Algorithms for distributed exploration

In this paper we propose algorithms for a set of problems where a distributed team of agents tries to compile a global map of the environment from local observations. We focus on two approaches: one based on behavioural agent technology where agents are pulled (or repelled) by various forces, and another where agents follow a approximate planning approach that is based on dynamic programming. We study these approaches under different conditions, such as different types of environments, varying sensor and communication ranges, and the availability of prior knowledge of the map. The results show that in most cases the simpler behavioural agent teams perform at least as well, if not better, than the teams based on approximate planning and dynamic programming. The research has not only practical implications for distributed exploration tasks, but also for analogous distributed search or optimisation problems.