Random Walk Exploration for Swarm Mapping

Abstract

Research in swarm robotics has shown that robot swarms are effective in the exploration of unknown environments. However, little work has been devoted to port the exploration capabilities of robot swarms into the context of mapping. Indeed, conceiving robot swarms that can map an unknown environment in a robust, scalable, and flexible way is an open issue. In this paper, we investigate a swarm mapping method in which robots first individually map the environment by random walk and then, we merge their maps into a single, global one. We focus on five variants of random walk and we compare the quality of the maps that a swarm produces when exploring the environment using these variants. Our experiments with ten e-puck robots show that, despite the individual maps being incomplete by themselves, it is possible to collectively map the environment by merging them. We found that the quality of the map depends on the exploration behavior of the individuals. Our results suggest that one of the variants of random walk, the ballistic motion, gives better mapping results for closed environments.