Swarm Intelligence and Bio-Inspired Computing

1. Swarm Intelligence and Bio-Inspired Computing 2007 Fall Comp790-058 Lecture Sang Woo Lee

2. Table of Content • What is Biologically Inspired Algorithm? • Swarm Intelligence • Evolutionary Computation • Application in Motion Planning • Trail-Laying Robots for Robust Terrain Coverage • Dynamic Redistribution of a Swarm of Robots • Evolving Schooling Behaviors to Escape from Predator

3. What is Biologically Inspired Algorithm? • Simulate biological phenomena or model • Working algorithm in nature • Proven its efficiency and robustness by natural selection

4. Motivation • Dealing too complex problems • Incapable to solve by human proposed solution • Absence of complete mathematical model • Existing of similar problem in nature • Adaptation • Self-organization • Communication • Optimization

5. Application • Robotic • Multi-Robot Motion Planning • Self-configuration • Network • Distributed autonomous system • Routing algorithm • Social Organization • Traffic control • Urban planning • Computer Immunology

6. Application in previous lecture • Boids in Nick’s lecture • Well known flocking algorithm • Flocking • Separation • Alignment • Cohesion • Machine Learning in Dave’s lecture • Neural Network • Supervised learning Method

7. Swarm Intelligence • Population of simple agents • Decentralized • Self-Organized • No or local communication • Example • Ant/Bee colonies • Bird flocking • Fish schooling

8. Ant Colony Optimization • Meta-heuristic Optimization • Inspired from the behavior of ant colonies • Shortest paths between the nest and a food source

9. Ant Path Algorithm • Evaporating pheromone trail • Probabilistic path decision • Biased by the amount of pheromone • Converge to shortest path • Ant trips on shorter path returns quicker • Longer path lose pheromone by evaporating

10. Ant Path Algorithm

11. Ant Colony Optimization • Solved problems • Traveling Salesman Problem • Quadratic Assignment Problem • Job Shop Scheduling • Vehicle and Network Routing

12. Ant Traffic Organization • Dussutour, A., Fourcassié, V., Helbing, D. & Deneubourg, J. L. Optimal traffic organization in ants under crowded conditions. Nature 428, 70-73 (2004) • Research on ant path selection in bottle-neck situation • Maximizing traffic volume

13. Ant Traffic Organization

14. Ant Traffic Organization • Symmetrical traffic in narrow path • Threshold width between 10.0 and 6.0mm • Pushed Ant is redirected to other path • Symmetry restored before the maximum flow • Benefits of using a single trail • Condensed trail - Better orientation guidance and stronger stimulus • High-density - Good information exchange • Optimize the rate of food return • Proved by analytical model and experiment

15. Ant Traffic Organization

16. Terrain-Covering Ant Robots • J. Svennebring and S. Koenig, “Trail-laying robots. for robust terrain coverage,”, Proc. of IEEE International Conference on Robotics and Automation 2003, Volume: 1,  On page(s): 75- 82 vol.1 • Inspired by Ant forage • Exploration & Coverage

17. Terrain-Covering Ant Robots • Pebbles III robot • 6 infrared proximeter • Bump sensor, 2 motors • Lay trails – Black pen to track trail • 8 Trail sensor

18. Theoretical Foundation • Node Counting • Robot repeated enter cells • Counting by markers in cell • Move to smallest number

19. Ant Robots • No communication • Very limited sensing • Very limited computing power • Marking current cell • Sensing markers of neighbor cells

20. Restriction • Assumptions on theoretical foundation • Move discrete step • Mark cell uniformly • No noise in sensor • By the way, it works even • Uneven quality trail • Some missing trail • Pushed to other location

21. Ant Robot Behavior • Obstacle Avoidance Behavior • Inversely proportional to the distance • Weight for each direction sensor • Trail Avoidance Behavior • Fixed length • Trail sensor with recent past information • Weight Balancing of two behavior • Need to well balanced

22. Experiment Result

23. Experiment Result • Work well with • Uneven quality trail • Move another location • Removing patches of trail

24. Simulation • Faster than random walk • Until some threshold • Too many trails result large coverage time • With no cleaning of Trail • Coverage time grow steeply • With cleaning of Trail • Same as ant pheromone • Works good with many coverage number

25. SimulationResult

26. Video

27. Dynamic Redistribution of a Swarm of Robots • A. Halasz, M. Ani Hsieh, S. Berman, V. Kumar. Dynamic Redistribution of a Swarm of Robots Among Multiple Sites, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. • Inspired by Ant house-hunting

28. Ant house hunting • Probability of initiating recruitment depends on the site’s quality • Superior site scout has shorter latency to recruit • Recruitment type • Summon fellow by tandem run • Passive majority by transport • Transport recruitment of new site triggered by population (Over the quorum) • Recruitment speed difference amplified by quorum requirement

29. Ant house hunting

30. Dynamic Redistribution of a Swarm of Robots • Collectively distributes itself to multiple sites • Predefined proportion • No inter-agent communication • Similar to task/resource allocation

31. Simple system model • Scalable • Using fraction rather than agent number • Graph G • Strongly connected graph • Edge • Transition rate Kij • Transition time Tij • Maximum transition capacity • All agents know Graph G

32. Simple system model • Property • Stability • Convergence • To a unique stable equilibrium point • Proved analistically

33. Result

34. Result

35. Result

36. Problem in first system • Transition in equilibrium state • Fast transition makes more idle trips • Extension • Inject Quorum sensing • Fast converge, less idle transition

37. System with quorum sensing • Adjacent sites communication • Quorum information instantly available • Transition rate switch • Above quorum to below quorum • Set to maximum transition rate • Stable • Converges asymptotically

38. System with quorum sensing

39. System with quorum sensing • Problem • Increasing quorum increase convergence speed • Too big quorum make system stuck by high transition rates

40. System with quorum sensing

41. Evolutionary Computation • Inspired from the natural processes that involve evolution • Genetic algorithm • Evolution strategies, evolutionary programming, genetic programming • Use a population of competing candidate solutions • Reproduce and evolve themselves

42. Evolutionary Computation • Evolution • Combination • Alteration • Selection • Increases the proportion of better solutions in the population • Better one survives!

43. Evolutionary Computation

44. Evolving Schooling Behaviors • T. Oboshi, S. Kato, A. Mutoh and H. Itoh, Collective or Scattering: Evolving Schooling Behaviors to Escape from Predator, edited by R. Standish, M. A. Bedau and Abbass, H. A., Artificial Life VIII (MIT Press, Cambridge, MA, 2002), p. 386. • Evolving schooling behavior by Genetic Algorithm

45. The basic behavior model • Fish’s schooling behavior • Use Aoki’s model • Assuming 2-D world • Movement • Speed and Direction

46. The basic behavior model • Four basic behavior patterns • Repulsion behavior • Move with a high parallel orientation • Biosocial attraction • Searching behavior • Reference individual • Nearer one selected with greater probability

47. The basic behavior model

48. The basic behavior model • Direction determined by • Previous direction • Four basic behavior patterns • Wobbling with normal distribution • Speed • Gamma distribution

49. Extension • Considering predator‘s existence • Urgent mode • Sensing predator approaching • Direction determined by • Lerp with 4 variables • Parallel to neighbor • Attracted to neighbor • Averting from predator • Away from predator

50. Extension