Global path planning is considered as a fundamental problem for mobile robots. In this paper, we investigate the capabilities of genetic algorithms (GA) for solving the global path planning problem in large-scale grid maps. First, we propose a GA approach for efficiently finding an (or near) optimal path in the grid map. We carefully designed GA operators to optimize the search process. We also conduct a comprehensive statistical evaluation of the proposed GA approach in terms of solution quality, and we compare it against the well-known A* algorithm as a reference. Extensive simulation results show that GA is able to find the optimal paths in large environments equally to A* in almost all the simulated cases.
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