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Bio-Inspired Algorithms in Robotics

Bio-Inspired Algorithms in Robotics. 2008 Fall Robotics Class Lecture Sang Woo Lee. Table of Content. Biologically Inspired Algorithm Swarm Intelligence Evolutionary computation Application in Robotics Trail-Laying Robots for Robust Terrain Coverage

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Bio-Inspired Algorithms in Robotics

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  1. Bio-Inspired Algorithmsin Robotics 2008 Fall Robotics Class Lecture Sang Woo Lee

  2. Table of Content • Biologically Inspired Algorithm • Swarm Intelligence • Evolutionary computation • Application in Robotics • Trail-Laying Robots for Robust Terrain Coverage • Dynamic Redistribution of a Swarm of Robots • Biomimetic Visual Sensing for flight control

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

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

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

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

  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. 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

  12. Terrain-Covering Ant Robots • Pebbles III robot • 6 infrared IR proximeter • Front, front-left, front-right, left, right, and rear • Bump sensor, 2 motors • Lay trails – Black pen to track trail (C) • 8 Trail sensors(A, B) • Each side 4 sensors

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

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

  15. 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

  16. 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

  17. Experiment Result

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

  19. 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

  20. SimulationResult

  21. Video • Ant robot video

  22. 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

  23. 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

  24. Ant house hunting

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

  26. 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

  27. Simple system model • Property • Stability • Convergence • To a unique stable equilibrium point • Proved analystically

  28. Result

  29. Result

  30. Result

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

  32. 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

  33. System with quorum sensing

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

  35. System with quorum sensing

  36. Bio-inspired visual sensing for flight control • Bio-inspired sensors and algorithms • UAV • Insects use Optic flow • Perception of depth • Large compound eyes • Perception of the horizon • Ocelli optical sensor

  37. Ocelli

  38. UAV • Unmmaned Aerial Vehicle • Aircraft with no pilot • Remotely controlled • Fly autonomously • Usage – Dangerous situations • Military Surveillance • Civil Application • Optic flow • Used for autonomous navigation • Complex Environment

  39. Conventional UAV • Conventional Aircraft Sensors • Inertial Measurement Unit • Gyro – angular acceleration • Accelerometer – linear acceleration • Global Positioning System • Pressure sensor • Radar for range finding • Suitable for large airplane • Appliable even for microsize UAV(~15cm)

  40. Conventional UAV • Weakness • Electronic jamming • Low flight altitude • Several meters • Complex environment • Need to know all geometric information

  41. Optic Flow • Movement of texture • Resulting from the insect’s motion • Same as Image velocity • Flight Altitude • Observed by downward direction • Low when fast optic flow • Obstacle detection • Expansion – divergence in forward direction • Close when rapid optic flow

  42. FOE(Focus Of Expansion) • Origin of optic flow • Indicate direction of heading • Inside of rapidly expanding region • Imminent collision • Outside of rapidly expanding region • Near obstacle • No collision

  43. Optic Flow and FOE

  44. Optic flow • Top view of UAV • OF = – ω + (v/d) sin θ • ω – due to self rotation • Right term • Due to translation relative to the obstacle

  45. Optic flow • Θ=0 • Move toward obstacle • Only due to self rotation • Trade off of looking forward • Quick detection of a bump or obstacle • Reduces the magnitude of the optic flow due to the obstacle • Can detect self-rotation • Can replace gyros

  46. Optic flow • Insect behaviors using optic flow • Centering Response • Landing Strategy • Saccade response • Hovering Strategy • Clutter response • Forward focus of expansion strategy • Fixation strategy • Forward collision response

  47. Insect strategy • Centering Response • Equalize the optic flow on the left and right sides • Enable to fly center of a tunnel • Landing Strategy • Keep the optic flow on the landing surface constant • Keep the forward speed proportional to the vertical speed

  48. Insect strategy • Saccade response • Turning away from regions of high optic flow • Avoid collisions with large obstacles • Hovering Strategy • Zero optic flow everywhere • Useful for docking and for flying in formation

  49. Insect strategy • Clutter response • Maintain average global optic flow constant • To regulate flight speed at a safe level • Useful for dense obstacle environment • Forward focus of expansion strategy • Holding optic flow in forward direction at zero • To maintain a straight-ahead course • Useful when winds cause sideslip

  50. Insect strategy • Fixation strategy • Fixating objects in the forward direction • Minimizing lateral optic flow in the downward field • To maintain a straight-ahead course

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