Download - Academy Publisher

ISBN 978-952-5726-09-1 (Print)
Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10)
Jinggangshan, P. R. China, 2-4, April. 2010, pp. 085-088
Swarm Dynamics Behavior Analysis and
Coordinated Control of Limited-Range Perceived
Agents
Zhibin Xue 1*,3 , and Jianchao Zeng 2
1 Department of chemical machinery, Chemical engineering college, Qinghai University, Xining, China
Email: zbxue_jack@163.com
2 Complex System & Computer Intelligence Laboratory; Taiyuan University of Science & Technology, Taiyuan, China
Email: zengjianchao@263.net
3 College of Electric & Information Engineering, Lanzhou University of Technology, Lanzhou, China
Abstract—Swarming behavior is ubiquitous in nature and
society. It is of both theoretical and practical importance to
investigate the underlying principles and mechanisms of
coordination and cooperation emerging in swarms. In this
paper we propose a simple isotropic range limited-perceive
agents dynamic model to study collective and formation
behavior of a group of mobile autonomous agents
interacting through a long range attraction and short range
repulsion function. It is shown that the individuals (agents)
in the swarm during the course of coordinative motion can
realize the local collision-free stabilization of constituting a
particular predefined geometrical configuration. Numerical
simulations are also worked out to illustrate the analytical
results.
Index Terms—swarm dynamics, coordinated control,
isotropic, limited-range perceive, multi-agent, collision-free,
formation control, numerical simulations
I. INTRODUCTION
Swarming behavior has been observed in nature. For
example, bees, ants and birds often work together in
groups for viability[1]. It is known that such cooperative
behavior has certain advantages, for example, predator
avoidance, foraging success and so on.
The general understanding in biology is that the
swarming behavior is a result of an interplay between a
long range attraction and a short range repulsion between
the individuals [1]. Understanding the cooperative and
operational principles of such systems may provide useful
ideas for modeling and exploring the collective dynamics
of swarming behavior in bionics for using in engineering
applications, such as the coordinated control of multiagent
systems.
However, most available results in the literature are on
the isotropic global perceive swarming model, convincing
results on the isotropic local perceive swarming model
are relatively few. Based on the analysis of various
biological swarms of dynamic aggregation mechanism,
an isotropic limited-range perceived swarming dynamic
model is proposed in this paper. Meanwhile, the chief
research objective of the paper is to use the limited range
perceive model to solve the formation control question of
multi-agent systems to constitute various predefined
geometrical configuration formations pattern on multiagent
systems in an n-dimensional Euclidean space by
using artificial potential functions and Newton’s iteration
method. Dynamic change of the environment, local
observation and nonlinear characteristics are ubiquitous
phenomena in nature, but the study is very difficult and it
has profound engineering significance. So this article is
able to provide some results on this topic.
II. SWARM DYNAMICAL MODELS
Gazi and Passino[2] proposed an isotropic global
perceive swarm model with a simple attraction-repulsion
function specifying the inter-individual interactions and
showed that the model can exhibit the basic features of
aggregation, cohesion and complete stability. However,
the attraction-repulsion functions considered in this study
could not avoid collisions since they are not unbounded
for infinitesimally small arguments.
g(y)
5
4
3
2
1
0
-1
-2
-3
-4
Manuscript received January 28, 2010; revised February 28, 2010;
accepted March 16, 2010.
*Corresponding author (email: zbxue_jack@163.com).
This work was supported by the National Natural Science
Foundation of China (Grant No. 60975074).
-5
-5 -4 -3 -2 -1 0 1 2 3 4 5
y
Figure 1. The attraction/repulsion function g(⋅ ) in Ref. [2]
© 2010 ACADEMY PUBLISHER
AP-PROC-CS-10CN006
85