diff --git a/Search-based Planning/Search_3D/LRT_Astar3D.py b/Search-based Planning/Search_3D/LRT_Astar3D.py
index e69de29..07e6c2a 100644
--- a/Search-based Planning/Search_3D/LRT_Astar3D.py	
+++ b/Search-based Planning/Search_3D/LRT_Astar3D.py	
@@ -0,0 +1,95 @@
+# this is the three dimensional LRTA* algo
+# !/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+@author: yue qi
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../Search-based Planning/")
+from Search_3D.env3D import env
+from Search_3D.utils3D import getAABB, getDist, getRay, StateSpace, Heuristic, getNearest, isCollide, hash3D, dehash, cost
+from Search_3D.plot_util3D import visualization
+import queue
+
+
+class LRT_A_star(object):
+    def __init__(self,resolution=0.5):
+        self.Alldirec = np.array([[1 ,0,0],[0,1 ,0],[0,0, 1],[1 ,1 ,0],[1 ,0,1 ],[0, 1, 1],[ 1, 1, 1],\
+                      [-1,0,0],[0,-1,0],[0,0,-1],[-1,-1,0],[-1,0,-1],[0,-1,-1],[-1,-1,-1],\
+                      [1,-1,0],[-1,1,0],[1,0,-1],[-1,0, 1],[0,1, -1],[0, -1,1],\
+                      [1,-1,-1],[-1,1,-1],[-1,-1,1],[1,1,-1],[1,-1,1],[-1,1,1]])
+        self.env = env(resolution = resolution)
+        self.Space = StateSpace(self)
+        self.start, self.goal = getNearest(self.Space,self.env.start), getNearest(self.Space,self.env.goal)
+        self.AABB = getAABB(self.env.blocks)
+        self.Space[hash3D(getNearest(self.Space,self.start))] = 0 
+        self.OPEN = queue.QueuePrior() 
+        self.h = Heuristic(self.Space,self.goal) # 1. initialize heuristic h = h0
+        self.Child = {}
+        self.CLOSED = set()
+        self.V = []
+        self.done = False
+        self.Path = []
+
+    def children(self,x):
+        allchild = []
+        for j in self.Alldirec:
+            collide,child = isCollide(self,x,j)
+            if not collide:
+                allchild.append(child)
+        return allchild
+
+    def step(self, xi, strxi):
+        childs = self.children(xi) # 4. generate depth 1 neighborhood S(s,1) = {s' in S | norm(s,s') = 1}
+        fvals = [cost(xi,i) + self.h[hash3D(i)] for i in childs]
+        xj , fmin = childs[np.argmin(fvals)], min(fvals) # 5. compute h'(s) = min(dist(s,s') + h(s'))
+        strxj = hash3D(xj)
+        # add the child of xi
+        self.Child[strxi] = xj
+        if fmin >= self.h[strxi]: # 6. if h'(s) > h(s) then update  h(s) = h'(s)
+            self.h[strxi] = fmin 
+        # TODO: action to move to xj
+        self.OPEN.put(strxj, self.h[strxj]) # 7. update current state s = argmin (dist(s,s') + h(s'))
+
+    def run(self):
+        x0 = hash3D(self.start)
+        xt = hash3D(self.goal)
+        self.OPEN.put(x0, self.Space[x0] + self.h[x0]) # 2. reset the current state
+        self.ind = 0
+        while xt not in self.CLOSED and self.OPEN: # 3. while s not in Sg do
+            strxi = self.OPEN.get()           
+            xi = dehash(strxi) 
+            self.CLOSED.add(strxi) 
+            self.V.append(xi)
+            visualization(self)
+            self.step(xi , strxi)
+            if self.ind % 100 == 0: print('iteration number = '+ str(self.ind))
+            self.ind += 1
+        self.done = True
+        self.Path = self.path()
+        visualization(self)
+        plt.show()
+
+    def path(self):
+        # this is a suboptimal path. 
+        path = []
+        strgoal = hash3D(self.goal)
+        strx = hash3D(self.start)
+        ind = 0
+        while strx != strgoal:
+            path.append([dehash(strx),self.Child[strx]])
+            strx = hash3D(self.Child[strx])
+            ind += 1
+            if ind == 1000:
+                return np.flip(path,axis=0)
+        path = np.flip(path,axis=0)
+        return path
+
+if __name__ == '__main__':
+    Astar = LRT_A_star(0.5)
+    Astar.run()
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