def run(self):
self.V.append(tuple(self.env.start))
self.ind = 0
self.fig = plt.figure(figsize=(10, 8))
xnew = self.env.start
while self.ind < self.maxiter and getDist(
xnew, self.env.goal) > self.stepsize:
xrand = sampleFree(self)
xnearest = nearest(self, xrand)
xnew = steer(self, xnearest, xrand)
collide, _ = isCollide(self, xnearest, xnew)
if not collide:
self.V.append(xnew) # add point
self.wireup(xnew, xnearest)
if getDist(xnew, self.env.goal) <= self.stepsize:
goal = tuple(self.env.goal)
self.wireup(goal, xnew)
self.Path, D = path(self)
print('Total distance = ' + str(D))
# visualization(self)
self.i += 1
self.ind += 1
# if the goal is really reached
self.done = True
visualization(self)
plt.show()
def run(self):
xnew = self.x0
print('start rrt*... ')
self.fig = plt.figure(figsize=(10, 8))
while self.ind < self.maxiter:
xrand = sampleFree(self)
xnearest = nearest(self, xrand)
xnew, dist = steer(self, xnearest, xrand)
collide, _ = isCollide(self, xnearest, xnew, dist=dist)
if not collide:
Xnear = near(self, xnew)
self.V.append(xnew) # add point
# visualization(self)
# minimal path and minimal cost
xmin, cmin = xnearest, cost(self, xnearest) + getDist(
xnearest, xnew)
# connecting along minimal cost path
Collide = []
for xnear in Xnear:
xnear = tuple(xnear)
c1 = cost(self, xnear) + getDist(xnew, xnear)
collide, _ = isCollide(self, xnew, xnear)
Collide.append(collide)
if not collide and c1 < cmin:
xmin, cmin = xnear, c1
self.wireup(xnew, xmin)
# rewire
for i in range(len(Xnear)):
collide = Collide[i]
xnear = tuple(Xnear[i])
c2 = cost(self, xnew) + getDist(xnew, xnear)
if not collide and c2 < cost(self, xnear):
# self.removewire(xnear)
self.wireup(xnear, xnew)
self.i += 1
self.ind += 1
# max sample reached
self.reached()
print('time used = ' + str(time.time() - starttime))
print('Total distance = ' + str(self.D))
visualization(self)
plt.show()
def run(self):
self.V.append(self.env.start)
self.ind = 0
self.fig = plt.figure(figsize=(10, 8))
xnew = self.env.start
while self.ind < self.maxiter and getDist(xnew, self.env.goal) > 1:
xrand = sampleFree(self)
xnearest = nearest(self, xrand)
xnew = steer(self, xnearest, xrand)
if not isCollide(self, xnearest, xnew):
self.V.append(xnew) # add point
self.wireup(xnew, xnearest)
visualization(self)
self.i += 1
self.ind += 1
if getDist(xnew, self.env.goal) <= 1:
self.wireup(self.env.goal, xnew)
self.Path, D = path(self)
print('Total distance = ' + str(D))
self.done = True
visualization(self)
plt.show()
def Main(self):
qgoal = tuple(self.env.start)
qstart = tuple(self.env.goal)
self.GrowRRT()
self.done = True
# visualization(self)
self.done = False
# change the enviroment
new0,old0 = self.env.move_block(a=[0, 0, -2], s=0.5, block_to_move=1, mode='translation')
while qgoal != qstart:
qgoal = self.Parent[qgoal]
# TODO move to qgoal and check for new obs
xrobot = qstart
# TODO if any new obstacle are observed
self.InvalidateNodes(new0, mode = 'translation')
for xi in self.Path:
if self.Flag[tuple(xi[0])] == 'Invalid':
self.RegrowRRT(tuple(self.env.start))
self.done = True
self.Path, D = path(self)
visualization(self)
plt.show()
def run(self):
self.V.append(self.env.start)
self.ind = 0
xnew = self.env.start
print('start rrt*... ')
self.fig = plt.figure(figsize=(10, 8))
while self.ind < self.maxiter:
xrand = sampleFree(self)
xnearest = nearest(self, xrand)
xnew = steer(self, xnearest, xrand)
if not isCollide(self, xnearest, xnew):
Xnear = near(self, xnew)
self.V.append(xnew) # add point
visualization(self)
# minimal path and minimal cost
xmin, cmin = xnearest, cost(self, xnearest) + getDist(
xnearest, xnew)
# connecting along minimal cost path
for xnear in Xnear:
c1 = cost(self, xnear) + getDist(xnew, xnear)
if not isCollide(self, xnew, xnear) and c1 < cmin:
xmin, cmin = xnear, c1
self.wireup(xnew, xmin)
# rewire
for xnear in Xnear:
c2 = cost(self, xnew) + getDist(xnew, xnear)
if not isCollide(self, xnew,
xnear) and c2 < cost(self, xnear):
self.removewire(xnear)
self.wireup(xnear, xnew)
self.i += 1
self.ind += 1
# max sample reached
self.reached()
print('time used = ' + str(time.time() - starttime))
print('Total distance = ' + str(self.D))
visualization(self)
plt.show()
def RRTplan(self, env, initial, goal):
threshold = self.stepsize
nearest = initial # state structure
self.V.append(initial)
rrt_tree = initial # TODO KDtree structure
while self.ind <= self.maxiter:
target = self.ChooseTarget(goal)
nearest = self.Nearest(rrt_tree, target)
extended, collide = self.Extend(env, nearest, target)
if not collide:
self.AddNode(rrt_tree, nearest, extended)
if getDist(nearest, goal) <= threshold:
self.AddNode(rrt_tree, nearest, self.xt)
break
self.i += 1
self.ind += 1
visualization(self)
# return rrt_tree
self.done = True
self.Path, D = path(self)
visualization(self)
plt.show()
def run(self):
self.V.append(self.x0)
while self.ind < self.maxiter:
xrand = sampleFree(self)
xnearest = nearest(self, xrand)
xnew, dist = steer(self, xnearest, xrand)
collide, _ = isCollide(self, xnearest, xnew, dist=dist)
if not collide:
self.V.append(xnew) # add point
self.wireup(xnew, xnearest)
if getDist(xnew, self.xt) <= self.stepsize:
self.wireup(self.xt, xnew)
self.Path, D = path(self)
print('Total distance = ' + str(D))
break
# visualization(self)
self.i += 1
self.ind += 1
# if the goal is really reached
self.done = True
visualization(self)
plt.show()
