diff --git a/.idea/misc.xml b/.idea/misc.xml
index 6c993b7..a2e120d 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file
diff --git a/.idea/path-planning-algorithms.iml b/.idea/path-planning-algorithms.iml
index 8b8c395..95f5d6e 100644
--- a/.idea/path-planning-algorithms.iml
+++ b/.idea/path-planning-algorithms.iml
@@ -2,11 +2,10 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.7" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">
-    <option name="format" value="PLAIN" />
     <option name="myDocStringFormat" value="Plain" />
   </component>
 </module>
\ No newline at end of file
diff --git a/Search-based Planning/Search_2D/Field_D_star.py b/Search-based Planning/Search_2D/Field_D_star.py
deleted file mode 100644
index 44991b2..0000000
--- a/Search-based Planning/Search_2D/Field_D_star.py	
+++ /dev/null
@@ -1,279 +0,0 @@
-"""
-Field D* 2D
-@author: huiming zhou
-"""
-
-import os
-import sys
-import math
-import matplotlib.pyplot as plt
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../Search-based Planning/")
-
-from Search_2D import plotting
-from Search_2D import env
-
-
-class FieldDStar:
-    def __init__(self, s_start, s_goal, heuristic_type):
-        self.s_start, self.s_goal = s_start, s_goal
-        self.heuristic_type = heuristic_type
-
-        self.Env = env.Env()  # class Env
-        self.Plot = plotting.Plotting(s_start, s_goal)
-
-        self.u_set = self.Env.motions  # feasible input set
-        self.obs = self.Env.obs  # position of obstacles
-        self.x = self.Env.x_range
-        self.y = self.Env.y_range
-
-        self.g, self.rhs, self.OPEN = {}, {}, {}
-        self.parent = {}
-        self.cknbr = {}
-        self.ccknbr = {}
-        self.bptr = {}
-        self.init_table()
-
-        for i in range(self.Env.x_range):
-            for j in range(self.Env.y_range):
-                self.rhs[(i, j)] = float("inf")
-                self.g[(i, j)] = float("inf")
-                self.bptr[(i, j)] = (i, j)
-
-        self.rhs[self.s_goal] = 0.0
-        self.OPEN[self.s_goal] = self.CalculateKey(self.s_goal)
-        self.visited = set()
-        self.count = 0
-        self.fig = plt.figure()
-
-    def init_table(self):
-        for i in range(1, self.Env.x_range - 1):
-            for j in range(1, self.Env.y_range - 1):
-                s_neighbor = self.get_neighbor_pure((i, j))
-                s_neighbor.append(s_neighbor[0])
-                for k in range(8):
-                    self.cknbr[((i, j), s_neighbor[k])] = s_neighbor[k + 1]
-                s_neighbor = list(reversed(s_neighbor))
-                for k in range(8):
-                    self.ccknbr[((i, j), s_neighbor[k])] = s_neighbor[k + 1]
-
-    def run(self):
-        self.Plot.plot_grid("Field D*")
-        self.ComputeShortestPath()
-        self.plot_path(self.extract_path())
-        self.fig.canvas.mpl_connect('button_press_event', self.on_press)
-        plt.show()
-
-    def on_press(self, event):
-        x, y = event.xdata, event.ydata
-        if x < 0 or x > self.x - 1 or y < 0 or y > self.y - 1:
-            print("Please choose right area!")
-        else:
-            x, y = int(x), int(y)
-            print("Change position: x =", x, ",", "y =", y)
-            self.visited = set()
-            self.count += 1
-
-            if (x, y) not in self.obs:
-                self.obs.add((x, y))
-                plt.plot(x, y, 'sk')
-                sn_list = self.get_neighbor((x, y))
-            else:
-                self.obs.remove((x, y))
-                plt.plot(x, y, marker='s', color='white')
-                sn_list = [(x, y)]
-                sn_list += self.get_neighbor((x, y))
-
-            for s in sn_list:
-                v_list = []
-                for sn in self.get_neighbor(s):
-                    v_list.append(self.ComputeCost(s, sn, self.ccknbr[(s, sn)]))
-                self.rhs[s] = min(v_list)
-                self.UpdateVertex(s)
-
-            self.ComputeShortestPath()
-            self.plot_visited(self.visited)
-            self.plot_path(self.extract_path())
-            self.fig.canvas.draw_idle()
-
-    def ComputeShortestPath(self):
-        while True:
-            s, v = self.TopKey()
-            if v >= self.CalculateKey(self.s_start) and \
-                    self.rhs[self.s_start] == self.g[self.s_start]:
-                break
-
-            if self.g[s] > self.rhs[s]:
-                self.g[s] = self.rhs[s]
-                self.OPEN.pop(s)
-                for sn in self.get_neighbor(s):
-                    if self.rhs[sn] > self.ComputeCost(sn, s, self.ccknbr[(sn, s)]):
-                        self.rhs[sn] = self.ComputeCost(sn, s, self.ccknbr[(sn, s)])
-                        self.bptr[sn] = s
-                    if self.rhs[sn] > self.ComputeCost(sn, s, self.cknbr[(sn, s)]):
-                        self.rhs[sn] = self.ComputeCost(sn, self.cknbr[(sn, s)], s)
-                        self.bptr[sn] = self.cknbr[(sn, s)]
-                    self.UpdateVertex(sn)
-            else:
-                self.g[s] = float("inf")
-                for sn in self.get_neighbor(s):
-                    if self.bptr[sn] == s or self.bptr[sn] == self.cknbr[(sn, s)]:
-                        v_list = []
-                        ssn_list = self.get_neighbor(sn)
-                        for ssn in ssn_list:
-                            v_list.append(self.ComputeCost(sn, ssn, self.ccknbr[(sn, ssn)]))
-                        self.rhs[sn] = min(v_list)
-                        self.bptr[sn] = ssn_list[v_list.index(min(v_list))]
-                        self.UpdateVertex(sn)
-                self.UpdateVertex(s)
-
-    def UpdateVertex(self, s):
-        if self.g[s] != self.rhs[s]:
-            self.OPEN[s] = self.CalculateKey(s)
-        elif s in self.OPEN:
-            self.OPEN.pop(s)
-
-    def get_neighbor_pure(self, s):
-        s_list = []
-
-        for u in self.u_set:
-            s_next = tuple([s[i] + u[i] for i in range(2)])
-            s_list.append(s_next)
-
-        return s_list
-
-    def CalculateKey(self, s):
-        return [min(self.g[s], self.rhs[s]) + self.h(self.s_start, s),
-                min(self.g[s], self.rhs[s])]
-
-    def ComputeCost(self, s, sa, sb):
-        if sa[0] != s[0] and sa[1] != s[1]:
-            s1, s2 = sb, sa
-        else:
-            s1, s2 = sa, sb
-
-        c = self.cost(s, s2)
-        b = self.cost(s, s1)
-
-        if c != float("inf"):
-            c = c / math.sqrt(2)
-
-        if min(c, b) == float("inf"):
-            vs = float("inf")
-        elif self.g[s1] <= self.g[s2]:
-            vs = min(c, b) + self.g[s1]
-        else:
-            f = self.g[s1] - self.g[s2]
-            if f <= b:
-                if c <= f:
-                    vs = math.sqrt(2) * c + self.g[s2]
-                else:
-                    y = min(f / (math.sqrt(c ** 2 - f ** 2)), 1)
-                    vs = c * math.sqrt(1 + y ** 2) + f * (1 - y) + self.g[s2]
-            else:
-                if c <= b:
-                    vs = math.sqrt(2) * c + self.g[s2]
-                else:
-                    x = 1 - min(b / (math.sqrt(c ** 2 - b ** 2)), 1)
-                    vs = c * math.sqrt(1 + (1 - x) ** 2) + b * x + self.g[s2]
-
-        return vs
-
-    def TopKey(self):
-        """
-        :return: return the min key and its value.
-        """
-
-        s = min(self.OPEN, key=self.OPEN.get)
-        return s, self.OPEN[s]
-
-    def h(self, s_start, s_goal):
-        heuristic_type = self.heuristic_type  # heuristic type
-
-        if heuristic_type == "manhattan":
-            return abs(s_goal[0] - s_start[0]) + abs(s_goal[1] - s_start[1])
-        else:
-            return math.hypot(s_goal[0] - s_start[0], s_goal[1] - s_start[1])
-
-    def cost(self, s_start, s_goal):
-        """
-        Calculate cost for this motion
-        :param s_start: starting node
-        :param s_goal: end node
-        :return:  cost for this motion
-        :note: cost function could be more complicate!
-        """
-
-        if self.is_collision(s_start, s_goal):
-            return float("inf")
-
-        return math.hypot(s_goal[0] - s_start[0], s_goal[1] - s_start[1])
-
-    def is_collision(self, s_start, s_end):
-        if s_start in self.obs or s_end in self.obs:
-            return True
-
-        if s_start[0] != s_end[0] and s_start[1] != s_end[1]:
-            if s_end[0] - s_start[0] == s_start[1] - s_end[1]:
-                s1 = (min(s_start[0], s_end[0]), min(s_start[1], s_end[1]))
-                s2 = (max(s_start[0], s_end[0]), max(s_start[1], s_end[1]))
-            else:
-                s1 = (min(s_start[0], s_end[0]), max(s_start[1], s_end[1]))
-                s2 = (max(s_start[0], s_end[0]), min(s_start[1], s_end[1]))
-
-            if s1 in self.obs or s2 in self.obs:
-                return True
-
-        return False
-
-    def get_neighbor(self, s):
-        s_list = []
-        for u in self.u_set:
-            s_next = tuple([s[i] + u[i] for i in range(2)])
-            if s_next not in self.obs:
-                s_list.append(s_next)
-
-        return s_list
-
-    def extract_path(self):
-        path = [self.s_start]
-        s = self.s_start
-        count = 0
-        while True:
-            count += 1
-            s = self.bptr[s]
-            path.append(s)
-
-            if s == self.s_goal or count > 100:
-                return list(reversed(path))
-
-    def plot_path(self, path):
-        px = [x[0] for x in path]
-        py = [x[1] for x in path]
-        plt.plot(px, py, linewidth=2)
-        plt.plot(self.s_start[0], self.s_start[1], "bs")
-        plt.plot(self.s_goal[0], self.s_goal[1], "gs")
-
-    def plot_visited(self, visited):
-        color = ['gainsboro', 'lightgray', 'silver', 'darkgray',
-                 'bisque', 'navajowhite', 'moccasin', 'wheat',
-                 'powderblue', 'skyblue', 'lightskyblue', 'cornflowerblue']
-
-        if self.count >= len(color) - 1:
-            self.count = 0
-
-        for x in visited:
-            plt.plot(x[0], x[1], marker='s', color=color[self.count])
-
-
-def main():
-    s_start = (5, 5)
-    s_goal = (45, 25)
-
-    fielddstar = FieldDStar(s_start, s_goal, "euclidean")
-    fielddstar.run()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/Search-based Planning/Search_2D/Potential_Field.py b/Search-based Planning/Search_2D/PotentialField.py
similarity index 99%
rename from Search-based Planning/Search_2D/Potential_Field.py
rename to Search-based Planning/Search_2D/PotentialField.py
index 44d0c85..497a5c0 100644
--- a/Search-based Planning/Search_2D/Potential_Field.py	
+++ b/Search-based Planning/Search_2D/PotentialField.py	
@@ -12,6 +12,7 @@ from collections import deque
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
                 "/../../Search-based Planning/")
 
+import
 from Search_2D import plotting
 from Search_2D import env
 
diff --git a/Search-based Planning/Search_2D/__pycache__/env.cpython-37.pyc b/Search-based Planning/Search_2D/__pycache__/env.cpython-37.pyc
index 033d81b..b0d7aae 100644
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diff --git a/Search-based Planning/Search_2D/__pycache__/plotting.cpython-37.pyc b/Search-based Planning/Search_2D/__pycache__/plotting.cpython-37.pyc
index 86c10d3..1c78399 100644
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diff --git a/Search-based Planning/Search_2D/__pycache__/queue.cpython-37.pyc b/Search-based Planning/Search_2D/__pycache__/queue.cpython-37.pyc
index bcbb608..c5190fa 100644
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