![]() An open set and closed set are being kept. PathPlanner class is defined to get a map, a start node and destination node and method is defined to compute the shortest path between those two while searching in the right direction. The A* algorithm is implemented in project_notebook.ipynb using starter code provided by Udacity. Test.py is provided by Udacity to test the results of the implementation given the provided two maps. The information about the map is kept in the dictionary as keys for nodes ids and values for the city's latitude and longitude as well as the adjacent cities. A map is created through the class Map who builds a graph (as a network) from values given in a dictionary. The files are:įrom helpers.py two maps are imported. This project is implemented in Python under Jupyter Notebook. This requirement is fulfilled when the heuristic is the straight distance between two points on a map since real roads paths are generally longer. For A* to find the shortest path, the heuristic of each node needs to be optimistic, or to represent an estimation that is shorter or equal than the real distance. The distance estimation is called the heuristic. A* is expanding the route that keeps the sum of total cost and the distance Since it does not keep record of the lowest total cost, it will find the destination but it will not always provide the shortest route, for example if there are obstacles along the way.įinding the shortest path while searching in the correct direction is where A* is the best choice. For the map search this is the straight line between nodes A and B.īest First Search, or greedy algorithm, always searches for the next node which gets you closest to the destination based on the estimate of the distance. For this we calculate the estimate distance between the start point and the destination. We want a targeted search that gets us in the direction of the destination. ![]() Imagine looking for your destination while going in the opposite direction. The bigger the map, the more obvious the inefficiency is. This algorithm works well if there is no concern for efficiency and number of operations. If all roads would be equal in cost, this search method is basicly the Breadth First Search When the destination is found and picked as being the shortest path from the start, we know that we found the shortest route. The nodes are explored in order based on how far they are from the start and keeping track of the total cost. ![]() The Uniform Cost Search is guaranteed to find the cheapest path from a starting node to the destination.īest Cost Search algorithm based on Dijkstra's algorithm applies the cheapest first method, expanding the shortest route from the start in every direction. Finding the shortest path is reduced to minimizing the total cost of the path. Operating on a real time system as in an autonomous vehicle, the efficiency of the algorithm is a big concern.ĭistances between cities can be seen as cost. The second is how the shortest route can be computed. The first is minimizing the total distance or cost of the final route. The best algorithm for this graph search needs to take into account two factors. Now that the problem has been reduced to a graph representation, the task is limited to a graph search that is best suited for this case. The car's current location is node A and the destination city is node B. Together with a list of its neighboring cities. For each city the global position is considered known, in terms of latitude and longitude, Cities are seen as nodes and roads are its edges. Given that the map is known, this can be represented under the form of a graph. Google maps style routing algorithm to calculate the shortest path between two points using A* search algorithm.įinding the shortest path between two locations is a key task in path planning expecially for self driving cars to navigate from point A to point B. ![]()
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