Space exploration algorithms aim to discover as much unknown space as possible as efficiently as possible in the shortest possible time. To achieve this goal, we use distributed algorithms, implemented on multi-agent systems. In this work, we explore, which of the algorithms can efficiently explore space in a simulated environment Gridland. Since Gridland, in it's original release, was not meant for simulating space exploration, we had to make some modifications and enable movement history and action tracking for a multi-agent system with the purpose of algorithm efficiency analysis. A random agent was implemented for reference and compared with an algorithm, that represents a group of so called "pseudo-random" algorithms, and a particle swarm based algorithm. We show that pseudo-random algorithms are much better than random algorithms, despite their simplicity. Algorithm RDPSO, based on particle swarm optimisation, proved to be efficient, despite not being the fastest.
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