def path(self, s_start=None):
'''After ComputeShortestPath()
returns, one can then follow a shortest path from x_init to
x_goal by always moving from the current vertex s, starting
at x_init. , to any successor s' that minimizes cBest(s,s') + g(s')
until x_goal is reached (ties can be broken arbitrarily).'''
path = []
s_goal = self.xt
s = self.x0
ind = 0
while getDist(s, s_goal) > self.env.resolution:
if s == self.x0:
children = [
i for i in self.CLOSED
if getDist(s, i) <= self.env.resolution * np.sqrt(3)
]
else:
children = list(self.CHILDREN[s])
snext = children[np.argmin(
[self.getcost(s, s_p) + self.getg(s_p) for s_p in children])]
path.append([s, snext])
s = snext
if ind > 100:
break
ind += 1
return path
def main(self):
s_last = self.x0
s_start = self.x0
print('first run ...')
self.ComputeShortestPath()
self.Path = self.path()
self.done = True
visualization(self)
plt.pause(0.5)
# plt.show()
# change the environment
print('running with map update ...')
for i in range(100):
range_changed1 = self.env.move_block(a=[0, 0, -0.1],
s=0.5,
block_to_move=0,
mode='translation')
range_changed2 = self.env.move_block(a=[0.1, 0, 0],
s=0.5,
block_to_move=1,
mode='translation')
range_changed3 = self.env.move_block(a=[0, 0.1, 0],
s=0.5,
block_to_move=2,
mode='translation')
#range_changed = self.env.move_block(a=[0.1, 0, 0], s=0.5, block_to_move=1, mode='translation')
# update the edge cost of c(u,v)
CHANGED1 = self.updatecost(range_changed1)
CHANGED2 = self.updatecost(range_changed2)
CHANGED3 = self.updatecost(range_changed3)
CHANGED2 = CHANGED2.union(CHANGED1)
CHANGED = CHANGED3.union(CHANGED2)
while getDist(s_start, self.xt) > 2 * self.env.resolution:
if s_start == self.x0:
children = [
i for i in self.CLOSED
if getDist(s_start, i) <= self.env.resolution *
np.sqrt(3)
]
else:
children = list(self.CHILDREN[s_start])
s_start = children[np.argmin([
cost(self, s_start, s_p) + self.g[s_p] for s_p in children
])]
# for all directed edges (u,v) with changed costs
if CHANGED:
self.km = self.km + heuristic_fun(self, s_start, s_last)
for u in CHANGED:
self.UpdateVertex(u)
s_last = s_start
self.ComputeShortestPath()
self.Path = self.path()
visualization(self)
plt.show()
def isCollide(self, x, child):
ray, dist = getRay(x, child), getDist(x, child)
if not isinbound(self.env.boundary, child):
return True, dist
for i in self.env.AABB_pyrr:
shot = pyrr.geometric_tests.ray_intersect_aabb(ray, i)
if shot is not None:
dist_wall = getDist(x, shot)
if dist_wall <= dist: # collide
return True, dist
for i in self.env.balls:
if isinball(i, child):
return True, dist
shot = pyrr.geometric_tests.ray_intersect_sphere(ray, i)
if shot != []:
dists_ball = [getDist(x, j) for j in shot]
if all(dists_ball <= dist): # collide
return True, dist
return False, dist
def UpdateVertex(self, u):
# if still in the hunt
if not getDist(self.xt,
u) <= self.env.resolution: # originally: u != s_goal
self.rhs[u] = min([
self.getcost(s, u) + self.getg(s) for s in self.getchildren(u)
])
# if u is in OPEN, remove it
self.OPEN.check_remove(u)
# if rhs(u) not equal to g(u)
if self.getg(u) != self.getrhs(u):
self.OPEN.put(u, self.CalculateKey(u))
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 ComputeorImprovePath(self):
while self.OPEN.top_key() < self.key(self.x0, self.epsilon) or self.rhs[self.x0] != self.g[self.x0]:
s = self.OPEN.get()
if getDist(s, tuple(self.env.start)) < self.env.resolution:
break
if self.g[s] > self.rhs[s]:
self.g[s] = self.rhs[s]
self.CLOSED.add(s)
self.V.add(s)
for s_p in self.getchildren(s):
self.UpdateState(s_p)
else:
self.g[s] = np.inf
self.UpdateState(s)
for s_p in self.getchildren(s):
self.UpdateState(s_p)
self.ind += 1
def ComputeShortestPath(self):
while self.OPEN.top_key() < self.CalculateKey(self.x0) or self.getrhs(
self.x0) != self.getg(self.x0):
kold = self.OPEN.top_key()
u = self.OPEN.get()
self.V.add(u)
self.CLOSED.add(u)
if getDist(self.x0, u) <= self.env.resolution:
break
# visualization(self)
if kold < self.CalculateKey(u):
self.OPEN.put(u, self.CalculateKey(u))
if self.getg(u) > self.getrhs(u):
self.g[u] = self.rhs[u]
else:
self.g[u] = np.inf
self.UpdateVertex(u)
for s in self.getchildren(u):
self.UpdateVertex(s)
self.ind += 1
def ComputeShortestPath(self):
while self.OPEN.top_key() < self.CalculateKey(self.x0) or self.getrhs(
self.x0) != self.getg(self.x0):
kold = self.OPEN.top_key()
u = self.OPEN.get()
self.V.add(u)
self.CLOSED.add(u)
if not self.done: # first time running, we need to stop on this condition
if getDist(self.x0, u) < 1 * self.env.resolution:
self.x0 = u
break
if kold < self.CalculateKey(u):
self.OPEN.put(u, self.CalculateKey(u))
if self.getg(u) > self.getrhs(u):
self.g[u] = self.rhs[u]
else:
self.g[u] = np.inf
self.UpdateVertex(u)
for s in self.getchildren(u):
self.UpdateVertex(s)
# visualization(self)
self.ind += 1
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()
