Swarm Intelligence in Robotics

Swarm Intelligence in Robotics
Dr. Alaa Khamis
Pattern Analysis and Machine Intelligence
University of Waterloo
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 1/221ECE493T8:2008-2009 - © PAMI Research Group – University of Waterloo
akhamis@pami.uwaterloo.ca

Outline
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo
• Introduction
• Body/Brain Evolution
• Traditional Robotics
• Swarm Robotics• Swarm Robotics
• Swarm Intelligence (SI)
• SI in Traditional Robotics
• SI in Swarm Robotics
• Concluding Remarks

Outline
• Introduction

Introduction
S h di L b
• PAMI Research Group
CIM L bSynchromedia Lab CIM Lab
Intelligent Multi-crane
CRS F3 and CRS T265
Mobile Robotics Lab
Magellan Pro
Pattern Analysis Lab
Speech
Understanding
ATRV-mini
Soccer-playing Robots
Data Mining
Services
Catalog
Computer
visionPattern
Analysis

Introduction
• Swarm Intelligence in Robotics
The objective of this talk is to highlight the different applicationsj g g pp
of the rapidly emerging field of swarm intelligence in solving
complex problems of traditional and swarm roboticscomplex problems of traditional and swarm robotics.

Outline
• Introduction

Body/Brain Evolution
Brains
SI Cognitive Robotics
Swarm
Robotics
100s
g
Robotics
DAI Multiagent
Distributed
Robot System
10s
y
1
AI Robotics Centralized Control
Multiple MachinesMachine MEMS-based Multiple Machines
Bodies
10s
p
1 100s
p

Outline
• Introduction

Traditional Robotics
The Evolutionary Stages
Industrial
Robotics
S i
Service Robots for Professional Use
S i R b t fService
Robotics
Personal
Service Robots for
Personal Use
Personal
RoboticsEvolution

Traditional Robotics
• Traditional Problems
 Environment Perception
 SLAM
 Path Planningg
 Navigation
 Autonomy Autonomy
 Human Interaction
 L i Learning

Outline
• Introduction

Swarm Robotics
• Definition
Swarm robotics is the study of how large number of relatively simple
h i ll b di d t b d i d h th t d i dphysically embodied agents can be designed such that a desired
collective behavior emerges from the local interactions among agents
and between the agents and the environment.g
Resolving complexity.
I i fIncreasing performance.
Simplicity in design.
Box Pushing
Reliable.

Swarm Robotics
• Typical Problem Domains
 Box-pushing
 Foraging
 Aggregation and Segregationgg g g g
 Formation Control
 Cooperative Mapping Cooperative Mapping
 Soccer Tournaments
 Sit P ti Site Preparation
 Sorting http://www.robocup.org/
 Collective Construction
http://www.fira.net/

Swarm Robotics
 Autonomous inspection of complex engineered structures.
• Applications of Swarm Robotics
 Distributed sensing tasks in micromachinery or the human body.
 Killing Cancer Tumors in Human Body
 Mining
 Agricultural Foraging
 i ki
Spy robots
 Cooperative Tracking
 Interactive Art
 S b d C i
UAV drones
 Space-based Construction
 Rescue Operations
 H it i D i i
 Humanitarian Demining
 Surveillance, Reconnaissance and Intelligence. Mobile Trackers

Swarm Robotics
http://www.swarm-bots.org/
• Projects
The SWARM-BOT project aims to
d l b istudy a novel swarm robotics system.
 It is directly inspired by the
collecti e beha ior of socialcollective behavior of social
insects and other animal
societies.
 It focuses on self-organization
and self-assembling of
autonomous agentsautonomous agents.
 Its main scientific challenge
lays in the development of a
Swarm-bots, Marco Dorigo, 2005
novel hardware and of
innovative control solutions.

Swarm Robotics
http://www.ai.sri.com/centibots/index.html
• Projects
The Centibots project:
The Centibots are a team of 100
autonomous robots (97 ActivMediaautonomous robots (97 ActivMedia
Amigobot and 6 ActivMedia Pioneer
2 AT) The goal of the project is to2 AT). The goal of the project is to
demonstrate 100 robots mapping,
tracking guarding in a coherenttracking, guarding in a coherent
fashion during a period of 24 hours.

Swarm Robotics
PAMI Lab• Projects http://horizon.uwaterloo.ca/multirobot/
Leaderless Distributed (LD) Flocking Algorithm
Flocking in Embedded Robotic SystemsFlocking in Embedded Robotic Systems
CORO - Caltech

Swarm Robotics
• Team Size and Composition
Team Size
Alone Pair Limited Infinite
Team Composition
H H tAlone Pair Limited
Group
Infinite
Group
Homogenous Heterogeneous

Swarm Robotics
• Team Reconfigurability
Team Reconfigurability
Static Coordinated Dynamicy

Swarm Robotics
• Communication Pattern
Explicit Communication
Add B d t G h
Implicit Communication
I t ti i I t tiAddress or
Directed
Messages
Broadcast Graph Interaction via
Environment
(Stigmergy)
Interaction
via Sensing
n
n
onAction
Perception
Environment
Virtual
Pheromone
Perception
Actio
Perception

Swarm Robotics
• Communication Range and Bandwidth
Communication Range
None N Infinite
Communication Bandwidth
High Moderate zeroLowNone Near Infinite High Moderate zeroLow

Swarm Robotics
• Organizational Paradigm
Hierarchical Organization Holarchical Organization

Swarm Robotics
Coalition-based Organization Team-based Organization

Swarm Robotics
Congregations Organization Societies Organization
Congregation
Social laws,
norms or
conventions
Socialization
process

Swarm Robotics
Federations Organization Market-based Organization
Regional province
or federate
Federal Level
Delegate or facilitator
B ing agentsBuying agents
Selling agents or
auctioneers

Swarm Robotics
Matrix Organization Compound Organization
Manger
Worker

Swarm Robotics
Paradigm Key
Characteristic
Benefits Drawbacks
Hierarchy Decomposition Maps to many common domains;
handles scale well
Potentially brittle; can lead to bottlenecks or
delays
Holarchy Decomposition with
autonomy
Exploit autonomy of functional units Must organize holons;
lack of predictable performance
Coalition Dynamic, goal- Exploit strength in numbers Short term benefits may not outweighCoalition Dynamic, goal
directed
Exploit strength in numbers Short term benefits may not outweigh
organization
construction costs
Team Group level cohesion Address larger grained problems; task-centric Increased communication
Congregation Long-lived, utility-
directed
Facilitates agent discovery Sets may be overly restrictive
directed
Society Open system Public services; well defined conventions Potentially complex, agents may require
additional
society-related capabilities
Federation Middle-agents Matchmaking, brokering, translation services; facilitates
dynamic agent pool
Intermediaries become bottlenecks
dynamic agent pool
Market Competition through
pricing
Good at allocation; increased utility through
centralization; increased fairness through bidding
Potential for collusion, malicious behavior;
allocation decision complexity can be high
Matrix Multiple managers Resource sharing; multiply-influenced agents Potential for conflicts; need for increased agent
sophistication
Compound Concurrent
organizations
Exploit benefits of several organizational styles Increased sophistication; drawbacks of several
organizational styles
Source: B. Horling and V. Lesser, "A Survey of Multi-Agent Organizational Paradigms, The Knowledge Engineering Review, Vol: 19, Num: 4, pp. 281 - 316, Cambridge
University Press, 2005.

Swarm Robotics
• Challenging Problems
 Coordination
 Algorithm Design
 I l i d T Implementation and Test
 Analysis and Modelling

Swarm Robotics
• Challenging Problems: Coordination
Coordination
Cooperation Collaboration Competition
Planning Negotiation
Resource Allocation
Coordinated Motion Planning
Coordinated Motion Planning

Swarm Robotics
Swarm roboticists face the problem of designing both the
• Challenging Problems: Algorithm Design
physical morphology and behaviours of the individual robots
such that when those robots interact with each other and their
environment, the desired overall collective behaviours will
emerge. At present there are no principled approaches to the
design of low-level behaviours for a given desired collective
behaviour [1].
“collective behavior is not simply the sum of each participant’s
b h i th t th i t l l” [ ]
behavior, as others emerge at the society level” [2].

Swarm Robotics
• Challenging Problems: Algorithm Design
Main decisional abilities:
- Mission planning,
- Task allocation and
- Coordinated task achievement
Management the task allocation,
SchedulingScheduling,
Cooperation/collaboration between the entities,
Conflict avoidance, etc.
Conflict avoidance, etc.

Swarm Robotics
To build and rigorously test a swarm of robots in the laboratory
• Challenging Problems: Implementation and Test
requires a considerable experimental infrastructure. Real-robot
experiments thus typically proceed hand-in-hand with
simulation and good tools are essential [1].

Swarm Robotics
Advanced Robotics Interface for Applications (ARIA):
Robotic Sensing and Control Libraries.
Open Robot Control Software (OROCOS): open-sourcep f p
real time control architecture for different machines.
Microsoft Robotics Studio: is a Windows-based
Pl /St /G b PSG i ft th t d
Microsoft Robotics Studio: is a Windows based
environment for robot control and simulation.
Player/Stage/Gazebo: PSG is open source software that used
and developed by an international community of researchers
from over 30 universities/companies.
3 / p

Swarm Robotics
Stage 3.0 [3]
2,000 minibots 100 Pioneer Robots

Swarm Robotics
A robotic swarm is typically a stochastic, non-linear system and
• Challenging Problems: Analysis and Modelling
constructing mathematical models for both validation and
parameter optimization is challenging. Such models would
surely be an essential part of constructing a safety argument for
real-world applications [1].

Outline
• Introduction

Swarm Intelligence
DNA Cellular Automata
Brain Neural Networks
Immune System Protection of computers
and networks
Evolution Genetic Algorithms
and networks
Evolution Genetic Algorithms
Social Insects Swarm Intelligence
Social Insects Swarm Intelligence

Swarm Intelligence
Nature Unmanned Aerial/Ground VehiclesAnalogies
Ants/UAVs
Prey/Target
Predators/Threats
Environment/
Battlefield

Swarm Intelligence
• Definition
Swarm intelligence (SI) refers to the phenomenon of a system of
spatially distributed individuals coordinating their actions in a
decentralized and self-organized manner so as to exhibit
complex collective behavior.
“SI is the property of a system whereby the
collective behaviors of (unsophisticated)
agents interacting locally with their
environment cause coherent functional global
patterns to emerge.”
Swarm intelligence solves optimization problems.

Swarm Intelligence
• Key Elements
 A large number of “simple” processing elements;
 Neighborhood communication;
 Though convergence in guaranteed, time to convergence is
uncertain.
 Most of the research are experimental: Most of the research are experimental:
ModelObservation Create MetaheuristicExtract
Build
Simulation
Test
Refine
Algorithm
Simulation

Swarm Intelligence
Initialize parameters
• SI-based Approaches
Initialize parameters
Initialize population
While (end condition not satisfied ) loop over all individuals
Find best so farFind best so far
Find best neighbor
Update individual

Swarm Intelligence
• SI-based Approaches
 Ant Colony Optimization (ACO)
 Particle Swarm Optimization (PSO)
 Stochastic Diffusion Search (SDS) Stochastic Diffusion Search (SDS)

Outline
• Introduction

SI in Traditional Robotics
• ACO-based Motion Planning [4]
• PSO-based Motion Planning [5]g [5]
• Wall-following autonomous robot (WFAR) navigation [6]
• Gait Optimization [7]
• Kinematics and Dynamics of Robot Manipulators [8,9]
• Learning [10]

• Motion Planning
Path planning is a process to compute a collision-free path for a
robot from a start position to a given goal position, amidst a
collection of obstacles.
T
S

 Proposed Method
 Step 1: MAKLINK graph theory to establish the free space
model of the mobile robot;;
 Step 2: utilizing the Dijkstra algorithm to find a sub-
optimal collision-free path;optimal collision-free path;
 Step 3: utilizing the ACS algorithm to optimize the location
f h b i l h h l b llof the sub-optimal path so as to generate the globally
optimal path.

 Step 1: MAKLINK-based Free Space Model
1. The heights of the environment and obstacles can be
ignored;
2. There exist some known obstacles distributed in the
environment both the environment and the obstacles haveenvironment, both the environment and the obstacles have
a polygonal shape;
I d t id i th t l t th b t l3. In order to avoid a moving path too close to the obstacles,
the boundaries of every obstacle can be expanded.

Real
Ob t l
F MAKLINK li
Obstacle
Grown Obstacle
Free MAKLINK line:
1) Either its two end points are two vertices on two
different grown obstacles or one point is a vertex ofdifferent grown obstacles or one point is a vertex of
a grown obstacle and the other is located on a
boundary of the environment;
2) Every free MAKLINK line cannot intersect any of
the grown obstacles.
boundary

 Step 1: MAKLINK-based Free Space Model [11,5]
1. Find all the lines that connect one of the corners that belong to a
◊ Constructing MAKLINK graph
polygonal obstacle, with all the other obstacles’ corners including the
corners of the current obstacle;
S
T

2. Delete the redundant free links to make every free space, of which the
edges are free links, obstacle edges and boundary walls, be a convex
polygon and its area be largest;
S
T

3. Find the midpoint of the remained free links and take them as the
path nodes, labeling orderly as 1, 2,…, n. The connections among the
midpoints that belong to the same convex area compose a network.
S
T

v1, v2, . . . , vl are the
middle points of these
f MAKLINK lifree MAKLINK lines;
l is total number of the
f lifree MAKLINK lines on a
MAKLINK graph.
l=26
v0 is S
vl+1 is T
Network graph for free motion of robot

 Step 2: Dijkstra Algorithm
vvvvvvS  1312111021
Sub-optimal path
Path length= 507 692 m
Tvvv  241716
30
Path length= 507.692 m.
This path is just a sub-optimal
path because it passes only
through the middle points of
th f MAKLINK li
Sub-optimal path generated by Dijkstra
those free MAKLINK lines

 Step 3: ACS Algorithm
1210 ...  dPPPP
Assume sub-optimal path generated by the Dijkstra algorithm is
1210 d
These path nodes lie on the middle points of the relevant free
MAKLINK lines Now we need to adjust and optimize theirMAKLINK lines. Now, we need to adjust and optimize their
locations on their corresponding free MAKLINK lines.
dihhPPPP iiiiii ,...,2,1],1,0[,)( 121 
midpoint502/)(
01

 iii
hPPP
hPP
Path Coding Method
1
midpoint5.02/)(
2
21


iii
iiii
hPP
hPPP

)}()({
d
hPhPlengthL
The objective function of the optimization problem will be:
)}(),({ 11
0


 ii
i
ii hPhPlengthL
ACS algorithm is used to find the optimal parameter set
}{ ***
hhh
such that L has the minimum value.
},...,,{ 21 dhhh

Grid graph on which there
are dx11 nodes in total.
nij is node j on line hi.
The path of an ant departThe path of an ant depart
from the starting point S is
S 
Tn
nnS
dj
jj

 ...21
Generating of nodes and moving paths

Assume that at initial time t =0
◊ Pheromone Concentration
Assume that at initial time t 0
All the nodes have the same pheromone concentration 0:
)10,...,2,1,0;,...,2,1()0(  jdioij 

◊ Transition Rule
Assume the number of ants is m.
In moving process, for an ant k, when it locates on line hi-1, it
will choose a node j from the eleven nodes of the next line hi
Assume the number of ants is m.
will choose a node j from the eleven nodes of the next line hi
to move to according to the following rule:
 


 
 
,
},])].[({[maxarg
o
oiuiuAu
qqifJ
qqift
j





◊ Transition Rule
 }])] [({[maxarg qqift 



 
 
,
},])].[({[maxarg
o
oiuiuAu
qqifJ
qqift
j



where
A represents the set: {0, 1, 2, . . . , 10};
iu(t) is the pheromone concentration of node niu;

◊ Transition Rule



 
 
,
},])].[({[maxarg
o
oiuiuAu
qqifJ
qqift
j



iu represents the visibility of node niu and is computed by the
following equation:
1.1 *
ijij yy 
1.1
j
ij 
yij is the y-coordinate of node nij and y*
ij are values that are
d h h d h l b h
corresponding to the path nodes on the optimal robot path
generated by the ACS algorithm in the previous iteration.

◊ Transition Rule



 
 
,
},])].[({[maxarg
o
oiuiuAu
qqifJ
qqift
j



 is an adjustable parameter which controls the relative
importance of visibility iu versus pheromone concentration
( )iu(t);
q is a random variable uniformly distributed over [0, 1]; q0 is a
tunable parameter (0q0 1);

◊ Transition Rule



 
 
,
},])].[({[maxarg
o
oiuiuAu
qqifJ
qqift
j



J is a node which is selected according to the following
probability formula and “Roulette Wheel Selection Method”

 10
])] [([
])].[([
)(


 iJiJk
iJ
t
t
tP
0
])].[([


 iwiw t

◊ Pheromone Update: After Each Iteration
)()()1()( ttt  )(.)().1()( ttt ijijij  
10  


L
tij
1
)(
L
where L+ is the length of robot path corresponding to the
best ant tour.
b

◊ Pheromone Update: After passing through a node nij
tt  )()1()(  oijij tt  .)().1()( 
When a node is visited several times by ants, repeatedly
applying the local updating rule will make the pheromone
level of this node diminish.
This has the effect of making the visited nodes less and
less attractive to the ants, which indirectly favors the
exploration of not yet visited nodes
exploration of not yet visited nodes.

 Results
ACS
Algorithm
Real-
coded GA
Average CPU 0 00059 0 00067Average CPU
time per
iteration (sec.)
0. 00059 0. 00067
Average number 175 912
of iterations
needed for
convergence
Average CPU 0.1033 0.6110Average CPU
time needed for
obtaining
optimal solution
(sec.)
0.1033 0.6110
(sec.)

• PSO-based Motion Planning [5]
Sub-optimal path from Dijkstra algorithm is
1210 ...  dPPPP
Th h d li h iddl i f h l fThese path nodes lie on the middle points of the relevant free
MAKLINK lines. Location adjustment:
dihhPPPP iiiiii ,...,2,1],1,0[,)( 121 
0 hPP
1
midpoint5.02/)(
0
2
21
1



iii
iiii
iii
hPP
hPPP
hPP
Path Coding Method
2 iii

Each particle Xi is constructed as:
i
),...,( 21 di hhhX  )( ii hP
The fitness value of each particle is:

d
Euclidian
Distance
)}(),({)( 11
0


 ii
i
iii hPhPlengthXf )( 11  ii hP
The smaller the fitness value is, the better the solution is.

1. Initialize particles at random, and set pBest= Xi;
i
2. Calculate each particle’s fitness value according to equation
d
)}(),({)( 11
0


 ii
d
i
iii hPhPlengthXf
and label the particle with the minimum fitness value as
gBest;

3. For k1=1 to k1max do {
4. For each particle Xi do {
5 Update v and x according to the following equations:5. Update vi and xi according to the following equations:
)]()([()
)]()([())()1(
2
1
kxkgBestrandc
kxkpBestrandckvkv
i
iiii

 
6. Calculate the fitness according to equation
,1),1()()1( nikvkxkx iii 
7 Update gBest and pBest ;
)}(),({)( 11
0


 ii
d
i
iii hPhPlengthXf
7. Update gBest and pBesti ;
8. If ||v||  , terminate ;}

Outline
• Introduction

SI in Swarm Robotics
• Multirobot Control [12]
• Collective Robotic Search (CRS) [13]( ) [ 3]
• Odor-source Localization [14,15]
• Mobile Sensor Deployment [16]
• Learning [17]
• Communication Relay [18]
• Group Behavior [19]

Unintelligent carts are commonly found in large
airports. Travelers pick up carts at designated
points and leave them in arbitrary places. It is a
considerable task to re-collect them.
It is, therefore, desirable that intelligent carts (intelligent robots), , g ( g )
draw themselves together automatically.

 Objectives
Using mobile software agents to locate robots scattered in a
field, e.g. an airport, and make them autonomously
determine their moving behaviors by using an ACO-based
clustering algorithm.

 Assumptions
- Each robot has a function for collision avoidance;
- Each robot has a function to sense RFID embedded in the
floor carpet to detect its precise coordinate in the field.
A team of mobile robots work under control of mobile agents RFID under a carpet tile

 Quasi Optimal Robots Collection
Step 1:
One mobile agent issued from the host computer visits
scattered robots one by one and collects the positions of them.
Mobile
Agent
Assembly
iPoint Assembly
Point
Intelligent
Cart
Host
Computer
Assembly
Point

Step 2:
Another agent called the simulation agent, performs ACC
algorithm and produces the quasi optimal gathering positions
for the mobile robots. The simulation agent is a static agent
that resides in the host computerthat resides in the host computer.
Assembly
i
Host
Computer
Point Assembly
Point
Intelligent
CartSimulation
Agent
Assembly
Point
Agent
ACO-based
Clustering

Step 3:
A number of mobile agents are issued from the host
computer. One mobile agent migrates to a designated mobile
robot, and drives the robot to the assigned quasi optimal
position that is calculated in step 2position that is calculated in step 2.
Assembly
i
Mobile
Agents
Host
Computer
Point Assembly
Point
Intelligent
Cart
Assembly
Point

 ACO-based Clustering
- More objects are clustered in a place where strong
pheromone sensed.
- When the artificial ant finds a cluster with certain number
of objects, it tends to avoid picking up an object from the
cluster. This number can be updated later.
- If the artificial ant cannot find any cluster with certain
strength of pheromone, it just continues a random walk.

Start
Have an
bj t
yes no
object
Pheromone Random
Picking up or not unlocked
object is probabilistically
determined using the walk walk
Empty space
d t t d?
object
d t t d?
nono
determined using the
formula:
*)( klp  detected? detected?
Put object Catch object
yes yes100
)(
1)(
klp
pf


p: density of pheromone
Satisfied
requirement?
no
p: density of pheromone=
number of adjacent objects.
Thus, when an object is
requirement?
End
yes
completely surrounded by
other objects, p is the max. value=9

100
*)(
1)(
klp
pf


k: constant value
Authors selected k= 13 in order to prevent any object surroundedAuthors selected k 13 in order to prevent any object surrounded
by other eight objects be picked up.
13*)08(  (never pick it up).004.004.11
100
13*)08(
1)( 

pf

100
*)(
1)(
klp
pf


l: constant value to make an object be locked.
l = zero (not locked).
If c=# of clusters & o=# of objects
37and31
63and32


lpoc
lpoc


Any objects meet these
conditions are locked This
19
37and31


lp
lpoc

 conditions are locked. This
lock process prevents artificial
ants remove objects from
growing clusters.

Start
Start
Have an
object
yes
object
Pheromone
lk
In “pheromone walk” state, an
artificial ant tends probabilistically
move toward a place it senses the walk
Empty space
detected?
no
move toward a place it senses the
strongest pheromone. The probability
that the artificial ant takes a certain
di i i / h i h h detected?
Put object
yes
direction is n/10, where n is the strength
of the sensed pheromone of that
direction.
Satisfied
requirement?
no
equ e e t?
End
yes

Start
Start
Have an
object
yes
object
Pheromone
lk
An artificial ant carrying an object
determines whether to put the carrying
object or to continue to carry This walk
Empty space
detected?
no
object or to continue to carry. This
decision is made based on the formula:
*
)(
kp
pf detected?
Put object
yes100
)(
p
pf 
The more it senses strong pheromone,
h d h
Satisfied
requirement?
no
the more it tends to put the carrying
object.
equ e e t?
End
yesThe probability f(p)=1 (must put it down).

Start
Start
Have an
object
yes
object
Pheromone
lk
Termination Condition:
h b f l d l i walk
Empty space
detected?
no
The number of resulted clusters is
less than ten, and
detected?
Put object
yesall the clusters have more or equal
to three objects.
Satisfied
requirement?
no
equ e e t?
End
yes

 Results
Airport Ant Colony Clustering Specified Position Clustering
Airport Field (Objects: 400, Ant Agent: 100)
po t
field
t Co o y C uste g Spec ed os t o C uste g
Cost Ave Clust Cost Ave Clust
1 4822 12.05 1 11999 29.99 4
2 3173 7.93 6 12069 30.17 4
3 3648 9.12 4 12299 30.74 4
4 3803 9.51 3 12288 30.72 44 3803 9.51 3 12288 30.72 4
5 4330 10.82 5 1215 30.31 4
Clust represents the number of clusters, and
Ave represents the average distance of all the objects moved.

• Collective Robotic Search (CRS) [13]
A group of unmanned mobile robots are searching for a specified
target in a high risk environment.

 Assumptions
- A number of robots/particles are randomly dropped into a
specified area and flown through the search space with one
new position calculated for each particle per iteration;
- The coordinates of the target are known and the robots use
a fitness function, in this case the Euclidean distance of the
individual robots relative to the target, to analyze the status
f th i t itiof their current position;
- Obstacle’s boundary coordinates are known.

 PSO-CRS Algorithm
Step 1:
A population of robots isA population of robots is
initialized in the search
environment containing aenvironment containing a
target and an obstacle,
with random positions,p
velocities, personal best
positions (pid), and global
best position (pgd).

Step 2:
The fitness value, Euclidean
distance from the robot to
the target, is determined for
each robot where T and Teach robot where Tx and Ty
are the targets coordinates
and Px and Py are the
t di t f thcurrent coordinates of the
individual robot.
22
)()( PTPTfitness 
)()( yyxx PTPTfitness 

Step 3:
The robot’s fitness is compared with its previous best fitness
(pBestid) for every iteration to determine the next possible
coordinate position for each robot in the search environment.
The next possible velocity and position of each robot areThe next possible velocity and position of each robot are
determined according to the following equations:
)]()([())()1( 1  kxkpBestrandckvkv idididid 
)1()()1(
)]()([()
)]()([())()1(
2
1



kvkxkx
kxkgBestrandc
kxkpBestrandckvkv
idd
idididid 
)1()()1(  kvkxkx ididid

Step 4:
If the next possible position xid(k+1) resides within theIf the next possible position xid(k+1) resides within the
obstacle space, the obstacle avoidance mechanism is
employed,
otherwise the robot moves to this new position.

Obstacle avoidance mechanism:
),,( nextverthoriz 
)points,,_,_,_,_(
),,(
dircenterycenterxstartystartxarc
dir: CW – CCW
points=4
nearest corner of
the obstacle
The new position of the particle
is set at the second point in the
arc
arc.

Step 5:
The pBestid with the best fitness for the entire swarm isid
determined and the global best coordinate location, gBestd, is
updated with this pBestid.
Step 6:
Until convergence is reached, repeat steps.

 Simulation Results
Search space: 20 by 20 units.
Vmax = 0.5 units.
# of robots = 10# of robots 10
# of targets = 1
# of obstacles=1

Robots’ pathways to the target location R b t ’ th t th t t l ti
Robots pathways to the target location.
Two robots collide into the obstacle.
Robots’ pathways to the target location
with obstacle avoidance mechanism.

Average Number of Iterations taken by the Swarm
with standard deviations over 20 trails
Obstacle
Type
PSO Parameters 1-
c1=0.5,c2=2, w=0.6
PSO Parameters 2-
c1=2,c2=2, w=0.6
Square Average±std 89 43±9 86 105 6±10 68Square Average±std 89.43±9.86 105.6±10.68
Maximum 104 128
Minimum 66 85
Circle Average±std 76.75±8.75 106.1±11.61
Maximum 95 128
Minimum 64 78

Outline
• Introduction

Concluding Remarks
• Swarm intelligence techniques such as Ant Colony Optimization
(ACO) and Particle Swarm Optimization (PSO) have shown to
be an effective global optimization algorithms in traditional and
swam robotics.
• ACO and PSO have been successfully applied in solving
problems such as motion planning, gait optimization, group
behavior, control, learning, odor-source localization, collective
robotic search and mobile sensor deployment, to mention a few.

References
[1] E. Sahin and A. Winfield, “Special issue on swarm robotics,” Swarm Intelligence, 2: 69–72, Springer Science,
2008.
[2] Pasteels et al. From Individual to Collective Behavior in Social Insects. Pages 155-175, 1987.
[3] Richard Vaughan, “Massively multi-robot simulation in stage,” Swarm Intelligence 2: 189–208, 2008.3 g , y g , g 9 ,
[4] T. Guan-Zheng, H. Huan and S. Aaron, "Ant Colony System Algorithm for Real-Time Globally Optimal Path
Planning of Mobile Robots", Acta Automatica Sinica, 33(3), p. 279-285, March 2007.
[5] Xueping Zhang, Hui Yin, Hongmei Zhang and Zhongshan Fan, “Spatial Clustering with Obstacles Constraints
by Hybrid Particle Swarm Optimization with GA Mutation “, LNCS, Springer, ISBN 978-3-540-87731-8, pp.569-by Hybrid Particle Swarm Optimization with GA Mutation , LNCS, Springer, ISBN 978 3 540 87731 8, pp.569
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Navigation”, LNCS, Springer, ISBN 978-3-540-23526-2, pp. 41-50, 2004.
[7] Cord Niehaus Thomas R¨ofer Tim Laue "Gait Optimization on a Humanoid Robot using Particle Swarm[7] Cord Niehaus, Thomas R ofer, Tim Laue, Gait Optimization on a Humanoid Robot using Particle Swarm
Optimization", The second workshop on Humanoid Soccer Robots, 2007.
[8] Gursel Alıcıa, Romuald Jagielskib, Y. Ahmet Sekerciogluc, Bijan Shirinzadehd, “Prediction of geometric errors
of robot manipulators with Particle Swarm Optimisation method”, Robotics and Autonomous Systems 54, pp.
956–966, 2006.956 966, 2006.
[9] Takeshi Matsui, Masatoshi Sakawa, Takeshi Uno, Kosuke Kato, Mitsuru Higashimori, and Makoto Kaneko,
“Particle Swarm Optimization for Jump Height Maximization of a Serial Link Robot”, Journal of Advanced
Computational Intelligence and Intelligent Informatics, Vol.11, No.8 pp. 956-963, 2007.
[10] Fabien Moutarde “A robot behavior learning experiment using Particle Swarm Optimization for training a
[10] Fabien Moutarde, A robot behavior-learning experiment using Particle Swarm Optimization for training a
neural-based Animat”, 10th International Conference on Control, Automation, Robotics and Vision (ICARCV
2008), 2008.

References
[11] Maki K. HABIB, Hajime ASAMA, “Efficient method to generate collision free paths for autonomous mobile
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Robots and Systems. Japan, November, 1991, 563-567.
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“Design of a Multi-Robot System Using Mobile Agents with Ant Colony Clustering” Proceedings of the 42ndDesign of a Multi-Robot System Using Mobile Agents with Ant Colony Clustering , Proceedings of the 42
Hawaii International Conference on System Sciences - 2009.
[13] Smith, L., Venayagamoorthy, G. K. and Holloway, P. (2006): Obstacle Avoidance in Collective Robotic Search
Using Particle Swarm Optimization. IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA, May
2006.
[ ] i i i h i i i d i “ b bili l i h f l i[14] Fei Li, Qing-Hao Meng, Shuang Bai, Ji-Gong Li and Dorin Popescu, “Probability-PSO Algorithm for Multi-
robot Based Odor Source Localization in Ventilated Indoor Environmen”, LNCS, Springer, 978-3-540-88512-2,
pp. 1206-1215, 2008.
[15] Wisnu Jatmiko, Petrus Mursanto, Benyamin Kusumoputro, Kosuke Sekiyama and Toshio Fukuda, “Modified
PSO algorithm based on flow of wind for odor source localization problems in dynamic environments”, WSEASPSO algorithm based on flow of wind for odor source localization problems in dynamic environments , WSEAS
TRANSACTIONS on SYSTEMS archive, 7(2), pp. 106-113, 2008.
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Optimization in Ad Hoc Sensor Networks,“ LNCS, Springer, ISBN 978-3-540-30881-2, pp. 533-541, 2005.
[17] Jim Pugh and Alcherio Martinoli, “Multi-robot learning with particle swarm optimization”, International
fConference on Autonomous Agents,ISBN:1-59593-303-4, p. 441-448, Japan, 2006.
[18] Sabine Hauert, Laurent Winkler, Jean-Christophe Zufferey, and Dario Floreano, “Ant-based swarming with
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