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2007 Fall Comp790-058 Lecture Sang Woo Lee 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 Simulate biological phenomena or model Working algorithm in nature Proven its efficiency and robustness by natural selection 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 Robotic Multi-Robot Motion Planning Self-configuration Network Distributed autonomous system Routing algorithm Social Organization Traffic control Urban planning Computer Immunology Boids in Nick’s lecture Well known flocking algorithm Flocking Separation Alignment Cohesion Machine Learning in Dave’s lecture Neural Network Supervised learning Method Population of simple agents Decentralized Self-Organized No or local communication Example Ant/Bee colonies Bird flocking Fish schooling Meta-heuristic Optimization Inspired from the behavior of ant colonies Shortest paths between the nest and a food source 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 Solved problems Traveling Salesman Problem Quadratic Assignment Problem Job Shop Scheduling Vehicle and Network Routing Dussutour, A., Fourcassié, V., Helbing, D. & Deneubourg, J. L. Optimal traffic organ ization in ants under crowded conditions. Nature 428, 70-73 (2004) Research on ant path selection in bottleneck situation Maximizing traffic volume 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 J. Svennebring and S. Koenig, “Traillaying 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 Pebbles III robot 6 infrared proximeter Bump sensor, 2 motors Lay trails – Black pen to track trail 8 Trail sensor Node Counting Robot repeated enter cells Counting by markers in cell Move to smallest number No communication Very limited sensing Very limited computing power Marking current cell Sensing markers of neighbor cells 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 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 Work well with Uneven quality trail Move another location Removing patches of trail 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 A. Halasz, M. Ani Hsieh, S. Berman, V. Ku mar. Dynamic Redistribution of a Swarm of Robots Among Multiple Sites, 2007 IE EE/RSJ International Conference on Intelli gent Robots and Systems. Inspired by 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 Collectively distributes itself to multiple sites Predefined proportion No inter-agent communication Similar to task/resource allocation 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 Property Stability Convergence To a unique stable equilibrium point Proved analistically Transition in equilibrium state Fast transition makes more idle trips Extension Inject Quorum sensing Fast converge, less idle transition Adjacent sites communication Quorum information instantly available Transition rate switch Above quorum to below quorum Set to maximum transition rate Stable Converges asymptotically Problem Increasing quorum increase convergence speed Too big quorum make system stuck by high transition rates 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 Evolution Combination Alteration Selection Increases the proportion of better solutions in the population Better one survives! 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 Fish’s schooling behavior Use Aoki’s model Assuming 2-D world Movement Speed and Direction Four basic behavior patterns Repulsion behavior Move with a high parallel orientation Biosocial attraction Searching behavior Reference individual Nearer one selected with greater probability Direction determined by Previous direction Four basic behavior patterns Wobbling with normal distribution Speed Gamma distribution 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 Chase detected prey Random search Predator's sensory field k times larger Distribution of predator's speed n times faster The artificial ecology BL – body length of individual Size : 40BL * 40BL N prey, 1 predator If prey < N Create next generation prey Genetic algorithm Gene of individual Weight of urgent mode pattern Each of 10 bit Selection Surviving time Evolution Crossover each weight region of two parent 5% mutation for each bit Parent selection probability Proposition to surviving time Average Proposition of 4 weight B(attract) becomes lower D(away) becomes higher A(parallel) becomes higher Evolve for schooling More for evading Average Proposition of 4 weight against n (predator’s speed) More schooling when lower risk More evading when higher risk Scattered evasion is more efficient with high risk If predator is too fast, no strategy survives Polarization Average angle deviation Bio-inspired algorithms has many application Simple algorithm becomes emergent logic Useful for too complex problems Very useful for swarm of robot control Simple computation Decentralized and self-organized Need no or local communication Useful to establish group behavior de Castro, Leandro N. Recent Developments in Biologically Inspire d Computing, Hershey, PA, USA: Idea Group Publishing, 2004. p vi i. Dussutour, A., Fourcassié, V., Helbing, D. & Deneubourg, J. L. Opti mal traffic organization in ants under crowded conditions. Nature 428, 70-73 (2004) 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 vo l.1 A. Halasz, M. Ani Hsieh, S. Berman, V. Kumar. Dynamic Redistributi on of a Swarm of Robots Among Multiple Sites, 2007 IEEE/RSJ Int ernational Conference on Intelligent Robots and Systems. 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.
