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Presentation Outline
 Multiple Agents – An Introduction
 How to build an ant robot
 Self-Organization of Multiple Agents
 Collective Target Tracking
 Towards real-time implementation
 Conclusion
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Multiple Agents
What are Agents?
Agents are man-made entities which can perform a
particular task.
Why Multiple Agents?
 Some tasks may be inherently too complex or
impossible for a single agent to perform
 Several simple agents may be easier to design/cost
efficient than a single complex agent.
 Flexibility and Robustness
 Comparable to pack hunters
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Multiple Agents
 More generally called as swarms of agents.
 Swarm Intelligence – Evolved from studies of insect
societies
Ex. Ant foraging activity is used to solve problems
in big communication networks
 Complex collective behavior from the interactions of
simple individuals
 Wide-range application ex. material transportation,
planetary missions, oceanographic sampling
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Multiple Agents
Future of Multiple Agents – Nanoswarms
 Swarm intelligence combined with Nanotechnology
 Also called as Molecular Robotics
 NSF has recently funded a research at USC (University of
Southern California) to build nanoswarms for controlling water
pollution.
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Multiple Agents
Self-Organization of Multiple Agents
 Centralized Vs Decentralized Approach
 Disadvantages of Leader – Follower strategy
1. Fragile
2. Extensive communication
3. May not be feasible
4. Cost considerations
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Multiple Agents
Biological Inspiration
 Ants are fascinating social insects. They are only
capable of short-range interactions, yet communities
of ants are able to solve complex problems efficiently
and reliably. Ants have therefore become a source of
algorithmic ideas for distributed systems where a robot
(or a computer) is the "individual" and a swarm of
robots (or the network) plays the role of the "colony".
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Multiple Agents
Proposed technique
Decentralized self-organization of Multiple Agents with
minimal hardware using Information Theoretic
Interactions
Goals
1. Distribute a group of robots uniformly inside a region
2. Move the collective towards a target
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How to build an ant-robot
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How to build an ant-robot
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How to build an ant-robot
 We can add any kind of ant head we like.
 Note: The ant needs quite a lot of power to lift itself up and
down as it walks. We need to use fresh batteries in our
motor's power box. If you reverse the direction the motor
spins (by using the switch on the power box), the ant will
walk forwards and backwards.
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Self-Organization Algorithm
Goal 1: Spreading the robots uniformly over a region
Spreading of agents – Literature Survey
 Centralized control
 Nearest neighbor(s) repulsion
 Detecting distance from the farthest agent
 Assuming a central beacon
Simplest region – A circle is chosen as the first step
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Self-Organization Algorithm
 Calculating the controlling force needed at the boundary of the
circle is a complex optimization problem.
 Not equivalent to the packing of identical circles inside a circle
problem
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Self-Organization Algorithm
 The controlling force needed at the boundary of the circle is
calculated empirically.
1.5107 2
/r
 Threshold γ  0.21128  A  N
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Self-Organization Algorithm
 The potential Vi for a particular robot is the sum of the received
signal amplitudes Aik from all other robots.
N
Vi 

N
Aik 
k 1
 d ik2  
1
k 1
 Vi is then compared to the threshold  to specify the sign of the
Information force (IF) given by
Vi
Fi  arctan(  Vi )
p i
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Self-Organization Algorithm
Simulation Video for spreading and obstacle avoidance
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Self-Organization Algorithm
Extending the algorithm to any region bounded by
piecewise-linear boundary
Jordan Curve Theorem
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Self-Organization Algorithm
Simulation Video for spreading over a star-shaped region
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Collective Target Tracking
Goal 2: Move the collective towards a target
 Before detecting the ant food in the middle of ant robots.
 After detecting the ant food all ant robots gathered near it.
1
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Collective Target Tracking
Four situations for this case
 Situation 1: All the robots know the position of the target and their
own positions. Then, it’s a trivial problem.
 Situation 2: Only one leader robot knows the position of the robot
and it leads the group towards the target.
Disadvantage – Centralized Control
Possible remedies – Have many leaders, Rotate the leader
 Situation 3: The target issues a beacon signal. The group moves
towards it.
 Situation 4: The robots does not know the position of the robot and
decentralized approach is needed. Best assumption in military
applications.
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Collective Target Tracking
Continuing with Situation 4…
Two base stations are used to transmit direction information to all
robots by giving separate IF to all the robots.
 Each base station is assumed to have a simple radar.
 Radar decides whether the center of the circle is to the left or right
of the target Line of Sight.
 The direction of the IF is rotated accordingly. IF from the base
stations is same for all the robots.
 Total IF experienced by the robots is the sum of IFs between the
robots and due to the base stations.
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Collective Target Tracking
Rotation of IF from the base stations
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Collective Target Tracking
Simulation Video for moving target tracking
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Towards Real-time Implementation
Various parameters used in the current simulation:
 Number of robots = 6
 Size of the robot = 16cm  9cm
 Differential wheels
Axle length = 16cm ; Wheel radius = 5cm
Min speed = 1 rad/s ; Max speed = 10 rad/s
Acceleration = 10 rad/s^2
 Radius of the circle = 3m
 Time taken to spread ~ 16 s
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Towards Real-time Implementation
Simulation in Ant Robots
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Conclusion
 Use of Information theoretic interactions to selforganization of multiple agents gives a good and
simple solution for spreading of the robots and moving
them towards a target.
 The algorithm can be suitably changed for the intended
application.
 Collision Avoidance is naturally built-in during
spreading and tracking.
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Conclusion
 The decentralized Self-Organization Algorithm is costeffective since
1. Each robot needs to have only a simple transmitter
and receiver.
2. The circuit complexity of the receiver does not
increase with increasing the number of robots.
3.
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It is possible to make each robot not know its own
absolute position as well as the position of the
other robots.
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