diff --git a/Search-based Planning/.idea/workspace.xml b/Search-based Planning/.idea/workspace.xml
index 0cf8218..f4555aa 100644
--- a/Search-based Planning/.idea/workspace.xml
+++ b/Search-based Planning/.idea/workspace.xml
@@ -19,7 +19,12 @@
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@@ -64,8 +69,8 @@
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diff --git a/Search-based Planning/Search_2D/ara_star.py b/Search-based Planning/Search_2D/ARA_star.py
similarity index 100%
rename from Search-based Planning/Search_2D/ara_star.py
rename to Search-based Planning/Search_2D/ARA_star.py
diff --git a/Search-based Planning/Search_2D/ida_star.py b/Search-based Planning/Search_2D/IDA_star.py
similarity index 100%
rename from Search-based Planning/Search_2D/ida_star.py
rename to Search-based Planning/Search_2D/IDA_star.py
diff --git a/Search-based Planning/Search_2D/LRTA_star.py b/Search-based Planning/Search_2D/LRTA_star.py
new file mode 100644
index 0000000..7d3ba64
--- /dev/null
+++ b/Search-based Planning/Search_2D/LRTA_star.py
@@ -0,0 +1,117 @@
+"""
+LRTA_star 2D
+@author: huiming zhou
+"""
+
+import os
+import sys
+import matplotlib.pyplot as plt
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+ "/../../Search-based Planning/")
+
+from Search_2D import queue
+from Search_2D import plotting
+from Search_2D import env
+
+
+class LrtAstar:
+ def __init__(self, x_start, x_goal, heuristic_type):
+ self.xI, self.xG = x_start, x_goal
+ self.heuristic_type = heuristic_type
+
+ self.Env = env.Env() # class Env
+
+ self.u_set = self.Env.motions # feasible input set
+ self.obs = self.Env.obs # position of obstacles
+
+ self.g = {self.xI: 0, self.xG: float("inf")}
+ self.OPEN = queue.QueuePrior() # priority queue / OPEN
+ self.OPEN.put(self.xI, self.h(self.xI))
+ self.CLOSED = set()
+ self.Parent = {self.xI: self.xI}
+
+ def searching(self):
+ h = {self.xI: self.h(self.xI)}
+ s = self.xI
+ parent = {self.xI: self.xI}
+ visited = []
+ count = 0
+ while s != self.xG:
+ count += 1
+ print(count)
+ visited.append(s)
+ h_list = {}
+ for u in self.u_set:
+ s_next = tuple([s[i] + u[i] for i in range(len(s))])
+ if s_next not in self.obs:
+ if s_next not in h:
+ h[s_next] = self.h(s_next)
+ h_list[s_next] = self.get_cost(s, s_next) + h[s_next]
+ h_new = min(h_list.values())
+ if h_new > h[s]:
+ h[s] = h_new
+ s_child = min(h_list, key=h_list.get)
+ parent[s_child] = s
+ s = s_child
+ # path_get = self.extract_path(parent)
+ return [], visited
+
+ def extract_path(self, parent):
+ path = [self.xG]
+ s = self.xG
+
+ while True:
+ s = parent[s]
+ path.append(s)
+
+ if s == self.xI:
+ break
+
+ return path
+
+ def h(self, s):
+ heuristic_type = self.heuristic_type
+ goal = self.xG
+
+ if heuristic_type == "manhattan":
+ return abs(goal[0] - s[0]) + abs(goal[1] - s[1])
+ elif heuristic_type == "euclidean":
+ return ((goal[0] - s[0]) ** 2 + (goal[1] - s[1]) ** 2) ** (1 / 2)
+ else:
+ print("Please choose right heuristic type!")
+
+ @staticmethod
+ def get_cost(x, u):
+ """
+ Calculate cost for this motion
+
+ :param x: current node
+ :param u: input
+ :return: cost for this motion
+ :note: cost function could be more complicate!
+ """
+
+ return 1
+
+
+def main():
+ x_start = (10, 5) # Starting node
+ x_goal = (45, 25) # Goal node
+
+ lrtastar = LrtAstar(x_start, x_goal, "manhattan")
+ plot = plotting.Plotting(x_start, x_goal) # class Plotting
+
+ path, visited = lrtastar.searching()
+ pathx = [x[0] for x in path]
+ pathy = [x[1] for x in path]
+ vx = [x[0] for x in visited]
+ vy = [x[1] for x in visited]
+ plot.plot_grid("test")
+ plt.plot(pathx, pathy, 'r')
+ plt.plot(vx, vy, 'gray')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()