An indexed K-D tree for neighborhood generation in swarm robotics simulation

Abstract

In this paper, an indexed K-D tree is proposed to solve the problem of neighborhoods generation in swarm robotic simulation. The problem of neighborhoods generation for both robots and obstacles can be converted as a set of range searches to locate the robots within the sensing areas. The indexed K-D tree provides an indexed structure for a quick search for the robots' neighbors in the tree generated by robots' positions, which is the most time consuming operation in the process of neighborhood generation. The structure takes full advantage of the fact that the matrix generated by robots' neighborhoods is symmetric and avoids duplicated search operations to a large extent. Simulation results demonstrate that the indexed K-D tree is significantly quicker than normal K-D tree and other methods for neighborhood generation when the population is larger than 10. © 2013 Springer-Verlag Berlin Heidelberg.