mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-13 03:28:04 -05:00
fix bug and huge refactoring
This commit is contained in:
Atsushi Sakai
@@ -14,190 +14,191 @@ import math
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show_animation = True
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class Node:
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class AStarPlanner:
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def __init__(self, x, y, cost, pind):
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self.x = x # index of grid
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self.y = y # index of grid
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self.cost = cost
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self.pind = pind
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def __init__(self, ox, oy, reso, rr):
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"""
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Intialize map for a star planning
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def __str__(self):
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return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
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def a_star_planning(sx, sy, gx, gy, ox, oy, reso, rr):
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"""
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A star path search
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input:
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sx: start x position [m]
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sy: start y position [m]
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gx: goal x position [m]
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gx: goal x position [m]
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ox: x position list of Obstacles [m]
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oy: y position list of Obstacles [m]
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reso: grid resolution [m]
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rr: robot radius[m]
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"""
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output:
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rx: x position list of the final path
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ry: y position list of the final path
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"""
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self.reso = reso
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self.rr = rr
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self.calc_obstacle_map(ox, oy)
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self.motion = self.get_motion_model()
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ox = [iox / reso for iox in ox]
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oy = [ioy / reso for ioy in oy]
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class Node:
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def __init__(self, x, y, cost, pind):
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self.x = x # index of grid
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self.y = y # index of grid
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self.cost = cost
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self.pind = pind
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obmap, minx, miny, maxx, maxy, xw, yw = calc_obstacle_map(ox, oy, reso, rr)
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def __str__(self):
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return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
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motion = get_motion_model()
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def planning(self, sx, sy, gx, gy):
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"""
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A star path search
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nstart = Node(calc_xyindex(sx, minx, reso), calc_xyindex(sy, minx, reso), 0.0, -1)
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ngoal = Node(calc_xyindex(gx, minx, reso), calc_xyindex(gy, minx, reso), 0.0, -1)
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input:
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sx: start x position [m]
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sy: start y position [m]
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gx: goal x position [m]
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gx: goal x position [m]
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openset, closedset = dict(), dict()
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openset[calc_index(nstart, xw, minx, miny)] = nstart
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output:
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rx: x position list of the final path
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ry: y position list of the final path
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"""
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while 1:
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c_id = min(
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openset, key=lambda o: openset[o].cost + calc_heuristic(ngoal, openset[o]))
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current = openset[c_id]
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nstart = self.Node(self.calc_xyindex(sx, self.minx),
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self.calc_xyindex(sy, self.miny), 0.0, -1)
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ngoal = self.Node(self.calc_xyindex(gx, self.minx),
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self.calc_xyindex(gy, self.miny), 0.0, -1)
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# show graph
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if show_animation: # pragma: no cover
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plt.plot(calc_position(current.x, minx, reso), calc_position(current.y, miny, reso), "xc")
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if len(closedset.keys()) % 10 == 0:
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plt.pause(0.001)
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openset, closedset = dict(), dict()
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openset[self.calc_index(nstart)] = nstart
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if current.x == ngoal.x and current.y == ngoal.y:
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print("Find goal")
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ngoal.pind = current.pind
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ngoal.cost = current.cost
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break
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while 1:
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c_id = min(
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openset, key=lambda o: openset[o].cost + self.calc_heuristic(ngoal, openset[o]))
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current = openset[c_id]
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# Remove the item from the open set
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del openset[c_id]
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# show graph
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if show_animation: # pragma: no cover
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plt.plot(self.calc_position(current.x, self.minx),
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self.calc_position(current.y, self.miny), "xc")
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if len(closedset.keys()) % 10 == 0:
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plt.pause(0.001)
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# Add it to the closed set
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closedset[c_id] = current
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if current.x == ngoal.x and current.y == ngoal.y:
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print("Find goal")
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ngoal.pind = current.pind
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ngoal.cost = current.cost
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break
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# expand search grid based on motion model
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for i, _ in enumerate(motion):
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node = Node(current.x + motion[i][0],
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current.y + motion[i][1],
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current.cost + motion[i][2], c_id)
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n_id = calc_index(node, xw, minx, miny)
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# Remove the item from the open set
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del openset[c_id]
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if n_id in closedset:
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continue
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# Add it to the closed set
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closedset[c_id] = current
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if not verify_node(node, obmap, minx, miny, maxx, maxy, reso):
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continue
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# expand search grid based on motion model
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for i, _ in enumerate(self.motion):
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node = self.Node(current.x + self.motion[i][0],
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current.y + self.motion[i][1],
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current.cost + self.motion[i][2], c_id)
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n_id = self.calc_index(node)
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if n_id not in openset:
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openset[n_id] = node # Discover a new node
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else:
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if openset[n_id].cost >= node.cost:
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# This path is the best until now. record it!
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openset[n_id] = node
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if n_id in closedset:
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continue
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rx, ry = calc_final_path(ngoal, closedset, reso, minx, miny)
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if not self.verify_node(node):
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continue
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return rx, ry
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if n_id not in openset:
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openset[n_id] = node # Discover a new node
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else:
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if openset[n_id].cost >= node.cost:
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# This path is the best until now. record it!
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openset[n_id] = node
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rx, ry = self.calc_final_path(ngoal, closedset)
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return rx, ry
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def calc_final_path(ngoal, closedset, reso, minx, miny):
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# generate final course
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rx, ry = [calc_position(ngoal.x, minx, reso)], [calc_position(ngoal.y, miny, reso)]
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pind = ngoal.pind
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while pind != -1:
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n = closedset[pind]
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rx.append(calc_position(n.x, minx, reso))
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ry.append(calc_position(n.y, miny, reso))
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pind = n.pind
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def calc_final_path(self, ngoal, closedset):
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# generate final course
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rx, ry = [self.calc_position(ngoal.x, self.minx)], [
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self.calc_position(ngoal.y, self.miny)]
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pind = ngoal.pind
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while pind != -1:
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n = closedset[pind]
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rx.append(self.calc_position(n.x, self.minx))
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ry.append(self.calc_position(n.y, self.miny))
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pind = n.pind
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return rx, ry
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return rx, ry
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def calc_heuristic(self, n1, n2):
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w = 1.0 # weight of heuristic
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d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
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return d
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def calc_position(self, index, minp):
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pos = index*self.reso+minp
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return pos
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def calc_heuristic(n1, n2):
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w = 1.0 # weight of heuristic
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d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
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return d
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def calc_xyindex(self, position, minp):
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return round((position - minp)/self.reso)
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def calc_position(index, minp, reso):
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return index*reso+minp
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def calc_index(self, node):
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return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
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def calc_xyindex(position, minp, reso):
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return round((position - minp)/reso)
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def verify_node(self, node):
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px = self.calc_position(node.x, self.minx)
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py = self.calc_position(node.y, self.miny)
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def verify_node(node, obmap, minx, miny, maxx, maxy, reso):
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if px < self.minx:
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return False
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elif py < self.miny:
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return False
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elif px >= self.maxx:
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return False
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elif py >= self.maxy:
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return False
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px = calc_position(node.x, minx, reso)
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py = calc_position(node.y, miny, reso)
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if self.obmap[node.x][node.y]:
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return False
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if px < minx:
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return False
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elif py < miny:
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return False
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elif px >= maxx:
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return False
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elif py >= maxy:
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return False
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return True
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if obmap[node.x][node.y]:
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return False
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def calc_obstacle_map(self, ox, oy):
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return True
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self.minx = round(min(ox))
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self.miny = round(min(oy))
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self.maxx = round(max(ox))
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self.maxy = round(max(oy))
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print("minx:", self.minx)
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print("miny:", self.miny)
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print("maxx:", self.maxx)
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print("maxy:", self.maxy)
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self.xwidth = round((self.maxx - self.minx)/self.reso)
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self.ywidth = round((self.maxy - self.miny)/self.reso)
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print("xwidth:", self.xwidth)
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print("ywidth:", self.ywidth)
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def calc_obstacle_map(ox, oy, reso, rr):
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# obstacle map generation
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self.obmap = [[False for i in range(self.ywidth)]
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for i in range(self.xwidth)]
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for ix in range(self.xwidth):
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x = self.calc_position(ix, self.minx)
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for iy in range(self.ywidth):
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y = self.calc_position(iy, self.miny)
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for iox, ioy in zip(ox, oy):
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d = math.sqrt((iox - x)**2 + (ioy - y)**2)
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if d <= self.rr:
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self.obmap[ix][iy] = True
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break
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minx = round(min(ox))
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miny = round(min(oy))
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maxx = round(max(ox))
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maxy = round(max(oy))
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print("minx:", minx)
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print("miny:", miny)
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print("maxx:", maxx)
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print("maxy:", maxy)
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def get_motion_model(self):
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# dx, dy, cost
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motion = [[1, 0, 1],
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[0, 1, 1],
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[-1, 0, 1],
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[0, -1, 1],
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[-1, -1, math.sqrt(2)],
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[-1, 1, math.sqrt(2)],
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[1, -1, math.sqrt(2)],
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[1, 1, math.sqrt(2)]]
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xwidth = round(maxx - minx)
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ywidth = round(maxy - miny)
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print("xwidth:", xwidth)
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print("ywidth:", ywidth)
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# obstacle map generation
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obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
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for ix in range(xwidth):
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x = ix + minx
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for iy in range(ywidth):
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y = iy + miny
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for iox, ioy in zip(ox, oy):
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d = math.sqrt((iox - x)**2 + (ioy - y)**2)
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if d <= rr:
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obmap[ix][iy] = True
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break
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return obmap, minx, miny, maxx, maxy, xwidth, ywidth
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def calc_index(node, xwidth, xmin, ymin):
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return (node.y - ymin) * xwidth + (node.x - xmin)
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def get_motion_model():
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# dx, dy, cost
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motion = [[1, 0, 1],
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[0, 1, 1],
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[-1, 0, 1],
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[0, -1, 1],
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[-1, -1, math.sqrt(2)],
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[-1, 1, math.sqrt(2)],
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[1, -1, math.sqrt(2)],
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[1, 1, math.sqrt(2)]]
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return motion
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return motion
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def main():
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@@ -208,7 +209,7 @@ def main():
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sy = 10.0 # [m]
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gx = 50.0 # [m]
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gy = 50.0 # [m]
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grid_size = 1.0 # [m]
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grid_size = 2.0 # [m]
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robot_radius = 1.0 # [m]
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# set obstable positions
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@@ -239,7 +240,8 @@ def main():
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plt.grid(True)
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plt.axis("equal")
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rx, ry = a_star_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_radius)
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a_star = AStarPlanner(ox, oy, grid_size, robot_radius)
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rx, ry = a_star.planning(sx, sy, gx, gy)
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if show_animation: # pragma: no cover
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plt.plot(rx, ry, "-r")
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@@ -1,7 +1,9 @@
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"""
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Dijkstra grid based planning
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Grid based Dijkstra planning
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author: Atsushi Sakai(@Atsushi_twi)
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"""
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import matplotlib.pyplot as plt
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@@ -9,210 +11,235 @@ import math
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show_animation = True
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class Dijkstra:
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class Node:
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def __init__(self, ox, oy, reso, rr):
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"""
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Initialize map for a star planning
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def __init__(self, x, y, cost, pind):
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self.x = x
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self.y = y
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self.cost = cost
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self.pind = pind
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ox: x position list of Obstacles [m]
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oy: y position list of Obstacles [m]
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reso: grid resolution [m]
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rr: robot radius[m]
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"""
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def __str__(self):
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return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
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self.reso = reso
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self.rr = rr
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self.calc_obstacle_map(ox, oy)
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self.motion = self.get_motion_model()
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class Node:
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def __init__(self, x, y, cost, pind):
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self.x = x # index of grid
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self.y = y # index of grid
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self.cost = cost
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self.pind = pind
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def dijkstra_planning(sx, sy, gx, gy, ox, oy, reso, rr):
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"""
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gx: goal x position [m]
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gx: goal x position [m]
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ox: x position list of Obstacles [m]
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oy: y position list of Obstacles [m]
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reso: grid resolution [m]
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rr: robot radius[m]
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"""
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def __str__(self):
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return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
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nstart = Node(round(sx / reso), round(sy / reso), 0.0, -1)
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ngoal = Node(round(gx / reso), round(gy / reso), 0.0, -1)
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ox = [iox / reso for iox in ox]
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oy = [ioy / reso for ioy in oy]
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def planning(self, sx, sy, gx, gy):
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"""
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dijkstra path search
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obmap, minx, miny, maxx, maxy, xw, yw = calc_obstacle_map(ox, oy, reso, rr)
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input:
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sx: start x position [m]
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sy: start y position [m]
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gx: goal x position [m]
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gx: goal x position [m]
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motion = get_motion_model()
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output:
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rx: x position list of the final path
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ry: y position list of the final path
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"""
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openset, closedset = dict(), dict()
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openset[calc_index(nstart, xw, minx, miny)] = nstart
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nstart = self.Node(self.calc_xyindex(sx, self.minx),
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self.calc_xyindex(sy, self.miny), 0.0, -1)
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ngoal = self.Node(self.calc_xyindex(gx, self.minx),
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self.calc_xyindex(gy, self.miny), 0.0, -1)
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while 1:
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c_id = min(openset, key=lambda o: openset[o].cost)
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current = openset[c_id]
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# print("current", current)
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openset, closedset = dict(), dict()
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openset[self.calc_index(nstart)] = nstart
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# show graph
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if show_animation:
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plt.plot(current.x * reso, current.y * reso, "xc")
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if len(closedset.keys()) % 10 == 0:
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plt.pause(0.001)
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while 1:
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c_id = min(openset, key=lambda o: openset[o].cost)
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current = openset[c_id]
|
||||
|
||||
if current.x == ngoal.x and current.y == ngoal.y:
|
||||
print("Find goal")
|
||||
ngoal.pind = current.pind
|
||||
ngoal.cost = current.cost
|
||||
break
|
||||
# show graph
|
||||
if show_animation: # pragma: no cover
|
||||
plt.plot(self.calc_position(current.x, self.minx),
|
||||
self.calc_position(current.y, self.miny), "xc")
|
||||
if len(closedset.keys()) % 10 == 0:
|
||||
plt.pause(0.001)
|
||||
|
||||
# Remove the item from the open set
|
||||
del openset[c_id]
|
||||
# Add it to the closed set
|
||||
closedset[c_id] = current
|
||||
if current.x == ngoal.x and current.y == ngoal.y:
|
||||
print("Find goal")
|
||||
ngoal.pind = current.pind
|
||||
ngoal.cost = current.cost
|
||||
break
|
||||
|
||||
# expand search grid based on motion model
|
||||
for i, _ in enumerate(motion):
|
||||
node = Node(current.x + motion[i][0], current.y + motion[i][1],
|
||||
current.cost + motion[i][2], c_id)
|
||||
n_id = calc_index(node, xw, minx, miny)
|
||||
# Remove the item from the open set
|
||||
del openset[c_id]
|
||||
|
||||
if not verify_node(node, obmap, minx, miny, maxx, maxy):
|
||||
continue
|
||||
# Add it to the closed set
|
||||
closedset[c_id] = current
|
||||
|
||||
if n_id in closedset:
|
||||
continue
|
||||
# Otherwise if it is already in the open set
|
||||
if n_id in openset:
|
||||
if openset[n_id].cost > node.cost:
|
||||
openset[n_id].cost = node.cost
|
||||
openset[n_id].pind = c_id
|
||||
else:
|
||||
openset[n_id] = node
|
||||
# expand search grid based on motion model
|
||||
for i, _ in enumerate(self.motion):
|
||||
node = self.Node(current.x + self.motion[i][0],
|
||||
current.y + self.motion[i][1],
|
||||
current.cost + self.motion[i][2], c_id)
|
||||
n_id = self.calc_index(node)
|
||||
|
||||
rx, ry = calc_final_path(ngoal, closedset, reso)
|
||||
if n_id in closedset:
|
||||
continue
|
||||
|
||||
return rx, ry
|
||||
if not self.verify_node(node):
|
||||
continue
|
||||
|
||||
if n_id not in openset:
|
||||
openset[n_id] = node # Discover a new node
|
||||
else:
|
||||
if openset[n_id].cost >= node.cost:
|
||||
# This path is the best until now. record it!
|
||||
openset[n_id] = node
|
||||
|
||||
def calc_final_path(ngoal, closedset, reso):
|
||||
# generate final course
|
||||
rx, ry = [ngoal.x * reso], [ngoal.y * reso]
|
||||
pind = ngoal.pind
|
||||
while pind != -1:
|
||||
n = closedset[pind]
|
||||
rx.append(n.x * reso)
|
||||
ry.append(n.y * reso)
|
||||
pind = n.pind
|
||||
rx, ry = self.calc_final_path(ngoal, closedset)
|
||||
|
||||
return rx, ry
|
||||
return rx, ry
|
||||
|
||||
def calc_final_path(self, ngoal, closedset):
|
||||
# generate final course
|
||||
rx, ry = [self.calc_position(ngoal.x, self.minx)], [
|
||||
self.calc_position(ngoal.y, self.miny)]
|
||||
pind = ngoal.pind
|
||||
while pind != -1:
|
||||
n = closedset[pind]
|
||||
rx.append(self.calc_position(n.x, self.minx))
|
||||
ry.append(self.calc_position(n.y, self.miny))
|
||||
pind = n.pind
|
||||
|
||||
def verify_node(node, obmap, minx, miny, maxx, maxy):
|
||||
return rx, ry
|
||||
|
||||
if obmap[node.x][node.y]:
|
||||
return False
|
||||
def calc_heuristic(self, n1, n2):
|
||||
w = 1.0 # weight of heuristic
|
||||
d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
|
||||
return d
|
||||
|
||||
if node.x < minx:
|
||||
return False
|
||||
elif node.y < miny:
|
||||
return False
|
||||
elif node.x > maxx:
|
||||
return False
|
||||
elif node.y > maxy:
|
||||
return False
|
||||
def calc_position(self, index, minp):
|
||||
pos = index*self.reso+minp
|
||||
return pos
|
||||
|
||||
return True
|
||||
def calc_xyindex(self, position, minp):
|
||||
return round((position - minp)/self.reso)
|
||||
|
||||
def calc_index(self, node):
|
||||
return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
|
||||
|
||||
def calc_obstacle_map(ox, oy, reso, vr):
|
||||
def verify_node(self, node):
|
||||
px = self.calc_position(node.x, self.minx)
|
||||
py = self.calc_position(node.y, self.miny)
|
||||
|
||||
minx = round(min(ox))
|
||||
miny = round(min(oy))
|
||||
maxx = round(max(ox))
|
||||
maxy = round(max(oy))
|
||||
# print("minx:", minx)
|
||||
# print("miny:", miny)
|
||||
# print("maxx:", maxx)
|
||||
# print("maxy:", maxy)
|
||||
if px < self.minx:
|
||||
return False
|
||||
elif py < self.miny:
|
||||
return False
|
||||
elif px >= self.maxx:
|
||||
return False
|
||||
elif py >= self.maxy:
|
||||
return False
|
||||
|
||||
xwidth = round(maxx - minx)
|
||||
ywidth = round(maxy - miny)
|
||||
# print("xwidth:", xwidth)
|
||||
# print("ywidth:", ywidth)
|
||||
if self.obmap[node.x][node.y]:
|
||||
return False
|
||||
|
||||
# obstacle map generation
|
||||
obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
|
||||
for ix in range(xwidth):
|
||||
x = ix + minx
|
||||
for iy in range(ywidth):
|
||||
y = iy + miny
|
||||
# print(x, y)
|
||||
for iox, ioy in zip(ox, oy):
|
||||
d = math.sqrt((iox - x)**2 + (ioy - y)**2)
|
||||
if d <= vr / reso:
|
||||
obmap[ix][iy] = True
|
||||
break
|
||||
return True
|
||||
|
||||
return obmap, minx, miny, maxx, maxy, xwidth, ywidth
|
||||
def calc_obstacle_map(self, ox, oy):
|
||||
|
||||
self.minx = round(min(ox))
|
||||
self.miny = round(min(oy))
|
||||
self.maxx = round(max(ox))
|
||||
self.maxy = round(max(oy))
|
||||
print("minx:", self.minx)
|
||||
print("miny:", self.miny)
|
||||
print("maxx:", self.maxx)
|
||||
print("maxy:", self.maxy)
|
||||
|
||||
def calc_index(node, xwidth, xmin, ymin):
|
||||
return (node.y - ymin) * xwidth + (node.x - xmin)
|
||||
self.xwidth = round((self.maxx - self.minx)/self.reso)
|
||||
self.ywidth = round((self.maxy - self.miny)/self.reso)
|
||||
print("xwidth:", self.xwidth)
|
||||
print("ywidth:", self.ywidth)
|
||||
|
||||
# obstacle map generation
|
||||
self.obmap = [[False for i in range(self.ywidth)]
|
||||
for i in range(self.xwidth)]
|
||||
for ix in range(self.xwidth):
|
||||
x = self.calc_position(ix, self.minx)
|
||||
for iy in range(self.ywidth):
|
||||
y = self.calc_position(iy, self.miny)
|
||||
for iox, ioy in zip(ox, oy):
|
||||
d = math.sqrt((iox - x)**2 + (ioy - y)**2)
|
||||
if d <= self.rr:
|
||||
self.obmap[ix][iy] = True
|
||||
break
|
||||
|
||||
def get_motion_model():
|
||||
# dx, dy, cost
|
||||
motion = [[1, 0, 1],
|
||||
[0, 1, 1],
|
||||
[-1, 0, 1],
|
||||
[0, -1, 1],
|
||||
[-1, -1, math.sqrt(2)],
|
||||
[-1, 1, math.sqrt(2)],
|
||||
[1, -1, math.sqrt(2)],
|
||||
[1, 1, math.sqrt(2)]]
|
||||
def get_motion_model(self):
|
||||
# dx, dy, cost
|
||||
motion = [[1, 0, 1],
|
||||
[0, 1, 1],
|
||||
[-1, 0, 1],
|
||||
[0, -1, 1],
|
||||
[-1, -1, math.sqrt(2)],
|
||||
[-1, 1, math.sqrt(2)],
|
||||
[1, -1, math.sqrt(2)],
|
||||
[1, 1, math.sqrt(2)]]
|
||||
|
||||
return motion
|
||||
return motion
|
||||
|
||||
|
||||
def main():
|
||||
print(__file__ + " start!!")
|
||||
|
||||
# start and goal position
|
||||
sx = 10.0 # [m]
|
||||
sy = 10.0 # [m]
|
||||
sx = -5.0 # [m]
|
||||
sy = -5.0 # [m]
|
||||
gx = 50.0 # [m]
|
||||
gy = 50.0 # [m]
|
||||
grid_size = 1.0 # [m]
|
||||
robot_size = 1.0 # [m]
|
||||
grid_size = 2.0 # [m]
|
||||
robot_radius = 1.0 # [m]
|
||||
|
||||
ox = []
|
||||
oy = []
|
||||
|
||||
for i in range(60):
|
||||
# set obstacle positions
|
||||
ox, oy = [], []
|
||||
for i in range(-10, 60):
|
||||
ox.append(i)
|
||||
oy.append(0.0)
|
||||
for i in range(60):
|
||||
oy.append(-10.0)
|
||||
for i in range(-10, 60):
|
||||
ox.append(60.0)
|
||||
oy.append(i)
|
||||
for i in range(61):
|
||||
for i in range(-10, 61):
|
||||
ox.append(i)
|
||||
oy.append(60.0)
|
||||
for i in range(61):
|
||||
ox.append(0.0)
|
||||
for i in range(-10, 61):
|
||||
ox.append(-10.0)
|
||||
oy.append(i)
|
||||
for i in range(40):
|
||||
for i in range(-10, 40):
|
||||
ox.append(20.0)
|
||||
oy.append(i)
|
||||
for i in range(40):
|
||||
for i in range(0, 40):
|
||||
ox.append(40.0)
|
||||
oy.append(60.0 - i)
|
||||
|
||||
if show_animation:
|
||||
if show_animation: # pragma: no cover
|
||||
plt.plot(ox, oy, ".k")
|
||||
plt.plot(sx, sy, "xr")
|
||||
plt.plot(sx, sy, "og")
|
||||
plt.plot(gx, gy, "xb")
|
||||
plt.grid(True)
|
||||
plt.axis("equal")
|
||||
|
||||
rx, ry = dijkstra_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_size)
|
||||
dijkstra = Dijkstra(ox, oy, grid_size, robot_radius)
|
||||
rx, ry = dijkstra.planning(sx, sy, gx, gy)
|
||||
|
||||
if show_animation:
|
||||
if show_animation: # pragma: no cover
|
||||
plt.plot(rx, ry, "-r")
|
||||
plt.show()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user