Discrete analysis of obstacle clustering by distributed robots

In this paper, we discuss some phenomena of obstacle clustering by distributed autonomous robots, in the light of space-discretization (or cellular automata) approach. This work was motivated by Swiss Robots which collect scattered obstacles into some clusters without any global information nor intelligent concentrated controller. In order to evaluate these phenomena from quantitative and statistical points of view, we propose an analysis platform using discretized state space, i.e., a hexagonal cellular space where the robots' direction and velocity are discretized as well. We then introduce two types of local rule, sensor avoiding rule (which resembles the Swiss Robot's action) and push & turn rule and compare the results focusing on size of resulting clusters, transient/steady-state behaviors and density of obstacles and robots.