Experimental Maps for Robot Path Planning
If you are searching for missing figures (Fig. 2) or examples of execution, please click this hyperlink [Figures and Explanation].
In robot path planning, the size of map is important factor to measure the performance of path planning algorithm.
We hope that it would be helpful for researchers who need large and realistic maps for robot path planning experiments. If you want to employ these maps for your research, please cite below reference.
Each map is delivered by MATLAB .mat file containing sp, dp, and map variables. The variables sp and dp indicates the index of starting and destination nodes, and map is a 2-dimensional binary array. In map variable, the obstacle node is represented as 1 (true) and free-space node is represented as 0 (false), respectively.
Figure (a) shows the index of each node in our proposed method. The nodes with yellow and red color indicates the starting and destination node, respectively. In this case, the value of variable sp is 6 and the value of variable dp is 20. According to Figure (a), the variable map will be like Figure (b).
Caves - 20 Artificial Maps
We created 20 artificial maps based on Random Dungeon Generator. Each map is composed of 500 by 500 nodes. Because of the shape, we named them as Cave. The thumbnails for 20 maps are as follows.
For Caves map set, we provide CSV version of .mat file. In CSV version, each map is delivered by text-plain files. Specific information for the format is:
Triumph, Pisa, Tokyo, and Vatican - 4 Real-World Maps
We created 4 real-world maps based on Google Maps service. These maps are obtained by finger-printing the image given by Google Map service after eliminating unnecessary objects and color information. Each map has 4 variations with different number of nodes; 1000 by 1000, 2000 by 2000, 3000 by 3000, and 4000 by 4000.