A* is a widely used algorithm for pathfinding in 2-D environments. This project takes the planning algorithm one step further and applies it to a 3-Dimensional environment.
This project's objective is to buid a planning module for Aerial Vehicles. Provided with the world boundary and Obstacle location data, the module provides the optimal path from start point to the goal by avoiding all obstacles.
This Project is developed using C++11 Standards and a compiler supporting c++11 is necessary to run the tests and demo.
Development of this project follows Solo Iterative Process (SIP) model in Software Engineering.
Tested on Ubuntu 16.04 LTS with GCC 5.4.0.
List of Libraries and Softwares used for development and their License information can be found Here
The World boundary and obstacle data is provided in the (xmin,ymin,zmin,xmax,ymax,zmax) format. The module then uses this data along with the robot dimensions information to discretize the world and build a 3-D Configurtion Space. This C-Space is used to plan the Robot's path using A* Search.
A* is an algorithm that searches for lowest cost path among all possible paths from start to goal. What A* algorithm does is that at each step it picks the node from open list according to the value f, which is sum of two other parameters g and h.
Here, g value of a node is the cost to move from start to that node and h value is the estimated movement cost to move from that given node to the final destination.There are many ways to calculate h and this project provides an option to select between Euclidean Distance and Manhattan Distance.
Obstacle nodes have the cost of infinity to travel through them and hence, the planner avoids these nodes in path planning.
Typical implementations of A* use a priority queue to perform the repeated selection of minimum (estimated) cost nodes to expand. This priority queue is known as the open list. At each step of the algorithm, the node with the lowest f(x) value is removed from the queue, the f and g values of its neighbors are updated accordingly, and these neighbors are added to the queue. The algorithm continues until a goal node has a lower f value than any node in the queue (or until the queue is empty). The f value of the goal is then the length of the shortest path, since h at the goal is zero in an admissible heuristic.
- Iteration 1:
- Planning of the Project Structure and Repository Setup.
- Setup Travis and Coveralls.
- Iteration 2:
- Build the Unit Testing Environment and Class Stubs.
- Implementation of Map Builder and A* Planner with Fully working Path Planner.
- Release Version 1.0.
- Iteration 3:
- Add Manhattan Distance Heuristic.
- Explore the Possibility of 3-D Plots.
- Release Version 2.0.
- Iteration 4:
- Documentation.
- Code Optimization.
Product and Iteration Backlogs can be found Here
UML Diagrams can be found Here
Skeleton Code and Initial UML Diagrams.
Implementation of Map Builder from Environment Boundary and Obstacle Data.
Implementation of A* Planner.
Release Version 1.0 of the Module.
Version 1.0 is Now Online.
Version 2.0 is Now Online.
Explore Possibility of 3-D Plots.
Version 2.0 is Now Online.
Add Manhattan Distance Heuristic Function.
Doxygen Comments and Documentation.
git clone --recursive https://github.com/VBot2410/A_Star_3D
cd <path to repository>
mkdir build
cd build
cmake ..
make
Run tests: ./test/A_Star-test
Run program: ./app/A_Star-app
Start Point:(0,0.5,3) & Goal Point:(3.75,6.25,0)
X,Y,Z resolution 0.25 and Robot Dimensions Margin 0.2
The Planned Path was as follows:
Doxygen is Required for Generating the Documentation for this project.
More Information on Installation Instructions Can be Found Here
To Generate the Documentation, Navigate to the A_Star_3D directory and run following command
doxygen A_Star_3D_Documentation
The Documentation will be Created in Doxygen Directory.
This Project is Licensed under the MIT License. Copy of the License Can be Found Here
MIT License
Copyright (c) 2017 Vaibhav Bhilare
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