def run(self, Reversed = True, xrobot = None):
if Reversed:
if xrobot is None:
self.x0 = tuple(self.env.goal)
self.xt = tuple(self.env.start)
else:
self.x0 = tuple(self.env.goal)
self.xt = xrobot
xnew = self.env.goal
else:
xnew = self.env.start
self.V.append(self.x0)
while self.ind < self.maxiter:
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
if self.Flag is not None:
self.Flag[xnew] = 'Valid'
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))
if self.Flag is not None:
self.Flag[self.xt] = 'Valid'
break
# visualization(self)
self.i += 1
self.ind += 1
def Sample(self, xstart, xgoal, cmax, bias=0.05):
# sample within a eclipse
if cmax < np.inf:
cmin = getDist(xgoal, xstart)
xcenter = np.array([(xgoal[0] + xstart[0]) / 2,
(xgoal[1] + xstart[1]) / 2,
(xgoal[2] + xstart[2]) / 2])
C = self.RotationToWorldFrame(xstart, xgoal)
r = np.zeros(3)
r[0] = cmax / 2
for i in range(1, 3):
r[i] = np.sqrt(cmax**2 - cmin**2) / 2
L = np.diag(r) # R3*3
xball = self.SampleUnitBall() # np.array
x = C @ L @ xball + xcenter
self.C = C # save to global var
self.xcenter = xcenter
self.L = L
if not isinside(self, x): # intersection with the state space
xrand = x
else:
return self.Sample(xstart, xgoal, cmax)
else:
xrand = sampleFree(self, bias=bias)
return xrand
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 Sample(self, m, cmax, bias=0.05, xrand=set()):
# sample within a eclipse
print('new sample')
if cmax < np.inf:
cmin = getDist(self.xgoal, self.xstart)
xcenter = np.array([(self.xgoal[0] + self.xstart[0]) / 2,
(self.xgoal[1] + self.xstart[1]) / 2,
(self.xgoal[2] + self.xstart[2]) / 2])
C = self.RotationToWorldFrame(self.xstart, self.xgoal)
r = np.zeros(3)
r[0] = cmax / 2
for i in range(1, 3):
r[i] = np.sqrt(cmax**2 - cmin**2) / 2
L = np.diag(r) # R3*3
xball = self.SampleUnitBall(m) # np.array
x = (C @ L @ xball).T + repmat(xcenter, len(xball.T), 1)
# x2 = set(map(tuple, x))
self.C = C # save to global var
self.xcenter = xcenter
self.L = L
x2 = set(
map(
tuple, x[np.array([
not isinside(self, state)
and isinbound(self.env.boundary, state) for state in x
])])) # intersection with the state space
xrand.update(x2)
# if there are samples inside obstacle: recursion
if len(x2) < m:
return self.Sample(m - len(x2), cmax, bias=bias, xrand=xrand)
else:
for i in range(m):
xrand.add(tuple(sampleFree(self, bias=bias)))
return xrand
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 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 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()
def RandomState(self):
# generate a random, obstacle free state
xrand = sampleFree(self, bias=0)
return xrand
def RANDOM_CONFIG(self):
return tuple(sampleFree(self))
def generateSampleSet(self, n):
V = set()
for i in range(n):
V.add(tuple(sampleFree(self, bias=0.0)))
return V