Path Planning

This repository contains path planning algorithms in C++.

Algorithms

1. Dijkstra's algorithm for grid based search.
2. AStar (A*) algorithm for grid based search.
3. Jump Point Search for grid based search (Modified for 4 way motion; no diagonal motion).
4. Lifelong Planning AStar (LPA*) algorithm for grid based search.
5. DStarLite (D* Lite) algorithm for grid based search.
6. RRT algorithm for grid based search.
7. RRTStar (RRT*) algorithm for grid based search.
8. Ant Colony Optimization algorithm (ACO) for grid based search.
9. Genetic algorithm (GA) for grid based search.

To build and run

 git clone https://github.com/vss2sn/path_planning.git  
 cd path_planning  
 mkdir build  
 cd build  
 cmake .. && make -j4  
 ./main  

Table of contents

• Algorithms
• Instructions
• Table of contents
• Notes
• Notes on CMake Options
• Notes on tests
• Notes on implementations
• TODOs

Notes

1. main creates a grid of a given size n, with any point set as an obstacle with a probability of 1/n. It then runs all the algorithms in the repository on the given grid.
2. Documentation can be found on GitHub pages. It has been created using Doxygen, and pip3 packages Sphinx (sphinx==1.8.3), Breathe (breathe==4.12.0), Exhale (exhale==0.2.2) and Read the Docs Sphinx Theme (sphinx_rtd_theme==0.4.3).

Notes on CMake Options

1. To run each algorithm independently, set BUILD_INDIVIDUAL to ON (Executables created: dijkstra, a_star, etc). If you want to run all of them on the same grid, set BUILD_INDIVIDUAL to OFF (Executable created: main).
2. To run tests, set BUILD_INDIVIDUAL to OFF and RUN_TESTS to ON.
3. Set CHECK_COVERAGE to check code coverage.
4. Set CUSTOM_DEBUG_HELPER_FUNCION to build functions that are used primarily for debugging (excluded from code coverage)

Notes on test

1. Unit test framework set up to set algorithms under different grids. This section uses Google Test.
2. CMake option RUN_TESTS allows building tests when set when BUILD_INDIVIDUAL is set OFF.
3. Due to the nature of Ant Colony Optimization and accounting for the hyper parameters, the tests are run with a 20% margin above the optimal solution. Similarly for Genetic Algorithm.
4. As RRT is not optimal, the test for RRT simply checks for the existence of a path

Notes on implementations

1. RRT stops as soon as goal is found. It is connects new points to the nearest point, not accounting for total cost to reach that point. In contrast RRT* chooses to connect to a new node to the node that allows the new node to have the minimum cost. RRT* also rewires the preexisting nodes to the new node if that path allows for a lower cost for the preexisting node.
2. Acceptable motions can be modified in the GetMotion() function in utils.cpp.
3. A* and D* Lite use Manhattan distance (L1) as their heuristic (change to L2 if adding diagonal moves to the GetMotion function). D* Lite also uses the same in its C function.
4. LPA* and D* Lite can be run live with random obstacle creation using the RunDStarLite function. For the live run of D* Lite, obstacles are detected on the current path of the bot with a probability of 1/n, n being the number of rows/columns in the grid. D* Lite is implemented based on Sven Koenig's & Maxim Likhachev's paper. It is also possible to specify the time step time step at which an obstacle will be discovered, irrespective of the current position.
5. To specify your own grid, set n to number of rows, created the 2D vector, setting 1 for obstacles and 0 elsewhere.
6. The genetic algorithm has an option shorten_chromosome, which allows the shortening of the chromosome (path length) based on the length of the path found that reaches the goal. This reduces computation time and pushes the solution towards the shortest path.

TODOs

1. Add references
2. Add algorithm explanations