animals. Alternatively, the

developed robots are utilized to analyse to have better

understanding of the complexity of animal behaviours.

Like natural characteristics of social animals, the robots

in swarm must be fully autonomous but their interaction

and communication enhance emergent dependencies among

them. Thus, a method compromising individual and collec-

tive characteristics into a control model is needed. Until now,

two major paradigms of distributed control for multi-agent

robotic systems have been widely known: behaviour based

control (BB) and neural network based control (NN).

The term “neural network” was coined out in 1800s

with emphasis of representation of behaviour, thought, and

emotions through a complex network of cells, so-called

neurons. In 1950s, this subject impacted on the development

of robotics with the Walter’s effort of building first mo-

bile robots to demonstrate the emergence of interconnected

devices-like neurons [3]. This tendancy was explicitly known

when Braitenberg built a number of simple vehicles [5] to

introduce new controllers by connecting sensors and motors.

This design was based on the principles of anatomy and

physiology of neuvous systems such as symmetry, time-

delayed reaction, and non-linear dynamics. Basically, an

Trung Dung Ngo is with Department of Electronics Systems, Automa-tion and Control,Aalborg University, Fr. Bajersvej 7C, 9220 Aalborg East,Denmark dungnt@es.aau.dk

artificial neural network is a computational model of input,

medium, and output cells to imitate the behavioural feature of

biological systems. In particular, artificial neural netwwork

used to develop robot control is a topological network

interconnecting sensors and actuators through hidden layers

in order to transfer sensory signals to the motor controller.

This approach is preferable in the community of evolutionary

robotics as learning and evolution algorithms are applied to

gain intelligence of the robots [6].

Fig. 1. The physical robots in the experimental scenario

The paradigm of behaviour-based systems that was pro-

posed in 1980s has been significantly developed by Brooks

[1] and documented by Arkin [4]. This is a practical approach

to robot controllers, allowing the robots situated in the

real world. In contrast to classical artificial intelligence, the

new fashion has advantages of the functional decomposition

such as fast reaction, robustness, accommodation of multi-

ple goals, and functional scalability. Behaviour based robot

control includes similar reactive characteristics as social

animals have, even it obtains other advanced characteristics

of reasoning and planning [8]. This approach is applied in the

engineering community as model based design is preferable,

for example in [7].

In general, both NN and BB paradigms are biologically

inspired approaches. They are centered on observation and

understanding of the designers about the system and the

environment. The design methods are analogous because

the principles of Braitenberg’s “law of uphill analysis anddownhill invention” and Brooks’ “function decomposition”are dependent to the unity of top-down analysis and bottom-

up synthesis. There are a few of qualitative research of

robot control, for example in [9], but none of quantitative

research has been, to the best of our knowledge, conducted

in comparison of NN and BB in robot swarms.

978-1-4244-9318-0/10/$26.00 © 2010 IEEE 116

Proceedings of the 2010 IEEEInternational Conference on Robotics and Biomimetics

December 14-18, 2010, Tianjin, China

In this paper, using massive experiments of swarm ag-

gregation with both simulated and physical robots, we aim

at examining whether NN or BB is better choice for robot

swarm. First, we briefly describe our physical and simulated

robots, and the experiment scenarios. We present our model

of BB adopted from Brook’s model and Arkin model, and

NN for swarm aggregation. Finally, numerous experiments

are worked out, then data are collected and analyzed to

provide concluding remarks.

II. SYSTEM DESCRIPTION

A. The Physical and Simulated Robots

The physical robots is a 12x12cm2 platform of two dif-

ferentially driven wheels. The robot is equipped with an

multi-sensory board for sensing and communication. Eight

pairs of 22◦infrared emitters and 40◦infrared receivers set-

up symmetrically on 8 directions provide a ‘flower’shape of

perception and communication, meaning that if a robot can

perceive and communicate with other robots if they appear

in its ‘flower’ area. Additionally, a robot can detect the light

source using 8 ambient light sensors symmetrically located

on the the board. The physical robots can be seen in in figure

1.

The simulated robots is designed to represent the char-

acteristics of the physical robots, except the perception and

communication ‘flower’shape is approximated by a cosine

function cos(4∗α) as illustrated in 2. Likewise, the simulated

robots are capable of detecting and moving towards the light

source.

(a) (b) (c)

Fig. 2. Intersection of sensor beam in reference to perception and com-munication between two robots: (a) no robot can communicate each other,(b) only left robot receives the signal, (c) they can mutually communicate

B. THE EXPERIMENTAL SCENARIOS

C. Materials of Experiments

Experiments of swarm aggregation is carried out in both

simulated and real environment.

The simulation has been written in C++ language on the

Qt SDK platform. C++ was selected because it allows multi-

threading processes that are extremely necessary to simulate

stochastic behaviours of robot swarms. Qt SDK platform

provides excellent graphical library for the API software

development. The developed simulator allows us adding an

unlimited number of robots, selecting an experiment scenario

and an algorithm in the GUI. Additionally, other functions

including grouping activation, sensor activation, PID control,

noise level adjustment, and actuation balance have been

implemented for diversity of experiments. We can also record

the robots’ position in Castersian coordinate (x,y,θ )for latter

data analysis, where x and y is the position coordinate and

θ is the orientation with respect to the global reference.

The experiment scenario for physical robots is an area of

3x3m2. An overhead camera is mount on the top in order to

record the robot performance and SwissTrack software [10]

is used to extract the robot trajectories.

Data gathered from the experiments is processed and

graphically shown for quantitative analysis of robots swarms,

using to verify the distributed control architectures.

III. DISTRIBUTED CONTROL

In our experiments of swarm aggregation, a robot has to

concurrently represent three major behaviours while acting

in the swarm: 1) avoiding obstacles including environmen-

tal obstacles and other robots, 2) maintaining connectivity

with neighbours, 3) moving towards the light source. Each

behaviour is resulted by mapping sensory or communica-

tion information onto the motor response. Thus, a control

architecture must integrate those behaviours into an unified

controller.

One of the properties of swarm aggregation is that agents

detecting the goal is able to inform and assist the other

in moving towards the goal through inter-communication in

colony, e.g, stigmergy in ant colony or wave in fish school.

However, this emergent decision is complicatively designed

under the constraints of concurrent behaviours. Performance

of the swarm aggregation is influenced by various factors,

either planning or reasoning behaviours or embodied reac-

tions situated in the world. Synthesis of those behaviours

into a controller for swarm aggregation is therefore difficult

to ensure that the robot individually acts while responding

to consensus of the swarm.

In the section, we describe our developed BB and NN for

experiments of swarm aggregation.

A. Behaviour-based Control

We developed two different controllers adopted from the

subsumption architecture proposed by Brooks [1] and action

selection proposed by Arkin [4] for our trial. The controller

is an integration of three independent behaviours: obstacle

avoidance, swarming, and moving towards the light source.

Specifically, obstacle avoidance is the reaction of a robot to

the other robots and objects in the environment; swarming

is the reaction of a robot in keeping distance with its

neighbours; and moving towards the light source is the

reaction of a robot to attraction of the goal.

In the first model, subsumption architecture, we prioritize

obstacle avoidance, connectivitiy maintainance and moving

towards the light source as we expect free collision when

swarming and the robots should give high priority of main-

taining aggregation than moving towards the light source.

In the implementation, the lower layer can suppress signal

of higher layer for the motor response while inhibitation is

embedded as “zero” interrupt of the lower layer to the upper

one.

The second control is a model of independent behaviours

in which each behaviour generates a signal normalized to a

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standard value, and the behaviour with the highest value is

selected to map on the motor schema.

Fig. 3. Behaviour based Control: a) Sumsumption architecture b) Actionselection architecture

B. Neural Network based Control

The model is a network inter-connecting sensory inputs of

robot communication, world perception, and light detection

to provide output signals to the motors. The hidden layers

are added up to mix sensory inputs if necessary. We think

that the light detection may compensate the inaccuracy of the

perception while the perception may increase the accuracy of

communication handshakes between robots, hence the first

hidden layer has been added up next to the input layer.

By observing the robot behaviour, we found that the robots

did not react gently when encouterning dynamical objects,

we decided to add the second hidden layer synthesizing

three types of input signals before mapping them onto

motor schema. Furthermore, the output is implemented with

internal feedback to make the robot movement smoothly. The

weights of interconnections between layers is selected by our

sensor measures and our expectation of the robot reaction,

for example we wish the robots react to infront obstaces

very fast by adding specific weights to the perception or

changing different weights to nodes of the light detection or

the communcation correspondingly.

IV. EXPERIMENTS AND RESULTS

A. Experiment Setup

In simulation we have established three major setups:

swarm aggregation, swarm aggregation without light source

in the obstacle-free environment, and swarm aggregation

with light source in the presence of obstacles. We examined

three control algorithms to collect data in each setup.

In real experimentation, due to limited time, we have only

conducted experiments of swarm aggregation without light

sources.

We have established three evaluation criteria for swarm

analysis: swarm trajectory (trace of robot trajectories) as

Fig. 4. Neural network based control

it shows how the robots navigate in the swarm; swarmcoherence (standard deviation of relative distances) as it

clarifies how coherent the swarm is; and swarm participation(robots in swarm) as it justifies how many robots get lost

when acting in the swarm. The criteria provide a standard

tool of evaludation to assess of how well distributed control

does in maintaining swarm aggregation.

B. Experiments with Simulated Robots

In the first setup, we only consider how well the swarm

maintains their aggregation without assigned mission in an

obstacle-free environment. This experiment aims at inves-

tigating properties of the reactive behaviours only. Here,

there are two reactive behaviours: react to avoid collision

with neighbours; and react to cohere with the neighbours.

The biological inspiration of this experiment is somehow to

mimic the swarm aggregation of locusts in nature. To do

so, we had to disactivate the light detection on the robots

and initially located all robots at the sensory boundary of

the neighbours as illustrated in figure 5(a). The results are

graphically shown in figure 6.

In the second setup, we tend to examine how well the

swarm maintains their aggregation whist moving towards

the light source in an obstacle-free environment as shown

in figure 5(b). The experiment is one step higher than the

first one as the swarm has been assigned a mission. A rea-

soning, moving towards the light source, is added-up in the

control architecture. However, it does not take environment

constraints into consideration. The experiment is naturally

inspired by fish school where fishes move towards a light

or sound source while grouping in a school. The results are

graphically presented in figure 7.

In the third setup, we investigate how well the robots

maintain their group while moving towards a light source in

the presence of obstacles 5(c). One more reactive behaviour,

react to the environment, is inserted in the control architec-

ture. This setup is somehow to mimic the ant colony where

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(a) (b) (c)

Fig. 5. Simulation setups: a) swarm aggregation, b) swarm aggregation with the light source, but in an obstacle-free environment, c) swam aggregationwith the light source and in the presence of obstacles

(a) (b) (c)

Fig. 6. Simulation without obstacles and light source: a)Action selection, b) Subsumption, c) Feedback neural network

ants move around to search for foods and bring them back

to the nest. The results are graphically reported in figure 8.

C. Experiments with Physical Robots

The experiments of swarm aggregation have been exam-

ined with seven physical robots. To avoid the disturbance

of sun light, the experiments were done in the dark room.

The BB and NN control implemented in the experiments are

adopted from the simulated control model, allowing to assess

our simulation and real experiments correlatively. The results

were collected and presented in figure 9.

V. ANALYSIS

Based on data collected from the experiments, we quan-

titatively analyse the swarm aggregation to issue remarks of

the distributed controls.

In the first experiment setup, the robots maintained their

connectivities very well as no robot got lost the aggregation.

The subsumption and the action selection were a bit different

in the performance time as the swarm with the subsumption

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(a) (b) (c)

Fig. 7. Simulation with light source, but without obstacles: a)Action selection, b) Subsumption c) Feedback neural network

architecture reached the stable status much faster as stated

in figure 6(b). In comparison of swarm coherence, NN

seemingly maintain robot connectivities better than BB does.

In the second experiment setup, some robots with NN got

lost connectivity while all robots with BB were well kept

in the swarm. The performance time tells us that the swarm

with BB is faster to reach the goal (light source).

In the third experiment setup, swarm participation indi-

cates some robots using BB got lost connectivity while they

worked well with NN. Supprisingly, the action selection was

the best in performance time as the robots was very fast to

reach the goal although their trajectories were more chaotic

than the others.

In two real experiments, the robots with NN and BB

released similar results of swarm coherence between the two

control methods. Apparently BB provides faster approach to

the goal while NN supports an good approach to the swarm

coherence.

Overall, statistical data of experiment resutls conducted in

both simulation and real experimentation suggests that:

• Both BB and NN are possibly used to design distributed

control for robot swarm and none of that is superior to

the other. Selection of a control is probably based on

the research interest of the designer and his effort in

choosing proper control parameters. ”Trial and error”

might be useful at the first step of controller design,

but intelligent computation in terms of learning and

evolution techniques will improve the overall quality.

• Although a swarm of robot can be aware of the decen-

tralized and loosely connected brains of mobile robots,

the experiment results confirmed that neural network

can be selected to design controllers for robot swarms.

Even neuron connection can be personally determined

based on the designer’s experience, swarm aggregation

can be achieved. Contrastingly, this is different from

Brooks’s opinion of connectionism and neural network

stated in [2].

• The performance results agreed that there are not big

gap between the simulated and the physical robots

if the simulation is developed based on the physical

characteristics of the real robots, called as physics-

based simulation. A simulation environment can provide

reliable experiments if we take as much as the real world

factors into the model, instead imposing implausibe

assumptions. Alternatively, the real-world experimen-

tation should be accelerated using simulation results.

This provides another point of view about the reality

gap between simulation and real-world that it can be

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(a) (b) (c)

Fig. 8. Simulation with obstacles and light source: a)Action selection, b) Subsumption c) Feedback neural network

(a) (b) (c)

Fig. 9. Real experimentation a)swarm trajectory, b) standard deviation of relative distance c) number of robots in swarm

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significantly reduced if the robot designerd stand with

both legs.

VI. CONCLUSION

We have examined several aspects of distributed control

paradigms for robot swarms in both the simulation and

the real-world experiments. On the one hand, behaviour-

based control and neural network based control have been

experimented and verified through a series of experiments

of swarm aggregation. Statistical results confirmed that it

is possible to use the two methods to design distributed

control of robot swarms and none of those is prominent.

The choice should be up to the designer’s experience and

their long-term research interest, for example, a researcher

of evoluationary robotics may prefer to use neural network

paradigm as she can apply learning and evolution algorithms

based on measure of the fitness of neural network while

another researcher of control theory may choose behaviour

based control as they can develop a model based control. On

the other hand, statistics of gathered data conducted in the

simulation and the real-world pointed out, in our personal

opinion, that situatedness and emergence of robot swarms

can be reliably conducted in simulation if the simulation is

built based on the physics of the real-world.

Remarkably, the aim of this paper is to commit that the

choice of distributed control paradigms for robot swarms is

not unique, instead depending on the designer. The tendancy

of simulating the robot swarm should be towards the physics-

based approach in order to narrow the reality gap between

simulation and real-world experimentation.

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