Managing group behaviors in swarm systems by associations

In this paper, we present a framework for managing group behaviors in multi-agent swarm systems. The framework explores the benefits by integrating stream function based path planning methods with novel artificial potential based flocking methods to realize complex swarming behaviors. A key development is the introduction of a set of flocking by dynamic association (DA) algorithms that effectively deal with a host of swarming issues such as flexible and agile formation, obstacle avoidance and inter-agent collision. In particular, the DA algorithms employ a so-called singular association (SA) rule for inter-agent interaction, i.e., each agent associates itself with only one other judiciously chosen agent. The resulting algorithms enjoy two important interrelated benefits. First, the SA rule greatly simplifies the interactions in swarm systems thus it is more readily to pursue rigorous analysis of such systems, some of which are included in this paper. Secondly, the simplicity of the SA rule is advantageous for practical implementations. Extensive simulation as well as preliminary experimental results are presented to illustrate the viability of the proposed framework