Due to increased exposure of the automation and robotics fields to applications such as the exploration of unknown terrain and search and rescue missions, among others, the idea of a system composed of an Unmanned Ground Vehicle (UGV) and Unmanned Aerial Vehicle (UAV) is being proposed to execute these missions. In this paper, several meta-heuristic optimization techniques were implemented to plan the UAV and UGV's paths including Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Aquila Optimizer (AO) and several modified versions of these techniques, such as (AGWO), a newly developed hybrid approach between the AO and GWO. The results showed that the combination of the Variable Weight Grey Wolf Optimizer (VW-GWO) and Ant Colony Optimizer (ACO) resulted in the best performance to solve these problems.