def updatecost(self, range_changed=None):
# TODO: update cost when the environment is changed
# chaged nodes
CHANGED = set()
for xi in self.CLOSED:
oldchildren = self.CHILDREN[xi] # A
# if you don't know where the change occured:
if range_changed is None:
newchildren = set(children(self, xi)) # B
added = newchildren.difference(oldchildren) # B-A
removed = oldchildren.difference(newchildren) # A-B
self.CHILDREN[xi] = newchildren
if added or removed:
CHANGED.add(xi)
for xj in removed:
self.COST[xi][xj] = cost(self, xi, xj)
for xj in added:
self.COST[xi][xj] = cost(self, xi, xj)
# if you do know where on the map changed, only update those changed around that area
else:
if isinbound(range_changed, xi):
newchildren = set(children(self, xi)) # B
added = newchildren.difference(oldchildren) # B-A
removed = oldchildren.difference(newchildren) # A-B
self.CHILDREN[xi] = newchildren
if added or removed:
CHANGED.add(xi)
for xj in removed:
self.COST[xi][xj] = cost(self, xi, xj)
for xj in added:
self.COST[xi][xj] = cost(self, xi, xj)
return CHANGED
def updatecost(self, range_changed=None, new=None, old=None, mode=False):
# scan graph for changed cost, if cost is changed update it
CHANGED = set()
for xi in self.CLOSED:
if xi in self.CHILDREN:
oldchildren = self.CHILDREN[xi] # A
if isinbound(old, xi, mode) or isinbound(new, xi, mode):
newchildren = set(children(self, xi)) # B
removed = oldchildren.difference(newchildren)
intersection = oldchildren.intersection(newchildren)
added = newchildren.difference(oldchildren)
self.CHILDREN[xi] = newchildren
for xj in removed:
self.COST[xi][xj] = cost(self, xi, xj)
for xj in intersection.union(added):
self.COST[xi][xj] = cost(self, xi, xj)
CHANGED.add(xi)
else:
if isinbound(old, xi, mode) or isinbound(new, xi, mode):
CHANGED.add(xi)
children_added = set(children(self, xi))
self.CHILDREN[xi] = children_added
for xj in children_added:
self.COST[xi][xj] = cost(self, xi, xj)
return CHANGED
def getcost(self, xi, xj):
# use a LUT for getting the costd
if xi not in self.COST:
for (xj, xjcost) in children(self, xi, settings=1):
self.COST[xi][xj] = cost(self, xi, xj, xjcost)
# this might happen when there is a node changed.
if xj not in self.COST[xi]:
self.COST[xi][xj] = cost(self, xi, xj)
return self.COST[xi][xj]
def process_state(self):
# main function of the D star algorithm, perform the process state
# around the old path when needed.
x, kold = self.min_state()
self.tag[x] = 'Closed'
self.V.add(x)
if x is None:
return -1
# check if 1st timer x
self.checkState(x)
if kold < self.h[x]: # raised states
for y in children(self, x):
# check y
self.checkState(y)
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
if kold == self.h[x]: # lower
for y in children(self, x):
# check y
self.checkState(y)
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
self.insert(y, bb)
else:
for y in children(self, x):
# check y
self.checkState(y)
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
self.insert(y, bb)
else:
if self.b[y] != x and self.h[y] > bb:
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.insert(y, self.h[y])
return self.get_kmin()
def updatecost(self, range_changed=None, new=None, old=None, mode=False):
# scan graph for changed Cost, if Cost is changed update it
CHANGED = set()
for xi in self.CLOSED:
if isinbound(old, xi, mode) or isinbound(new, xi, mode):
newchildren = set(children(self, xi)) # B
self.CHILDREN[xi] = newchildren
for xj in newchildren:
self.COST[xi][xj] = cost(self, xi, xj)
CHANGED.add(xi)
return CHANGED
def run(self):
x0, xt = self.start, self.goal
self.OPEN1.put(x0, self.g[x0] + heuristic_fun(self,x0,xt)) # item, priority = g + h
self.OPEN2.put(xt, self.g[xt] + heuristic_fun(self,xt,x0)) # item, priority = g + h
self.ind = 0
while not self.CLOSED1.intersection(self.CLOSED2): # while xt not reached and open is not empty
xi1, xi2 = self.OPEN1.get(), self.OPEN2.get()
self.CLOSED1.add(xi1) # add the point in CLOSED set
self.CLOSED2.add(xi2)
self.V.append(xi1)
self.V.append(xi2)
# visualization(self)
allchild1, allchild2 = children(self,xi1), children(self,xi2)
self.evaluation(allchild1,xi1,conf=1)
self.evaluation(allchild2,xi2,conf=2)
if self.ind % 100 == 0: print('iteration number = '+ str(self.ind))
self.ind += 1
self.common = self.CLOSED1.intersection(self.CLOSED2)
self.done = True
self.Path = self.path()
visualization(self)
plt.show()
def updatecost(self, range_changed=None, new=None, old=None, mode=False):
# scan graph for changed Cost, if Cost is changed update it
CHANGED = set()
for xi in self.CLOSED:
if self.isinobs(old, xi, mode) or self.isinobs(new, xi, mode):
# if self.isinobs(new, xi, mode):
self.V.remove(xi)
# self.V.difference_update({i for i in children(self, xi)})
newchildren = set(children(self, xi)) # B
self.CHILDREN[xi] = newchildren
for xj in newchildren:
self.COST[xi][xj] = cost(self, xi, xj)
CHANGED.add(xi)
return CHANGED
def run(self, N=None):
xt = self.xt
xi = self.x0
while self.OPEN: # while xt not reached and open is not empty
xi = self.OPEN.get()
if xi not in self.CLOSED:
self.V.append(np.array(xi))
self.CLOSED.add(xi) # add the point in CLOSED set
if getDist(xi, xt) < self.env.resolution:
break
# visualization(self)
for xj in children(self, xi):
# if xj not in self.CLOSED:
if xj not in self.g:
self.g[xj] = np.inf
else:
pass
a = self.g[xi] + cost(self, xi, xj)
if a < self.g[xj]:
self.g[xj] = a
self.Parent[xj] = xi
# if (a, xj) in self.OPEN.enumerate():
# update priority of xj
self.OPEN.put(xj, a + 1 * heuristic_fun(self, xj))
# else:
# add xj in to OPEN set
# self.OPEN.put(xj, a + 1 * heuristic_fun(self, xj))
# For specified expanded nodes, used primarily in LRTA*
if N:
if len(self.CLOSED) % N == 0:
break
if self.ind % 100 == 0:
print('number node expanded = ' + str(len(self.V)))
self.ind += 1
self.lastpoint = xi
# if the path finding is finished
if self.lastpoint in self.CLOSED:
self.done = True
self.Path = self.path()
if N is None:
#visualization(self)
plt.show()
return True
return False
def move(self):
st, localhvals = self.st, self.localhvals
maxhval = max(localhvals)
sthval = self.Astar.h[st]
# find the lowest path up hill
while sthval < maxhval:
parentsvals , parents = [] , []
# find the max child
for xi in children(self.Astar,st):
if xi in self.Astar.CLOSED:
parents.append(xi)
parentsvals.append(self.Astar.h[xi])
lastst = st
st = parents[np.argmax(parentsvals)]
self.path.append([st,lastst]) # add to path
sthval = self.Astar.h[st]
self.Astar.reset(self.st)
def updateHeuristic(self):
# Initialize hvalues at infinity
for xi in self.Astar.CLOSED:
self.Astar.h[xi] = np.inf
Diff = True
while Diff: # repeat DP until converge
hvals, lasthvals = [], []
for xi in self.Astar.CLOSED:
lasthvals.append(self.Astar.h[xi])
# update h values if they are smaller
Children = children(self.Astar, xi)
minfval = min([
cost(self.Astar, xi, xj, settings=0) + self.Astar.h[xj]
for xj in Children
])
# h(s) = h(s') if h(s) > c(s,s') + h(s')
if self.Astar.h[xi] >= minfval:
self.Astar.h[xi] = minfval
hvals.append(self.Astar.h[xi])
if lasthvals == hvals: Diff = False
def move(self):
st = self.Astar.x0
ind = 0
# find the lowest path down hill
while st in self.Astar.CLOSED: # when minchild in CLOSED then continue, when minchild in OPEN, stop
Children = children(self.Astar, st)
minh, minchild = np.inf, None
for child in Children:
# check collision here, not a supper efficient
collide, _ = isCollide(self.Astar, st, child)
if collide:
continue
h = self.Astar.h[child]
if h <= minh:
minh, minchild = h, child
self.path.append([st, minchild])
st = minchild
for (_, p) in self.Astar.OPEN.enumerate():
if p == st:
break
ind += 1
if ind > 1000:
break
self.Astar.reset(st)
def getchildren(self, xi):
if xi not in self.CHILDREN:
allchild = children(self, xi)
self.CHILDREN[xi] = set(allchild)
return self.CHILDREN[xi]
def main(self):
s_last = self.x0
print('first run ...')
self.ComputeShortestPath()
self.Path = self.path()
self.done = True
visualization(self)
plt.pause(0.5)
# plt.show()
print('running with map update ...')
t = 0 # count time
ischanged = False
self.V = set()
while getDist(self.x0, self.xt) > 2 * self.env.resolution:
#---------------------------------- at specific times, the environment is changed and Cost is updated
if t % 2 == 0:
new0, old0 = self.env.move_block(a=[-0.1, 0, -0.2],
s=0.5,
block_to_move=1,
mode='translation')
new1, old1 = self.env.move_block(a=[0, 0, -0.2],
s=0.5,
block_to_move=0,
mode='translation')
new2, old2 = self.env.move_block(theta=[0, 0, 0.1 * t],
mode='rotation')
#new2,old2 = self.env.move_block(a=[-0.3, 0, -0.1], s=0.5, block_to_move=1, mode='translation')
ischanged = True
self.Path = []
#----------------------------------- traverse the route as originally planned
if t == 0:
children_new = [
i for i in self.CLOSED
if getDist(self.x0, i) <= self.env.resolution * np.sqrt(3)
]
else:
children_new = list(children(self, self.x0))
self.x0 = children_new[np.argmin([
self.getcost(self.x0, s_p) + self.getg(s_p)
for s_p in children_new
])]
# TODO add the moving robot position codes
self.env.start = self.x0
# ---------------------------------- if any Cost changed, update km, reset slast,
# for all directed edgees (u,v) with chaged edge costs,
# update the edge Cost cBest(u,v) and update vertex u. then replan
if ischanged:
self.km += heuristic_fun(self, self.x0, s_last)
s_last = self.x0
CHANGED = self.updatecost(True, new0, old0)
CHANGED1 = self.updatecost(True, new1, old1)
CHANGED2 = self.updatecost(True, new2, old2, mode='obb')
CHANGED = CHANGED.union(CHANGED1, CHANGED2)
# self.V = set()
for u in CHANGED:
self.UpdateVertex(u)
self.ComputeShortestPath()
ischanged = False
self.Path = self.path(self.x0)
visualization(self)
t += 1
plt.show()
