4/22/20031/28

4/22/20031/28 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

4/22/20031/28 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

4/22/20031/28 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

4/22/20031/28 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.

4/22/20031/28 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

4/22/20031/28 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".

4/22/20031/28 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

4/22/20031/28 How to build an ant-robot

4/22/20031/28 How to build an ant-robot

4/22/20031/28 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.

4/22/20031/28 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

4/22/20031/28 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

4/22/20031/28 Self-Organization Algorithm  The controlling force needed at the boundary of the circle is calculated empirically.  Threshold

4/22/20031/28 Self-Organization Algorithm  The potential V i for a particular robot is the sum of the received signal amplitudes A ik from all other robots.  V i is then compared to the threshold  to specify the sign of the Information force (IF) given by

4/22/20031/28 Self-Organization Algorithm Simulation Video for spreading and obstacle avoidance

4/22/20031/28 Self-Organization Algorithm Extending the algorithm to any region bounded by piecewise-linear boundary Jordan Curve Theorem

4/22/20031/28 Self-Organization Algorithm Simulation Video for spreading over a star-shaped region

4/22/20031/28 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. 12

4/22/20031/28 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.

4/22/20031/28 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.

4/22/20031/28 Collective Target Tracking Rotation of IF from the base stations

4/22/20031/28 Collective Target Tracking Simulation Video for moving target tracking

4/22/20031/28 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

4/22/20031/28 Towards Real-time Implementation Simulation in Ant Robots

4/22/20031/28 Conclusion  Use of Information theoretic interactions to self- organization 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.

4/22/20031/28 Conclusion  The decentralized Self-Organization Algorithm is cost- effective 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. It is possible to make each robot not know its own absolute position as well as the position of the other robots.