Optimal exploration path Code
Optimal exploration of grid environments for a realistic robot.
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quattrinili
File | Date | Author | Commit |
---|---|---|---|
incl | 2013-12-14 | quattrinili | [r1] Code for finding exploration path |
src | 2013-12-14 | quattrinili | [r1] Code for finding exploration path |
tools | 2013-12-14 | quattrinili | [r1] Code for finding exploration path |
Doxyfile | 2013-12-14 | quattrinili | [r1] Code for finding exploration path |
Makefile | 2013-12-14 | quattrinili | [r1] Code for finding exploration path |
README | 2013-12-28 | quattrinili | [r3] Updated README file. |
main.cpp | 2013-12-14 | quattrinili | [r1] Code for finding exploration path |
============================================================================= C++ Exploration problem solver Alberto Quattrini Li Politecnico di Milano, Italy 21/12/2013 ============================================================================= STRUCTURE OF FOLDER =================== -ROOT-+- : contains Doxyfile, README, Makefile (linux), and main.cpp | |-incl : headers of classes |-src : sources of classes '-tools: contains source code of another method INSTRUCTIONS ============ To compile the program, just use the make tool. Dependencies: - OpenCV - google-glog - google-gflags -Linux 1.Have installed g++ and make 2.take Makefile related to linux in the root 3.launch make To change parameters to the problem you can use the parameters specified by gflags The format of the map should be a gray-scale image, where white (255) represents free space, and gray (128) represents obstacles. In the output images, black (0) represents unknown area. DESCRIPTION =========== The search class library provided is meant to be used for finding the best path, assuming that we already know the map. In this way, we can compare, given a map, how distant the result given by online algorithms is from the optimal execution of the path.