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Swarm Intelligence in Robotics
Dr. Alaa KhamisPattern 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
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
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 2/222ECE493T8:2008-2009 - © PAMI Research Group – University of Waterloo
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
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 3/223ECE493T8:2008-2009 - © PAMI Research Group – University of Waterloo
Introduction
S h di L b
• PAMI Research Group
CIM L bSynchromedia Lab CIM Lab
Intelligent Multi-craneCRS F3 and CRS T265
Mobile Robotics Lab
Magellan Pro
Pattern Analysis LabSpeech
Understanding
ATRV-miniSoccer-playing Robots
Data Mining Services Catalog
Computer visionPattern
Analysis
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 4/224ECE493T8:2008-2009 - © PAMI Research Group – University of Waterloo
Introduction• Swarm Intelligence in Robotics
The objective of this talk is to highlight the different applications j 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.
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 5/225ECE493T8:2008-2009 - © PAMI Research Group – University of Waterloo
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
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 6/226ECE493T8:2008-2009 - © PAMI Research Group – University of Waterloo
Body/Brain Evolution
Brains
SI Cognitive RoboticsSwarm
Robotics
100s
g Robotics
DAI MultiagentDistributed
Robot System10s
y
1AI Robotics Centralized Control
Multiple MachinesMachine MEMS-based Multiple Machines
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Bodies10s
p
1 100s
p
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
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 8/228ECE493T8:2008-2009 - © PAMI Research Group – University of Waterloo
Traditional Robotics
The Evolutionary Stages
IndustrialRobotics
S i
Service Robots for Professional Use
S i R b t fServiceRobotics
Personal
Service Robots forPersonal Use
PersonalRoboticsEvolution
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Traditional Robotics• Traditional Problems
Environment Perception
SLAM
Path Planningg
Navigation
Autonomy Autonomy
Human Interaction
L i Learning
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 10/2210ECE493T8:2008-2009 - © PAMI Research Group – University of Waterloo
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
MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 11/2211ECE493T8:2008-2009 - © PAMI Research Group – University of Waterloo
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 fIncreasing performance.
Simplicity in design.
Box Pushing
Reliable.
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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
Sortinghttp://www.robocup.org/
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Collective Constructionhttp://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
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Humanitarian Demining Surveillance, Reconnaissance and Intelligence. Mobile Trackers
Swarm Roboticshttp://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-organizationand self-assembling of autonomous agentsautonomous agents.
Its main scientific challenge lays in the development of a
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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.
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Swarm RoboticsPAMI Lab• Projects
http://horizon.uwaterloo.ca/multirobot/
Leaderless Distributed (LD) Flocking AlgorithmFlocking in Embedded Robotic Systems Flocking in Embedded Robotic Systems
CORO - Caltech
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Swarm Robotics• Team Size and Composition
Team Size
Alone Pair Limited Infinite
Team Composition
H H tAlone Pair LimitedGroup
InfiniteGroup
Homogenous Heterogeneous
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Swarm Robotics• Team Reconfigurability
Team Reconfigurability
Static Coordinated Dynamicy
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Swarm Robotics• Communication Pattern
Explicit Communication
Add B d t G h
Implicit Communication
I t ti i I t tiAddress or DirectedMessages
Broadcast Graph Interaction via Environment (Stigmergy)
Interaction via Sensing
nn
onA
ctio
n
Perc
eptio
n
EnvironmentVirtual
Pheromone Perc
eptio
n
Act
io
Perc
eptio
n
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Swarm Robotics• Communication Range and Bandwidth
Communication Range
None N Infinite
Communication Bandwidth
High Moderate zeroLowNone Near Infinite High Moderate zeroLow
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Swarm Robotics• Organizational Paradigm
Hierarchical Organization Holarchical Organization
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Swarm Robotics• Organizational Paradigm
Coalition-based Organization Team-based Organization
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Swarm Robotics• Organizational Paradigm
Congregations Organization Societies Organization
Congregation
Social laws, norms or
conventions
Socialization process
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Swarm Robotics• Organizational Paradigm
Federations Organization Market-based OrganizationRegional province
or federate
Federal Level
Delegate or facilitator
B ing agentsBuying agents
Selling agents or auctioneers
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Swarm Robotics• Organizational Paradigm
Matrix Organization Compound OrganizationManger
Worker
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Swarm Robotics• Organizational Paradigm
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 withautonomy
Exploit autonomy of functional units Must organize holons;lack of predictable performance
Coalition Dynamic, goal- Exploit strength in numbers Short term benefits may not outweigh Coalition Dynamic, goaldirected
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 restrictivedirected
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 bottlenecksdynamic 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 agentsophistication
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Compound Concurrent organizations
Exploit benefits of several organizational styles Increased sophistication; drawbacks of severalorganizational 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
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Swarm Robotics• Challenging Problems: Coordination
Coordination
Cooperation Collaboration Competition
Planning Negotiation
Resource Allocation
Coordinated Motion Planning
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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” [ ]
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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.
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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].
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Swarm Robotics• Challenging Problems: Implementation and Test
Advanced Robotics Interface for Applications (ARIA):Robotic Sensing and Control Libraries.
Open Robot Control Software (OROCOS): open-source p f preal 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.
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3 / p
Swarm Robotics
Stage 3.0 [3]
• Challenging Problems: Implementation and Test
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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].
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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
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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
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Social Insects Swarm Intelligence
Swarm Intelligence
Nature Unmanned Aerial/Ground VehiclesAnalogies
Ants/UAVs
Prey/Target
Predators/Threats
Environment/Battlefield
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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.”
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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
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AlgorithmSimulation
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
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Swarm Intelligence• SI-based Approaches
Ant Colony Optimization (ACO)
Particle Swarm Optimization (PSO)
Stochastic Diffusion Search (SDS) Stochastic Diffusion Search (SDS)
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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
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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]
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SI in Traditional Robotics• 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
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S
SI in Traditional Robotics• ACO-based Motion Planning [4]
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 ll of the sub-optimal path so as to generate the globally
optimal path.
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SI in Traditional Robotics
Step 1: MAKLINK-based Free Space Model
• ACO-based Motion Planning [4]
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 have environment, both the environment and the obstacles have
a polygonal shape;
I d t id i th t l t th b t l 3. In order to avoid a moving path too close to the obstacles,
the boundaries of every obstacle can be expanded.
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SI in Traditional Robotics
Step 1: MAKLINK-based Free Space Model
• ACO-based Motion Planning [4]
RealOb 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 of different grown obstacles or one point is a vertex of a grown obstacle and the other is located on a boundary of the environment;
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2) Every free MAKLINK line cannot intersect any of the grown obstacles. boundary
SI in Traditional Robotics
Step 1: MAKLINK-based Free Space Model [11,5]
• ACO-based Motion Planning [4]
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
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T
SI in Traditional Robotics
Step 1: MAKLINK-based Free Space Model [11,5]
• ACO-based Motion Planning [4]
2. Delete the redundant free links to make every free space, of which the
◊ Constructing MAKLINK graph
edges are free links, obstacle edges and boundary walls, be a convex polygon and its area be largest;
S
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T
SI in Traditional Robotics
Step 1: MAKLINK-based Free Space Model [11,5]
• ACO-based Motion Planning [4]
3. Find the midpoint of the remained free links and take them as the
◊ Constructing MAKLINK graph
path nodes, labeling orderly as 1, 2,…, n. The connections among the midpoints that belong to the same convex area compose a network.
S
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T
SI in Traditional Robotics
Step 1: MAKLINK-based Free Space Model
• ACO-based Motion Planning [4]
v1, v2, . . . , vl are the middle points of these f MAKLINK lifree MAKLINK lines;
l is total number of the f li free MAKLINK lines on a MAKLINK graph.
l=26
v0 is S
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vl+1 is TNetwork graph for free motion of robot
SI in Traditional Robotics
Step 2: Dijkstra Algorithm
• ACO-based Motion Planning [4]
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
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Sub-optimal path generated by Dijkstra
those free MAKLINK lines
SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
1210 ... dPPPPAssume 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 their MAKLINK lines. Now, we need to adjust and optimize their locations on their corresponding free MAKLINK lines.
dihhPPPP iiiiii ,...,2,1 ],1,0[ ,)( 121
midpoint502/)( 0 1
iii
hPPPhPP
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Path Coding Method 1
midpoint 5.0 2/)(
2
21
iii
iiii
hPPhPPP
SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
)}()({d
hPhPlengthL
The objective function of the optimization problem will be:
)}(),({ 110
iii
ii hPhPlengthL
ACS algorithm is used to find the optimal parameter set
}{ *** hhhsuch that L has the minimum value.
},...,,{ 21 dhhh
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SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
Grid graph on which there are dx11 nodes in total.
nij is node j on line hi.
The path of an ant depart The path of an ant depart from the starting point S is
S
Tn
nnS
dj
jj
...21
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Generating of nodes and moving paths
SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
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
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SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
◊ 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:
, },])].[({[max arg
o
oiuiuAu
qqifJqqift
j
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SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
◊ Transition Rule
}])] [({[maxarg qqift
, },])].[({[max arg
o
oiuiuAu
qqifJqqift
j
where
A represents the set: {0, 1, 2, . . . , 10};
iu(t) is the pheromone concentration of node niu;
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SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
◊ Transition Rule
}])] [({[maxarg qqift
, },])].[({[max arg
o
oiuiuAu
qqifJqqift
j
iu represents the visibility of node niu and is computed by the following equation: 1.1 *
ijij yy
1.1j
ij
yij is the y-coordinate of node nij and y*ij are values that are
d h h d h l b h
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corresponding to the path nodes on the optimal robot pathgenerated by the ACS algorithm in the previous iteration.
SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
◊ Transition Rule
}])] [({[maxarg qqift
, },])].[({[max arg
o
oiuiuAu
qqifJqqift
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
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tunable parameter (0q0 1);
SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
◊ Transition Rule
}])] [({[maxarg qqift
, },])].[({[max arg
o
oiuiuAu
qqifJqqift
j
J is a node which is selected according to the following probability formula and “Roulette Wheel Selection Method”
10
])] [([
])].[([)(
iJiJkiJ
t
ttP
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0
])].[([
iwiw t
SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
◊ Pheromone Update: After Each Iteration
)()()1()( ttt )(.)().1()( ttt ijijij
10
L
tij1)(L
where L+ is the length of robot path corresponding to the best ant tour.
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b
SI in Traditional Robotics
Step 3: ACS Algorithm
• ACO-based Motion Planning [4]
◊ 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
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exploration of not yet visited nodes.
SI in Traditional Robotics
Results
• ACO-based Motion Planning [4]
ACS Algorithm
Real-coded GA
Average CPU 0 00059 0 00067Average CPU time per
iteration (sec.)
0. 00059 0. 00067
Average number 175 912of iterations needed for
convergence
Average CPU 0.1033 0.6110Average CPU time needed for
obtaining optimal solution
(sec.)
0.1033 0.6110
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(sec.)
SI in Traditional Robotics• PSO-based Motion Planning [5]
Sub-optimal path from Dijkstra algorithm is
1210 ... dPPPP
Th h d li h iddl i f h l f These 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 midpoint 5.0 2/)(
0
2
21
1
iii
iiii
iii
hPPhPPP
hPP
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Path Coding Method2 iii
SI in Traditional Robotics
Each particle Xi is constructed as:
• PSO-based Motion Planning [5]
i
),...,( 21 di hhhX )( ii hP
The fitness value of each particle is:
d
Euclidian Distance
)}(),({)( 110
iii
iii hPhPlengthXf)( 11 ii hP
The smaller the fitness value is, the better the solution is.
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SI in Traditional Robotics
1. Initialize particles at random, and set pBest= Xi;
• PSO-based Motion Planning [5]
i
2. Calculate each particle’s fitness value according to equation
d
)}(),({)( 110
ii
d
iiii hPhPlengthXf
and label the particle with the minimum fitness value as gBest;
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SI in Traditional Robotics
3. For k1=1 to k1max do {
• PSO-based Motion Planning [5]
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
kxkgBestrandckxkpBestrandckvkv
i
iiii
6. Calculate the fitness according to equation
,1 ),1()()1( nikvkxkx iii
7 Update gBest and pBest ;
)}(),({)( 110
ii
d
iiii hPhPlengthXf
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7. Update gBest and pBesti ;
8. If ||v|| , terminate ;}
SI in Traditional Robotics• PSO-based Motion Planning [5]
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Outline
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• Introduction
• Body/Brain Evolution
• Traditional Robotics
• Swarm Robotics• Swarm Robotics
• Swarm Intelligence (SI)
• SI in Traditional Robotics
• SI in Swarm Robotics
• Concluding Remarks
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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]
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SI in Swarm Robotics• Multirobot Control [12]
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.
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SI in Swarm Robotics• Multirobot Control [12]
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.
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SI in Swarm Robotics• Multirobot Control [12]
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.
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A team of mobile robots work under control of mobile agents RFID under a carpet tile
SI in Swarm Robotics• Multirobot Control [12]
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
AssemblyiPoint Assembly
Point
IntelligentCart
HostComputer
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AssemblyPoint
SI in Swarm Robotics• Multirobot Control [12]
Quasi Optimal Robots Collection
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 agentthat resides in the host computerthat resides in the host computer.
Assemblyi
HostComputer
Point AssemblyPoint
IntelligentCartSimulation
Agent
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AssemblyPoint
AgentACO-based Clustering
SI in Swarm Robotics• Multirobot Control [12]
Quasi Optimal Robots Collection
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.
Assemblyi
Mobile Agents
HostComputer
Point AssemblyPoint
IntelligentCart
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AssemblyPoint
SI in Swarm Robotics• Multirobot Control [12]
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.
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SI in Swarm Robotics• Multirobot Control [12]
ACO-based Clustering
Start
Have anbj t
yes noobject
Pheromone Random
Picking up or not unlocked object is probabilistically determined using the walk walk
Empty spaced t t d?
objectd t t d?
nono
determined using the formula:
*)( klp detected? detected?
Put object Catch object
yes yes100)(1)( klppf
p: density of pheromone
Satisfiedrequirement?
no
p: density of pheromone=number of adjacent objects.Thus, when an object is
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requirement?
End
yescompletely surrounded byother objects, p is the max. value=9
SI in Swarm Robotics• Multirobot Control [12]
ACO-based Clustering
100*)(1)( klppf
k: constant value
Authors selected k= 13 in order to prevent any object surrounded Authors selected k 13 in order to prevent any object surrounded by other eight objects be picked up.
13*)08( (never pick it up).004.004.11100
13*)08(1)(
pf
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SI in Swarm Robotics• Multirobot Control [12]
ACO-based Clustering
100*)(1)( klppf
l: constant value to make an object be locked.
l = zero (not locked).
If c=# of clusters & o=# of objects
37and3163 and 32
lpoclpoc
Any objects meet these conditions are locked This
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19 37 and 31
lplpoc
conditions are locked. This lock process prevents artificial ants remove objects from growing clusters.
SI in Swarm RoboticsStart
• Multirobot Control [12]
ACO-based Clustering
Start
Have anobject
yesobject
Pheromonelk
In “pheromone walk” state, anartificial ant tends probabilistically move toward a place it senses the walk
Empty spacedetected?
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
yesdirection is n/10, where n is the strength of the sensed pheromone of that direction.
Satisfiedrequirement?
no
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equ e e t?
End
yes
SI in Swarm RoboticsStart
• Multirobot Control [12]
ACO-based Clustering
Start
Have anobject
yesobject
Pheromonelk
An artificial ant carrying an object determines whether to put the carrying object or to continue to carry This walk
Empty spacedetected?
no
object or to continue to carry. This decision is made based on the formula:
*)( kppf detected?
Put object
yes100)( ppf
The more it senses strong pheromone, h d h
Satisfiedrequirement?
no
the more it tends to put the carrying object.
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equ e e t?
End
yesThe probability f(p)=1 (must put it down).
SI in Swarm RoboticsStart
• Multirobot Control [12]
ACO-based Clustering
Start
Have anobject
yesobject
Pheromonelk
Termination Condition:
h b f l d l i walk
Empty spacedetected?
no
The number of resulted clusters is less than ten, and
detected?
Put object
yesall the clusters have more or equal to three objects.
Satisfiedrequirement?
no
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equ e e t?
End
yes
SI in Swarm Robotics• Multirobot Control [12]
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 gCost Ave Clust Cost Ave Clust
1 4822 12.05 1 11999 29.99 42 3173 7.93 6 12069 30.17 43 3648 9.12 4 12299 30.74 44 3803 9.51 3 12288 30.72 44 3803 9.51 3 12288 30.72 45 4330 10.82 5 1215 30.31 4
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Clust represents the number of clusters, andAve represents the average distance of all the objects moved.
SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
A group of unmanned mobile robots are searching for a specified target in a high risk environment.
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SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
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.
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SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
PSO-CRS Algorithm
Step 1:
A population of robots is A population of robots is initialized in the search environment containing a environment containing a target and an obstacle, with random positions, pvelocities, personal best positions (pid), and global
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best position (pgd).
SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
PSO-CRS Algorithm
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 Tyare the targets coordinates and Px and Py are the
t di t f th current coordinates of the individual robot.
22 )()( PTPTfitness
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)()( yyxx PTPTfitness
SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
PSO-CRS Algorithm
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 are The next possible velocity and position of each robot are determined according to the following equations:
)]()([())()1( 1 kxkpBestrandckvkv idididid
)1()()1()]()([()
)]()([())()1(
2
1
kvkxkxkxkgBestrandc
kxkpBestrandckvkv
idd
idididid
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)1()()1( kvkxkx ididid
SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
PSO-CRS Algorithm
Step 4:
If the next possible position xid(k+1) resides within the If 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.
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SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
PSO-CRS Algorithm
Obstacle avoidance mechanism:
),,( nextverthoriz )points,,_,_,_,_(
),,(dircenterycenterxstartystartxarc
dir: CW – CCWpoints=4
nearest corner of the obstacle
The new position of the particle is set at the second point in the arc
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arc.
SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
PSO-CRS Algorithm
Step 5:
The pBestid with the best fitness for the entire swarm isiddetermined and the global best coordinate location, gBestd, isupdated with this pBestid.
Step 6:
Until convergence is reached, repeat steps.
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SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
Simulation Results
Search space: 20 by 20 units.
Vmax = 0.5 units.
# of robots = 10# of robots 10
# of targets = 1
# of obstacles=1
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SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
Simulation Results
Robots’ pathways to the target location R b t ’ th t th t t l ti
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Robots pathways to the target location. Two robots collide into the obstacle.
Robots’ pathways to the target location with obstacle avoidance mechanism.
SI in Swarm Robotics• Collective Robotic Search (CRS) [13]
Simulation Results
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
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Minimum 64 78
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
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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.
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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.569578, 2008.
[6] Mehtap Kose and Adnan Acan, “Knowledge Incorporation into ACO-Based Autonomous Mobile Robot 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
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[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 robot based on new free space structuring approach,” In Proceedings of International Workshop on Intelligent Robots and Systems. Japan, November, 1991, 563-567.[12] Yasushi Kambayashi, Yasuhiro Tsujimura, Hidemi Yamachi, Munehiro Takimoto and Hisashi Yamamoto, “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 42Hawaii 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”, WSEAS PSO 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.[16] Wu Xiaoling, Shu Lei, Yang Jie, Xu Hui, Jinsung Cho and Sungyoung Lee,“Swarm Based Sensor Deployment 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 positionless micro air vehicles for communication relay”, Swarm Intell (2008) 2: 167–188. 2008.
[19] Yan Meng, Olorundamilola Kazeem and Juan C. Muller, “A Hybrid ACO/PSO Control Algorithm for
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g y / gDistributed Swarm Robots,” Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS 2007).
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