Autonomous vehicles are expected to play a key role in rescue and transportation. Planning an optimal path with the minimum computational effort for these vehicles in their missions improves their efficiency and adds safety for the vehicles and third parties on the ground. The objective of this thesis is to study the computational effort of four planning methods that implement linear temporal logic (LTL) to translate the high-level mission requirements and environmental specifications. The Potential Field Method and the Critical Path method required less computational effort to find one of the shortest paths for the mission The Multigraph Network Planning method and the Critical Path method can find all the possible paths with predetermined path length. The Random Walk method required more computational effort and memory compared to the other three methods.