You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This is the Unidirectional and Bidirectional A Star Search Algorithm Python Implementation.
5
4
6
-
1. For the heuristic, Euclidean distance have been used to get an estimate from the given node to the goal node.
5
+
Important Details (Unidirectional A star search Algorithm):
6
+
7
+
1. For the heuristic, Euclidean distance/octile have been used to get an estimate from the given node to the goal node.
7
8
8
9
2. In order to address the ties (nodes having the same F value in our (discovered) heap, then we prefer using that node whihc are closer to the goal node i.e. node having higher H value.)
9
10
@@ -12,3 +13,16 @@ Important Details:
12
13
4. This particular implementation will only work for 2D maps.
13
14
14
15
5. In order to get more test cases, please refer to https://www.movingai.com/benchmarks/grids.html
16
+
17
+
Important Details (Bidirectional A star search Algorithm):
18
+
19
+
1. For the heuristic, Euclidean distance/octile have been used to get an estimate from the given node to the goal node.
20
+
21
+
2. The node selction policy that is being used here is : we select that frontier which offers us the node with minimum f-value.
22
+
23
+
3. If the source/target node happens to be such a node which is not walkable, then the algorithm will return an empty path with a total cost of 0.
24
+
25
+
4. This particular implementation will only work for 2D maps.
26
+
27
+
5. In order to get more test cases, please refer to https://www.movingai.com/benchmarks/grids.html
0 commit comments