Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
1
International Graduate
School of Dynamic
Intelligent Systems
Coordinative Behavior in
Evolutionary Multi
-
agent System by
Genetic Algorithm
(T. Shibata and T. Fukuda)
Chuan
-
Kang Ting
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
2
International Graduate
School of Dynamic
Intelligent Systems
Path Planning for Multiple Robots
1
2
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6
7
8
9
10
2
1
4
3
6
5
8
7
10
9
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
3
International Graduate
School of Dynamic
Intelligent Systems
Selfish
-
planning
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9
10
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
4
International Graduate
School of Dynamic
Intelligent Systems
Collision & Deadlock
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10
Selfish
Coordination
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
5
International Graduate
School of Dynamic
Intelligent Systems
Setting
•
Subject:
path planning of multiple robots
•
Purpose:
optimize the path of each robot to avoid
collisions, reduce waiting time, and decrease tour
length
•
Conditions:
no global communication between
robots but local sense to surrounding
•
Method:
apply genetic algorithm to optimize
motion planning of each robot
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
6
International Graduate
School of Dynamic
Intelligent Systems
Evolution in Nature
Chromosomes
Mutation
Crossover
Mating
Survival of the Fittest
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
7
International Graduate
School of Dynamic
Intelligent Systems
Genetic Algorithms
Genetic Algorithm
()
{
t := 0;
init_population
P(t);
evaluate
P(t);
do {
P’(t) :=
select_parents
P(t);
crossover
P’(t);
mutate
P’(t);
evaluate
P’(t);
P(t+1) :=
survive
P(t), P’(t)
t := t + 1;
} while not terminated;
}
Initialization
Mutation
Evaluate
& Survive
Crossover
Selection
Terminated?
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
8
International Graduate
School of Dynamic
Intelligent Systems
Chromosome
-
background
•
MAKLINK
–
Construct the available free space between obstacles
–
Based on free links: whose two ends are either corners of polygonal
obstacles or a point on the working
-
space boundary
–
Every node can allow only one robot at the same time
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
9
International Graduate
School of Dynamic
Intelligent Systems
Chromosome
-
representation
•
Chromosome
–
An encoded expression of
potential solutions. (usually
in the form of string)
•
Path representation
–
Encode the order of visiting
points as a string
•
Example
–
0
-
1
-
2
-
6
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9
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11
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15
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22
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25
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26
–
0
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4
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8
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10
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15
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20
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23
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26
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
10
International Graduate
School of Dynamic
Intelligent Systems
Chromosome
-
population
•
Population
–
a set of chromosomes
•
Chromosomes are generated randomly. i.e. the
population contains a set of random solutions
•
Each robot is equipped with one population
0
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1
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2
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6
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9
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11
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15
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0
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Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
11
International Graduate
School of Dynamic
Intelligent Systems
Operator
-
Crossover
•
Exchange information from two selected
chromosomes
–
Crossover point: the first identical node in both parents
–
Example:
Parent1:
0
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1
-
2
-
6
-
9
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11
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15
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17
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22
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25
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26
Parent2:
0
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4
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8
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10
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15
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20
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23
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26
Child1:
0
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1
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2
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6
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9
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11
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15
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20
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23
-
26
Child2:
0
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4
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8
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10
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15
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17
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22
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25
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26
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
12
International Graduate
School of Dynamic
Intelligent Systems
Operator
-
Mutation
•
Mutation rate is low (for
escaping local optimum)
1.
Randomly selects one gene
(node) to mutate
2.
Its following nodes are
picked randomly from
sequentially connected
nodes
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
13
International Graduate
School of Dynamic
Intelligent Systems
Evaluation
-
Fitness
•
Fitness = Tour Length + Waiting Time
(Static)
(Dynamic)
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
14
International Graduate
School of Dynamic
Intelligent Systems
Evolution Flowchart
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
15
International Graduate
School of Dynamic
Intelligent Systems
Experimental Results (1)
•
Optimize the path for single robot (selfish
-
planning)
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
16
International Graduate
School of Dynamic
Intelligent Systems
Experimental Results (2)
•
Fitness variation of multiple robots
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
17
International Graduate
School of Dynamic
Intelligent Systems
Experimental Results (3)
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
18
International Graduate
School of Dynamic
Intelligent Systems
Experimental Results (4)
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
19
International Graduate
School of Dynamic
Intelligent Systems
Summary
•
The paper proposes a strategy of coordinative
motion planning for multi
-
agent systems.
•
Without global communication, the robot uses
Genetic Algorithm (GA) to search the feasible
solutions, which consider the tour distance and
waiting time at the same time.
•
Simulation results demonstrate this strategy can
avoid collisions and achieve coordination of
multiple robots.
Coordinative Behavior in Evolutionary Multi
-
agent System
by Genetic Algorithm
Chuan
-
Kang Ting
–
Page:
20
International Graduate
School of Dynamic
Intelligent Systems
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