Merge branch 'master' of https://github.com/zhm-real/path-planning-algorithms

Search-based Planning/Search_3D/Dstar3D.py

@@ -8,32 +8,43 @@ from collections import defaultdict
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../Search-based Planning/")
 from Search_3D.env3D import env
 from Search_3D import Astar3D
-from Search_3D.utils3D import StateSpace, getDist, getNearest, getRay, isinbound, isinball, isCollide, children, cost, \
-    initcost
+from Search_3D.utils3D import StateSpace, getDist, getNearest, getRay, isinbound, isinball, isCollide, children, cost, initcost
 from Search_3D.plot_util3D import visualization
 
 
 class D_star(object):
-    def __init__(self, resolution=1):
+    def __init__(self,resolution = 1):
         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.env = env(resolution = resolution)
         self.X = StateSpace(self.env)
         self.x0, self.xt = getNearest(self.X, self.env.start), getNearest(self.X, self.env.goal)
-        self.b = defaultdict(lambda: defaultdict(dict))  # back pointers every state has one except xt.
-        self.OPEN = {}  # OPEN list, here use a hashmap implementation. hash is point, key is value
-        self.h = self.initH()  # estimate from a point to the end point
-        self.tag = self.initTag()  # set all states to new
-        self.V = set()  # vertice in closed
+        self.b = defaultdict(lambda: defaultdict(dict))# back pointers every state has one except xt.
+        self.OPEN = {} # OPEN list, here use a hashmap implementation. hash is point, key is value 
+        self.h = self.initH() # estimate from a point to the end point
+        self.tag = self.initTag() # set all states to new
+        self.V = set()# vertice in closed
         # initialize cost set
         # self.c = initcost(self)
         # for visualization
         self.ind = 0
         self.Path = []
         self.done = False
+        self.Obstaclemap = {}
+
+        
+    def update_obs(self):
+        for xi in self.X:
+            print('xi')
+            self.Obstaclemap[xi] = False
+            for aabb in self.env.blocks:
+                self.Obstaclemap[xi] = isinbound(aabb, xi)
+            if self.Obstaclemap[xi] == False:
+                for ball in self.env.balls:
+                    self.Obstaclemap[xi] = isinball(ball, xi)
 
     def initH(self):
         # h set, all initialzed h vals are 0 for all states.
@@ -46,7 +57,7 @@ class D_star(object):
         # tag , New point (never been in the OPEN list)
         #       Open point ( currently in OPEN )
         #       Closed (currently in CLOSED)
-        t = {}
+        t = {} 
         for xi in self.X:
             t[xi] = 'New'
         return t
@@ -56,7 +67,7 @@ class D_star(object):
         # -1 if it does not exist
         if self.OPEN:
             minv = np.inf
-            for v, k in enumerate(self.OPEN):
+            for v,k in enumerate(self.OPEN):
                 if v < minv: minv = v
             return minv
         return -1
@@ -67,11 +78,11 @@ class D_star(object):
         # it also removes this min value form the OPEN set.
         if self.OPEN:
             minv = np.inf
-            for v, k in enumerate(self.OPEN):
+            for v,k in enumerate(self.OPEN):
                 if v < minv: mink, minv = k, v
             return mink, self.OPEN.pop(mink)
         return None, -1
-
+    
     def insert(self, x, h_new):
         # inserting a key and value into OPEN list (x, kx)
         # depending on following situations
@@ -82,31 +93,31 @@ class D_star(object):
         if self.tag[x] == 'Closed':
             kx = min(self.h[x], h_new)
         self.OPEN[x] = kx
-        self.h[x], self.tag[x] = h_new, 'Open'
-
+        self.h[x],self.tag[x] = h_new, 'Open'
+            
     def process_state(self):
         x, kold = self.min_state()
         self.tag[x] = 'Closed'
         self.V.add(x)
         if x == None: return -1
-        if kold < self.h[x]:  # raised states
-            for y in children(self, x):
-                a = self.h[y] + cost(self, y, x)
+        if kold < self.h[x]: # raised states
+            for y in children(self,x):
+                a = self.h[y] + cost(self,y,x)
                 if self.h[y] <= kold and self.h[x] > a:
-                    self.b[x], self.h[x] = y, a
-        elif kold == self.h[x]:  # lower
-            for y in children(self, x):
-                bb = self.h[x] + cost(self, x, y)
+                    self.b[x], self.h[x] = y , a
+        if kold == self.h[x]:# lower
+            for y in children(self,x):
+                bb = self.h[x] + cost(self,x,y)
                 if self.tag[y] == 'New' or \
-                        (self.b[y] == x and self.h[y] != bb) or \
-                        (self.b[y] != x and self.h[y] > bb):
+                    (self.b[y] == x and self.h[y] != bb) or \
+                    (self.b[y] != x and self.h[y] > bb):
                     self.b[y] = x
                     self.insert(y, bb)
-        else:
-            for y in children(self, x):
-                bb = self.h[x] + cost(self, x, y)
+        else: 
+            for y in children(self,x):
+                bb = self.h[x] + cost(self,x,y)
                 if self.tag[y] == 'New' or \
-                        (self.b[y] == x and self.h[y] != bb):
+                    (self.b[y] == x and self.h[y] != bb):
                     self.b[y] = x
                     self.insert(y, bb)
                 else:
@@ -114,28 +125,28 @@ class D_star(object):
                         self.insert(x, self.h[x])
                     else:
                         if self.b[y] != x and self.h[y] > bb and \
-                                self.tag[y] == 'Closed' and self.h[y] == kold:
+                            self.tag[y] == 'Closed' and self.h[y] == kold:
                             self.insert(y, self.h[y])
         return self.get_kmin()
 
-    def modify_cost(self, x, y, cval):
+    def modify_cost(self,x):
         # TODO: implement own function
         # self.c[x][y] = cval
-        # if self.tag[x] == 'Closed': self.insert(x,self.h[x])
+        xparent = self.b[x]
+        if self.tag[x] == 'Closed': self.insert(x , self.h[xparent] + cost(self,x,xparent))
         # return self.get_kmin()
-        pass
 
     def modify(self, x):
+        self.modify_cost(x)
         while True:
             kmin = self.process_state()
             if kmin >= self.h[x]: break
 
-    def path(self, goal=None):
+    def path(self, goal = None):
         path = []
         if not goal:
             x = self.x0
-        else:
-            x = goal
+        else: x = goal
         start = self.xt
         while x != start:
             path.append([np.array(x), np.array(self.b[x])])
@@ -147,7 +158,7 @@ class D_star(object):
         self.OPEN[self.xt] = 0
         # first run
         while True:
-            # TODO: self.x0 =
+            #TODO: self.x0 = 
             self.process_state()
             visualization(self)
             if self.tag[self.x0] == "Closed":
@@ -156,21 +167,32 @@ class D_star(object):
         self.Path = self.path()
         self.done = True
         visualization(self)
+        plt.pause(0.5)
         # plt.show()
         # when the environemnt changes over time
         s = tuple(self.env.start)
         while s != self.xt:
+            
             if s == tuple(self.env.start):
-                s = self.b[self.x0]
-            else:
-                s = self.b[s]
-            # self.modify(s)
-            self.env.move_block(a=[0, 0, -0.1], s=0.5, block_to_move=1, mode='translation')
-            self.Path = self.path(s)
-            visualization(self)
+                sparent = self.b[self.x0]
+            else: 
+                sparent = self.b[s]
+            
+            self.env.move_block(a=[0,0,-0.1],s=0.5,block_to_move=1,mode='translation')
+            # self.update_obs()
+            print('updated')
+            if cost(self,s, sparent) == np.inf:
+                self.modify(s)
+            print('modified')
             self.ind += 1
+        
+        self.Path = self.path()
+        visualization(self)
+        plt.show()
+            
+        
 
-
+        
 if __name__ == '__main__':
     D = D_star(1)
-    D.run()
+    D.run()