RRT* (Rapidly exploring Random Tree*) based algorithms are widely used for path planning. Informed RRT* uses RRT* for generating an initial path and optimizes the path by limiting sampling regions to the area around the initial path. RRT* algorithms have several limitations such as slow convergence speed, large memory requirements, and difficulties in finding paths when narrow aisles or doors exist. In this paper, we propose an algorithm to deal with these problems. The proposed algorithm applies the image skeletonization to the gridmap image for generating an initial path. Because this initial path is close to the optimal cost path even in the complex environments, the cost can converge to the optimum more quickly in the proposed algorithm than in the conventional Informed RRT*. Also, we can reduce the number of nodes and memory requirement. The performance of the proposed algorithm is verified by comparison with the conventional Informed RRT* and Informed RRT* using initial path generated by A*.