mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
Merge branch 'master' of https://github.com/zhm-real/path-planning-algorithms
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zhm-real
@@ -8,32 +8,43 @@ from collections import defaultdict
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../Search-based Planning/")
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from Search_3D.env3D import env
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from Search_3D import Astar3D
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from Search_3D.utils3D import StateSpace, getDist, getNearest, getRay, isinbound, isinball, isCollide, children, cost, \
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initcost
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from Search_3D.utils3D import StateSpace, getDist, getNearest, getRay, isinbound, isinball, isCollide, children, cost, initcost
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from Search_3D.plot_util3D import visualization
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class D_star(object):
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def __init__(self, resolution=1):
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def __init__(self,resolution = 1):
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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],
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[-1, 0, 0], [0, -1, 0], [0, 0, -1], [-1, -1, 0], [-1, 0, -1], [0, -1, -1],
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[-1, -1, -1],
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[1, -1, 0], [-1, 1, 0], [1, 0, -1], [-1, 0, 1], [0, 1, -1], [0, -1, 1],
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[1, -1, -1], [-1, 1, -1], [-1, -1, 1], [1, 1, -1], [1, -1, 1], [-1, 1, 1]])
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self.env = env(resolution=resolution)
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self.env = env(resolution = resolution)
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self.X = StateSpace(self.env)
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self.x0, self.xt = getNearest(self.X, self.env.start), getNearest(self.X, self.env.goal)
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self.b = defaultdict(lambda: defaultdict(dict)) # back pointers every state has one except xt.
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self.OPEN = {} # OPEN list, here use a hashmap implementation. hash is point, key is value
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self.h = self.initH() # estimate from a point to the end point
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self.tag = self.initTag() # set all states to new
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self.V = set() # vertice in closed
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self.b = defaultdict(lambda: defaultdict(dict))# back pointers every state has one except xt.
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self.OPEN = {} # OPEN list, here use a hashmap implementation. hash is point, key is value
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self.h = self.initH() # estimate from a point to the end point
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self.tag = self.initTag() # set all states to new
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self.V = set()# vertice in closed
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# initialize cost set
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# self.c = initcost(self)
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# for visualization
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self.ind = 0
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self.Path = []
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self.done = False
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self.Obstaclemap = {}
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def update_obs(self):
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for xi in self.X:
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print('xi')
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self.Obstaclemap[xi] = False
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for aabb in self.env.blocks:
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self.Obstaclemap[xi] = isinbound(aabb, xi)
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if self.Obstaclemap[xi] == False:
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for ball in self.env.balls:
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self.Obstaclemap[xi] = isinball(ball, xi)
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def initH(self):
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# h set, all initialzed h vals are 0 for all states.
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@@ -46,7 +57,7 @@ class D_star(object):
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# tag , New point (never been in the OPEN list)
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# Open point ( currently in OPEN )
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# Closed (currently in CLOSED)
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t = {}
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t = {}
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for xi in self.X:
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t[xi] = 'New'
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return t
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@@ -56,7 +67,7 @@ class D_star(object):
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# -1 if it does not exist
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if self.OPEN:
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minv = np.inf
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for v, k in enumerate(self.OPEN):
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for v,k in enumerate(self.OPEN):
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if v < minv: minv = v
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return minv
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return -1
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@@ -67,11 +78,11 @@ class D_star(object):
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# it also removes this min value form the OPEN set.
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if self.OPEN:
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minv = np.inf
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for v, k in enumerate(self.OPEN):
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for v,k in enumerate(self.OPEN):
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if v < minv: mink, minv = k, v
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return mink, self.OPEN.pop(mink)
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return None, -1
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def insert(self, x, h_new):
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# inserting a key and value into OPEN list (x, kx)
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# depending on following situations
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@@ -82,31 +93,31 @@ class D_star(object):
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if self.tag[x] == 'Closed':
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kx = min(self.h[x], h_new)
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self.OPEN[x] = kx
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self.h[x], self.tag[x] = h_new, 'Open'
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self.h[x],self.tag[x] = h_new, 'Open'
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def process_state(self):
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x, kold = self.min_state()
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self.tag[x] = 'Closed'
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self.V.add(x)
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if x == None: return -1
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if kold < self.h[x]: # raised states
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for y in children(self, x):
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a = self.h[y] + cost(self, y, x)
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if kold < self.h[x]: # raised states
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for y in children(self,x):
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a = self.h[y] + cost(self,y,x)
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if self.h[y] <= kold and self.h[x] > a:
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self.b[x], self.h[x] = y, a
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elif kold == self.h[x]: # lower
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for y in children(self, x):
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bb = self.h[x] + cost(self, x, y)
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self.b[x], self.h[x] = y , a
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if kold == self.h[x]:# lower
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for y in children(self,x):
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bb = self.h[x] + cost(self,x,y)
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if self.tag[y] == 'New' or \
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(self.b[y] == x and self.h[y] != bb) or \
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(self.b[y] != x and self.h[y] > bb):
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(self.b[y] == x and self.h[y] != bb) or \
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(self.b[y] != x and self.h[y] > bb):
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self.b[y] = x
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self.insert(y, bb)
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else:
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for y in children(self, x):
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bb = self.h[x] + cost(self, x, y)
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else:
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for y in children(self,x):
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bb = self.h[x] + cost(self,x,y)
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if self.tag[y] == 'New' or \
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(self.b[y] == x and self.h[y] != bb):
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(self.b[y] == x and self.h[y] != bb):
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self.b[y] = x
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self.insert(y, bb)
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else:
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@@ -114,28 +125,28 @@ class D_star(object):
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self.insert(x, self.h[x])
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else:
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if self.b[y] != x and self.h[y] > bb and \
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self.tag[y] == 'Closed' and self.h[y] == kold:
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self.tag[y] == 'Closed' and self.h[y] == kold:
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self.insert(y, self.h[y])
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return self.get_kmin()
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def modify_cost(self, x, y, cval):
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def modify_cost(self,x):
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# TODO: implement own function
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# self.c[x][y] = cval
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# if self.tag[x] == 'Closed': self.insert(x,self.h[x])
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xparent = self.b[x]
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if self.tag[x] == 'Closed': self.insert(x , self.h[xparent] + cost(self,x,xparent))
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# return self.get_kmin()
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pass
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def modify(self, x):
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self.modify_cost(x)
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while True:
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kmin = self.process_state()
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if kmin >= self.h[x]: break
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def path(self, goal=None):
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def path(self, goal = None):
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path = []
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if not goal:
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x = self.x0
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else:
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x = goal
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else: x = goal
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start = self.xt
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while x != start:
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path.append([np.array(x), np.array(self.b[x])])
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@@ -147,7 +158,7 @@ class D_star(object):
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self.OPEN[self.xt] = 0
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# first run
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while True:
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# TODO: self.x0 =
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#TODO: self.x0 =
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self.process_state()
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visualization(self)
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if self.tag[self.x0] == "Closed":
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@@ -156,21 +167,32 @@ class D_star(object):
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self.Path = self.path()
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self.done = True
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visualization(self)
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plt.pause(0.5)
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# plt.show()
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# when the environemnt changes over time
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s = tuple(self.env.start)
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while s != self.xt:
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if s == tuple(self.env.start):
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s = self.b[self.x0]
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else:
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s = self.b[s]
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# self.modify(s)
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self.env.move_block(a=[0, 0, -0.1], s=0.5, block_to_move=1, mode='translation')
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self.Path = self.path(s)
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visualization(self)
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sparent = self.b[self.x0]
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else:
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sparent = self.b[s]
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self.env.move_block(a=[0,0,-0.1],s=0.5,block_to_move=1,mode='translation')
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# self.update_obs()
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print('updated')
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if cost(self,s, sparent) == np.inf:
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self.modify(s)
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print('modified')
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self.ind += 1
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self.Path = self.path()
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visualization(self)
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plt.show()
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if __name__ == '__main__':
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D = D_star(1)
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D.run()
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D.run()
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