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Multi-Robot Systems<br />CSCI 7000-006<br />Wednesday, September 2, 2009<br />NikolausCorrell<br />
So far<br />Reactive algorithms<br />Lowest-level control for individual autonomous robots<br />Coordination mechanism for...
Today<br />More advanced reactive algorithms<br />Message propagation and information gradients<br />Threshold-based algor...
Information Gradients<br />Example: find hot-spot in the environment<br />Given<br />Robot swarm<br />Every robot can samp...
Tracking down a hot spot<br />Need local range and bearing or global position<br />Algorithm:<br />Sample temperature<br /...
Information Gradients: Static and Mobile Systems<br />Similar to artificial potential fields<br />But: robots/embedded sys...
Information Gradients: Programming Language<br />MIT Proto (Scheme dialect)<br />Functional language<br />“Program” evalua...
Example: Deployment of Wifi Networks<br />Goal: Communication Infrastructure<br />Self-Deployment, Self-Repair<br />Applic...
Challenges<br />3m (GPS)<br />Positioning<br />Bearing<br />Unidirectional communication<br />Local-to-global: coding & al...
Algorithm<br />Only sensor: number of neighbors<br />Move randomly until local topology constraints are fulfilled<br />Con...
How to establish connection?<br />Gateway emits special message<br />Nodes forward gateway message<br />“Hop-count” to gat...
Proto Algorithm<br />Parameters<br />
System Architecture<br />1 Hz<br />IP Layer<br />IP Layer<br />IP Layer<br />IP Layer<br />IP Layer<br />IP Layer<br />IP ...
Topology vs. control parameters(centralized deployment)<br />( 1 &lt; neighbors &lt; 3)<br />( 2 &lt; neighbors &lt; 4)<br...
Area coverage vs. control parameters(centralized deployment)<br />( 2 &lt; neighbors &lt; 4)<br />( 1 &lt; neighbors &lt; ...
Indoor Experiment<br />9 robots<br />Central deployment<br />500m2 ~ 30min<br />
Taking advantage of Localization and Communication<br />Baseline: deployment algorithm<br />Algorithms<br />Move to last-k...
Threshold-based algorithms<br />React probabilistically to stimulus<br />Threshold determines likelihood<br />Different th...
Example 1: Task Allocation(Krieger & Billeter)<br />Goal: forage for “energy” in the environment<br />Maintain energy in t...
System architecture and algorithm<br />Nest maintains information on colony energy and broadcast this information<br />Rob...
Results<br />Krieger, M.J.B. & Billeter, J-B. (2000). The call of duty: Selforganised task allocation in a population of u...
Example 2: Aggregation<br />Goal: Aggregation (Monday)<br />Threshold-based task allocation<br />Workers estimate availabi...
Results<br />
Analysis<br />Homogenous controllers<br />Local perception of stimulus (heterogeneous team) leads to diversity<br />Fully ...
Optimizing the response threshold<br />Systematic search /parameter sweep<br />Performance metric is steadily increasing<b...
Summary<br />Reactive algorithms, information gradients and response-thresholds are powerful heuristics for generating com...
Upcoming <br />Friday: Message passing in ROS<br />Next week:<br />Monday: Labor Day<br />Wednesday: (multi-robot) Localiz...
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September 2, Reactive Algorithms II

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Multi-Robot Systems

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September 2, Reactive Algorithms II

  1. 1. Multi-Robot Systems<br />CSCI 7000-006<br />Wednesday, September 2, 2009<br />NikolausCorrell<br />
  2. 2. So far<br />Reactive algorithms<br />Lowest-level control for individual autonomous robots<br />Coordination mechanism for swarms of individually simple individuals<br />Coordination via local communication or by modifying the environment<br />
  3. 3. Today<br />More advanced reactive algorithms<br />Message propagation and information gradients<br />Threshold-based algorithms<br />
  4. 4. Information Gradients<br />Example: find hot-spot in the environment<br />Given<br />Robot swarm<br />Every robot can sample temperature<br />Robots can exchange info<br />Course question<br />Algorithm<br />What else do you need to know?<br />4<br />4<br />4<br />7<br />8<br />3<br />9<br />7<br />6<br />7<br />6<br />6<br />5<br />5<br />2<br />5<br />
  5. 5. Tracking down a hot spot<br />Need local range and bearing or global position<br />Algorithm:<br />Sample temperature<br />Broadcast temperature<br />Receive from neighbors<br />Broadcast direction to hottest neighbor<br />Other application: routingYouTube<br />Open E-puck Range & Bearing Miniaturized Board for Local Communication in Swarm Robotics. A. Gutierrez, A. Campo, M. Dorigo, J. Donate, F. Monasterio-Huelinand L. Magdalena. ICRA 2009.<br />
  6. 6. Information Gradients: Static and Mobile Systems<br />Similar to artificial potential fields<br />But: robots/embedded systems provide the force field<br />Multiple sources can emit competing gradients<br />http://borg.cc.gatech.edu/gnats<br />
  7. 7. Information Gradients: Programming Language<br />MIT Proto (Scheme dialect)<br />Functional language<br />“Program” evaluates into a motion vector<br />Commands<br />Integrate over neighborhood<br />Differentiate over neighborhood (gradient)<br />…<br />Key: every information used in the program is also broadcast<br />(def cluster-to (src)<br /> (* -1 (grad (distance-to src))))<br />(cluster-to (is-light))<br />http://groups.csail.mit.edu/stpg/proto.html<br />
  8. 8. Example: Deployment of Wifi Networks<br />Goal: Communication Infrastructure<br />Self-Deployment, Self-Repair<br />Applications<br />Natural disasters<br />Military<br />Environmental Monitoring<br />
  9. 9. Challenges<br />3m (GPS)<br />Positioning<br />Bearing<br />Unidirectional communication<br />Local-to-global: coding & algorithms<br />Locomotion<br />Its just me here!<br />Heeello!<br />Everybody maintain coverage!<br />
  10. 10. Algorithm<br />Only sensor: number of neighbors<br />Move randomly until local topology constraints are fulfilled<br />Connected to gateway<br />Minimum number of neighbors<br />Maximum number of neighbors<br />3<br />2<br />2<br />4<br />1<br />if ( 2 &lt; neighbors &lt; 4)<br /> move<br />else<br /> stop<br />
  11. 11. How to establish connection?<br />Gateway emits special message<br />Nodes forward gateway message<br />“Hop-count” to gateway<br />Count-to-infinity problem<br />Unconnected if number of hops larger than total number of nodes<br />2<br />1<br />0<br />2<br />3<br />0<br />3<br />4<br />0<br />
  12. 12. Proto Algorithm<br />Parameters<br />
  13. 13. System Architecture<br />1 Hz<br />IP Layer<br />IP Layer<br />IP Layer<br />IP Layer<br />IP Layer<br />IP Layer<br />IP Layer<br />OLSRd<br />(Optimized Link State Routing)<br />Link<br />Layer<br />Link<br />Layer<br />Link<br />Layer<br />Link<br />Layer<br />Link<br />Layer<br />Link<br />Layer<br />Atheros MIPS 180MHz<br />Mobile<br />Layer<br />Mobile<br />Layer<br />Mobile<br />Layer<br />Mobile<br />Layer<br />Mobile<br />Layer<br />Mobile<br />Layer<br />Link<br />Layer<br />Deployment Algorithm<br />PROTO<br />Mobile<br />Layer<br />iRobot Create<br />20 Hz<br />Differential Wheels Drive<br />
  14. 14. Topology vs. control parameters(centralized deployment)<br />( 1 &lt; neighbors &lt; 3)<br />( 2 &lt; neighbors &lt; 4)<br />( 3 &lt; neighbors &lt; 5)<br />
  15. 15. Area coverage vs. control parameters(centralized deployment)<br />( 2 &lt; neighbors &lt; 4)<br />( 1 &lt; neighbors &lt; 5)<br />( 1 &lt; neighbors &lt; 6)<br />
  16. 16. Indoor Experiment<br />9 robots<br />Central deployment<br />500m2 ~ 30min<br />
  17. 17. Taking advantage of Localization and Communication<br />Baseline: deployment algorithm<br />Algorithms<br />Move to last-known-good position<br />Move to last-known-good and disperse<br />Consensus and flocking<br />Course Question: Why would you need the consensus and flocking algorithm? Hint: local minima of the dispersion algorithm. <br />Videos: Anna Derbakova<br />
  18. 18. Threshold-based algorithms<br />React probabilistically to stimulus<br />Threshold determines likelihood<br />Different thresholds for competing stimuli<br />Biological inspiration: social insects<br />Probability to do task i<br />Stimulus intensity<br />Krieger, M.J.B. & Billeter, J-B. (2000). The call of duty: Selforganised task allocation in a population of up to twelve mobile robots Robotics Autonom. Sys. 30: 65-84.<br />
  19. 19. Example 1: Task Allocation(Krieger & Billeter)<br />Goal: forage for “energy” in the environment<br />Maintain energy in the “nest” on a certain level<br />Ant-like task allocation and recruitment in cooperative robots. M. Krieger, J. Billeter and L. Keller. Nature, vol 406, pp. 992-995, August 2006.<br />
  20. 20. System architecture and algorithm<br />Nest maintains information on colony energy and broadcast this information<br />Robots receive energy from the nest<br />Robots forage randomly for “food”, increasing nest energy<br />Robots forage only when nest energy falls below a certain threshold<br />Beacon provides visual cue for finding the nest<br />
  21. 21. Results<br />Krieger, M.J.B. & Billeter, J-B. (2000). The call of duty: Selforganised task allocation in a population of up to twelve mobile robots Robotics Autonom. Sys. 30: 65-84.<br />
  22. 22. Example 2: Aggregation<br />Goal: Aggregation (Monday)<br />Threshold-based task allocation<br />Workers estimate availability of work (stimulus)<br />Workers take a rest when there is little work<br />Self-regulation of swarm activity<br />
  23. 23. Results<br />
  24. 24. Analysis<br />Homogenous controllers<br />Local perception of stimulus (heterogeneous team) leads to diversity<br />Fully scalable: number of agents do not matter<br />Course question:<br />What happens if threshold is too low<br />What happens if threshold is too high<br />How find optimal threshold?<br />
  25. 25. Optimizing the response threshold<br />Systematic search /parameter sweep<br />Performance metric is steadily increasing<br />Comparing snap-shot of the experiment at T=10h<br />Too few workers<br />Too many workers<br />(clusters get destroyed)<br />10 robots, 20 seeds<br />
  26. 26. Summary<br />Reactive algorithms, information gradients and response-thresholds are powerful heuristics for generating complex behavior<br />Information gradients enabled by range and bearing hardware<br />Potential for extreme miniaturization and large numbers of agents<br />
  27. 27. Upcoming <br />Friday: Message passing in ROS<br />Next week:<br />Monday: Labor Day<br />Wednesday: (multi-robot) Localization<br />Friday: Lab, navigation<br />

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