The aerodynamic benefits of self-organization in bird flocks

Natural aggregation processes such as the familiar flocking of birds have been accurately modeled using a simple, decentralized controller. Variations on this “boid” controller typically involve three or more control laws, each with an associated control gain and sensor range. In this paper, the boid controller is fitted with an additional rule designed to produce aerodynamically-ecient formations, such as those exploited by migratory birds and hypothetical unmanned aerial vehicles. A simple genetic algorithm is then used to optimize the control parameters for minimum power consumption in a flock of simulated birds. This report focuses on the development and utility of the flocking simulator as a fitness function for the GA. Preliminary results indicate that average power consumption can be significantly reduced with the modified, optimized boid controller.

[1]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1998 .

[2]  Craig W. Reynolds.  An evolved, vision-based behavioral model of coordinated group motion , 1993 .

[3]  A. Filippone.  Heuristic optimization applied to an intrinsically difficult problem - Birds formation flight , 1996 .

[4]  Stewart W. Wilson,et al.  Not) Evolving Collective Behaviours in Synthetic Fish , 1996 .

[5]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[6]  Karl Sims,et al.  Evolving 3d morphology and behavior by competition , 1994 .

[7]  Craig W. Reynolds.  Competition, Coevolution and the Game of Tag , 1994 .

[8]  P. Lissaman,et al.  The facts of lift , 1996 .

[9]  Mark R. Anderson,et al.  FORMATION FLIGHT AS A COOPERATIVE GAME , 1998 .

[10]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[11]  Craig W. Reynolds.  Evolution of corridor following behavior in a noisy world , 1994 .

[12]  J. Anderson,et al.  Fundamentals of Aerodynamics , 1984 .

[13]  Craig W. Reynolds.  An Evolved, Vision-Based Model of Obstacle Avoidance Behavior , 1994 .

[14]  Jason L. Speyer,et al.  Decentralized controllers for unmanned aerial vehicle formation flight , 1996 .

[15]  Craig W. Reynolds.  Evolution of obstacle avoidance behavior: using noise to promote robust solutions , 1994 .

[16]  H. Weimerskirch,et al.  Energy saving in flight formation , 2001, Nature.

[17]  Stewart W. Wilson,et al.  Not) Evolving Collective Behaviours in Synthetic Fish , 1996 .

[18]  B. Crowther,et al.  Flocking of autonomous unmanned air vehicles , 2003, The Aeronautical Journal (1968).

[19]  Peter J. Angeline,et al.  Competitive Environments Evolve Better Solutions for Complex Tasks , 1993, ICGA.

[20]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[21]  Cutts,et al.  ENERGY SAVINGS IN FORMATION FLIGHT OF PINK-FOOTED GEESE , 1994, The Journal of experimental biology.