Rule-based guidance for flight vehicle flocking

Abstract Within nature, mobile organisms often exhibit a form of emergent behaviour known as flocking. Flocking is adaptive because, for a given population density, the information processing required for collision avoidance is minimized. Within human activities, the problem of increasing civil air traffic is leading to consideration of decentralized traffic management methodologies, such as free flight. On the other hand, for military flight vehicles, safe operation of flight vehicles at high air space densities is seen as an opportunity. The present paper considers the use of guidance based on flocking rules to support the management of flight vehicles in situations of high airspace density. Three elemental flocking schemes are identified based on guidance tasks and a simple analytical framework is developed that allows insight into the fundamental flocking behaviour for all three schemes. Flight vehicles are modelled as point masses moving with constant speed in the horizontal plane. A non-dimensional approach is used throughout to provide generality. The proposed flocking algorithm is implemented within Matlab and numerical simulation results are used to validate analytical predictions of steady state flock size and entropy. Results show that a simple parameter based on the ratio of rule weights is sufficient to predict flock behaviour.

[1]  I. Y. Burdun,et al.  AI knowledge model for self-organizing conflict prevention/resolution in close free-flight air space , 1999, 1999 IEEE Aerospace Conference. Proceedings (Cat. No.99TH8403).

[2]  William Crowther Flocking of unmanned air vehicles , 2002 .

[3]  Nigel M. Allinson,et al.  Relating Organisational Structure to Performance: An Initial Focus on Centralisation , 2002 .

[4]  W. Andy Wright,et al.  A Measure of Emergence in an Adapting, Multi-Agent Context , 2000 .

[5]  S. Gueron,et al.  The Dynamics of Herds: From Individuals to Aggregations , 1996 .

[6]  William Blake,et al.  Design, performance and modeling considerations for close formation flight , 1998 .

[7]  Heinz Erzberger,et al.  Conflict Probability Estimation for Free Flight , 1997 .

[8]  John David Anderson,et al.  Introduction to Flight , 1985 .

[9]  Moriyuki Mizumachi,et al.  Aircraft collision avoidance with potential gradient—ground-based avoidance for horizontal maneuvers , 1995 .

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

[11]  T. Vicsek,et al.  Collective motion of organisms in three dimensions , 1999, physics/9902021.

[12]  Hayakawa,et al.  Collective motion in a system of motile elements. , 1996, Physical review letters.

[13]  P. Wang,et al.  Coordination and control of multiple microspacecraft moving in formation , 1996 .

[14]  J Byrne Is it time to give airliners the freedom of the skies , 2002 .

[15]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[16]  K. Zeghal A REVIEW OF DIFFERENT APPROACHES BASED ON FORCE FIELDS FOR AIRBORNE CONFLICT RESOLUTION , 1998 .

[17]  W. Potts The chorus-line hypothesis of manoeuvre coordination in avian flocks , 1984, Nature.

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

[19]  E. Feron,et al.  Real-time motion planning for agile autonomous vehicles , 2000, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[20]  Narendra Ahuja,et al.  A potential field approach to path planning , 1992, IEEE Trans. Robotics Autom..

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

[22]  J. Toner,et al.  Flocks, herds, and schools: A quantitative theory of flocking , 1998, cond-mat/9804180.

[23]  M. Pachter,et al.  Automatic formation flight control , 1992 .

[24]  B L Partridge,et al.  The structure and function of fish schools. , 1982, Scientific American.