mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-13 15:48:13 -05:00
1cb45a5d3b
* Fixed multitype list * Cast to float * Reverted to all floats * Moved all remaining to float
260 lines
7.7 KiB
Python
260 lines
7.7 KiB
Python
"""
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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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import math
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show_animation = True
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class Dijkstra:
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def __init__(self, ox, oy, resolution, robot_radius):
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"""
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Initialize map for a star planning
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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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resolution: grid resolution [m]
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rr: robot radius[m]
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"""
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self.min_x = None
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self.min_y = None
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self.max_x = None
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self.max_y = None
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self.x_width = None
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self.y_width = None
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self.obstacle_map = None
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self.resolution = resolution
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self.robot_radius = robot_radius
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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, parent_index):
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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.parent_index = parent_index # index of previous Node
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def __str__(self):
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return str(self.x) + "," + str(self.y) + "," + str(
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self.cost) + "," + str(self.parent_index)
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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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input:
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s_x: start x position [m]
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s_y: 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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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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start_node = self.Node(self.calc_xy_index(sx, self.min_x),
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self.calc_xy_index(sy, self.min_y), 0.0, -1)
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goal_node = self.Node(self.calc_xy_index(gx, self.min_x),
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self.calc_xy_index(gy, self.min_y), 0.0, -1)
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open_set, closed_set = dict(), dict()
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open_set[self.calc_index(start_node)] = start_node
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while True:
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c_id = min(open_set, key=lambda o: open_set[o].cost)
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current = open_set[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.min_x),
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self.calc_position(current.y, self.min_y), "xc")
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# for stopping simulation with the esc key.
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plt.gcf().canvas.mpl_connect(
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'key_release_event',
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lambda event: [exit(0) if event.key == 'escape' else None])
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if len(closed_set.keys()) % 10 == 0:
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plt.pause(0.001)
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if current.x == goal_node.x and current.y == goal_node.y:
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print("Find goal")
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goal_node.parent_index = current.parent_index
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goal_node.cost = current.cost
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break
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# Remove the item from the open set
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del open_set[c_id]
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# Add it to the closed set
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closed_set[c_id] = current
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# expand search grid based on motion model
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for move_x, move_y, move_cost in self.motion:
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node = self.Node(current.x + move_x,
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current.y + move_y,
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current.cost + move_cost, c_id)
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n_id = self.calc_index(node)
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if n_id in closed_set:
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continue
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if not self.verify_node(node):
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continue
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if n_id not in open_set:
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open_set[n_id] = node # Discover a new node
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else:
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if open_set[n_id].cost >= node.cost:
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# This path is the best until now. record it!
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open_set[n_id] = node
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rx, ry = self.calc_final_path(goal_node, closed_set)
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return rx, ry
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def calc_final_path(self, goal_node, closed_set):
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# generate final course
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rx, ry = [self.calc_position(goal_node.x, self.min_x)], [
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self.calc_position(goal_node.y, self.min_y)]
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parent_index = goal_node.parent_index
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while parent_index != -1:
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n = closed_set[parent_index]
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rx.append(self.calc_position(n.x, self.min_x))
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ry.append(self.calc_position(n.y, self.min_y))
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parent_index = n.parent_index
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return rx, ry
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def calc_position(self, index, minp):
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pos = index * self.resolution + minp
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return pos
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def calc_xy_index(self, position, minp):
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return round((position - minp) / self.resolution)
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def calc_index(self, node):
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return (node.y - self.min_y) * self.x_width + (node.x - self.min_x)
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def verify_node(self, node):
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px = self.calc_position(node.x, self.min_x)
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py = self.calc_position(node.y, self.min_y)
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if px < self.min_x:
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return False
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if py < self.min_y:
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return False
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if px >= self.max_x:
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return False
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if py >= self.max_y:
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return False
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if self.obstacle_map[node.x][node.y]:
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return False
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return True
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def calc_obstacle_map(self, ox, oy):
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self.min_x = round(min(ox))
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self.min_y = round(min(oy))
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self.max_x = round(max(ox))
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self.max_y = round(max(oy))
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print("min_x:", self.min_x)
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print("min_y:", self.min_y)
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print("max_x:", self.max_x)
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print("max_y:", self.max_y)
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self.x_width = round((self.max_x - self.min_x) / self.resolution)
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self.y_width = round((self.max_y - self.min_y) / self.resolution)
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print("x_width:", self.x_width)
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print("y_width:", self.y_width)
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# obstacle map generation
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self.obstacle_map = [[False for _ in range(self.y_width)]
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for _ in range(self.x_width)]
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for ix in range(self.x_width):
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x = self.calc_position(ix, self.min_x)
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for iy in range(self.y_width):
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y = self.calc_position(iy, self.min_y)
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for iox, ioy in zip(ox, oy):
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d = math.hypot(iox - x, ioy - y)
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if d <= self.robot_radius:
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self.obstacle_map[ix][iy] = True
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break
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@staticmethod
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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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def main():
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print(__file__ + " start!!")
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# start and goal position
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sx = -5.0 # [m]
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sy = -5.0 # [m]
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gx = 50.0 # [m]
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gy = 50.0 # [m]
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grid_size = 2.0 # [m]
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robot_radius = 1.0 # [m]
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# set obstacle positions
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ox, oy = [], []
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for i in range(-10, 60):
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ox.append(float(i))
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oy.append(-10.0)
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for i in range(-10, 60):
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ox.append(60.0)
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oy.append(float(i))
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for i in range(-10, 61):
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ox.append(float(i))
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oy.append(60.0)
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for i in range(-10, 61):
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ox.append(-10.0)
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oy.append(float(i))
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for i in range(-10, 40):
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ox.append(20.0)
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oy.append(float(i))
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for i in range(0, 40):
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ox.append(40.0)
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oy.append(60.0 - i)
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if show_animation: # pragma: no cover
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plt.plot(ox, oy, ".k")
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plt.plot(sx, sy, "og")
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plt.plot(gx, gy, "xb")
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plt.grid(True)
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plt.axis("equal")
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dijkstra = Dijkstra(ox, oy, grid_size, robot_radius)
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rx, ry = dijkstra.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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plt.pause(0.01)
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plt.show()
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if __name__ == '__main__':
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main()
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