The past few decades have seen tremendous progress in planetary exploration rovers and the most groundbreaking missions in space exploration history. Although the main focus has been toward single rover deployment, there has also been substantial research into swarm intelligence and multi-rover systems. This paper presents single and multi-rover path planning strategies for future planetary exploration missions. The main objective is to develop real-time Coverage Path Planning (CPP) solutions in unknown environments. The algorithms presented here are based on simple building blocks that use solely myopic sensing information to iteratively compute a motion cost function and decide their next move. Several single rover exploration methods are introduced and compared through simulation. The results show that complete coverage is not always possible in planetary exploration scenarios. Also, by slightly reducing the required percent of terrain to explore, the efficiency can be improved. In addition to this, two variants of multi-rover path planning algorithms are defined. A first method is a fully collaborative method, where the rovers explore a region together and a second where the terrain is segmented and the rover operate separately from each other.