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@@ -13,6 +13,7 @@ sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
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from rrt_2D import env
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from rrt_2D import env
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from rrt_2D import plotting
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from rrt_2D import plotting
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+from rrt_2D import utils
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class Node:
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class Node:
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@@ -24,18 +25,19 @@ class Node:
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class RrtStar:
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class RrtStar:
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- def __init__(self, x_start, x_goal, expand_len,
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- goal_sample_rate, search_radius, iter_limit):
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+ def __init__(self, x_start, x_goal, step_len,
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+ goal_sample_rate, search_radius, iter_max):
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self.xI = Node(x_start)
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self.xI = Node(x_start)
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self.xG = Node(x_goal)
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self.xG = Node(x_goal)
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- self.expand_len = expand_len
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+ self.step_len = step_len
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self.goal_sample_rate = goal_sample_rate
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self.goal_sample_rate = goal_sample_rate
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self.search_radius = search_radius
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self.search_radius = search_radius
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- self.iter_limit = iter_limit
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+ self.iter_max = iter_max
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self.vertex = [self.xI]
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self.vertex = [self.xI]
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self.env = env.Env()
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self.env = env.Env()
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self.plotting = plotting.Plotting(x_start, x_goal)
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self.plotting = plotting.Plotting(x_start, x_goal)
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+ self.utils = utils.Utils()
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self.x_range = self.env.x_range
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self.x_range = self.env.x_range
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self.y_range = self.env.y_range
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self.y_range = self.env.y_range
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@@ -44,12 +46,12 @@ class RrtStar:
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self.obs_boundary = self.env.obs_boundary
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self.obs_boundary = self.env.obs_boundary
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def planning(self):
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def planning(self):
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- for k in range(self.iter_limit):
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+ for k in range(self.iter_max):
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node_rand = self.random_state(self.goal_sample_rate)
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node_rand = self.random_state(self.goal_sample_rate)
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node_near = self.nearest_neighbor(self.vertex, node_rand)
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node_near = self.nearest_neighbor(self.vertex, node_rand)
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node_new = self.new_state(node_near, node_rand)
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node_new = self.new_state(node_near, node_rand)
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- if node_new and not self.check_collision(node_new):
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+ if node_new and not self.utils.is_collision(node_near, node_new):
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neighbor_index = self.find_near_neighbor(node_new)
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neighbor_index = self.find_near_neighbor(node_new)
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if neighbor_index:
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if neighbor_index:
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node_new = self.choose_parent(node_new, neighbor_index)
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node_new = self.choose_parent(node_new, neighbor_index)
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@@ -59,10 +61,28 @@ class RrtStar:
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index = self.search_goal_parent()
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index = self.search_goal_parent()
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return self.extract_path(self.vertex[index])
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return self.extract_path(self.vertex[index])
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+ def check_collision(self, node_end):
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+ for (ox, oy, r) in self.obs_circle:
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+ if math.hypot(node_end.x - ox, node_end.y - oy) <= r:
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+ return True
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+
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+ for (ox, oy, w, h) in self.obs_rectangle:
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+ if 0 <= (node_end.x - ox) <= w and 0 <= (node_end.y - oy) <= h:
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+ return True
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+
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+ for (ox, oy, w, h) in self.obs_boundary:
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+ if 0 <= (node_end.x - ox) <= w and 0 <= (node_end.y - oy) <= h:
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+ return True
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+
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+ return False
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+
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def random_state(self, goal_sample_rate):
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def random_state(self, goal_sample_rate):
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+ delta = self.utils.delta
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+
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if np.random.random() > goal_sample_rate:
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if np.random.random() > goal_sample_rate:
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- return Node((np.random.uniform(self.x_range[0], self.x_range[1]),
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- np.random.uniform(self.y_range[0], self.y_range[1])))
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+ return Node((np.random.uniform(self.x_range[0] + delta, self.x_range[1] - delta),
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+ np.random.uniform(self.y_range[0] + delta, self.y_range[1] - delta)))
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+
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return self.xG
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return self.xG
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def nearest_neighbor(self, node_list, n):
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def nearest_neighbor(self, node_list, n):
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@@ -70,19 +90,18 @@ class RrtStar:
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for nd in node_list]))]
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for nd in node_list]))]
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def new_state(self, node_start, node_goal):
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def new_state(self, node_start, node_goal):
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- node_new = Node((node_start.x, node_start.y))
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- dist, theta = self.get_distance_and_angle(node_new, node_goal)
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- dist = min(self.expand_len, dist)
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+ dist, theta = self.get_distance_and_angle(node_start, node_goal)
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- node_new.x += dist * math.cos(theta)
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- node_new.y += dist * math.sin(theta)
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+ dist = min(self.step_len, dist)
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+ node_new = Node((node_start.x + dist * math.cos(theta),
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+ node_start.y + dist * math.sin(theta)))
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node_new.parent = node_start
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node_new.parent = node_start
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return node_new
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return node_new
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def find_near_neighbor(self, node_new):
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def find_near_neighbor(self, node_new):
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n = len(self.vertex) + 1
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n = len(self.vertex) + 1
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- r = min(self.search_radius * math.sqrt((math.log(n) / n)), self.expand_len)
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+ r = min(self.search_radius * math.sqrt((math.log(n) / n)), self.step_len)
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dist_table = [math.hypot(nd.x - node_new.x, nd.y - node_new.y) for nd in self.vertex]
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dist_table = [math.hypot(nd.x - node_new.x, nd.y - node_new.y) for nd in self.vertex]
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@@ -103,7 +122,7 @@ class RrtStar:
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def search_goal_parent(self):
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def search_goal_parent(self):
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dist_list = [math.hypot(n.x - self.xG.x, n.y - self.xG.y) for n in self.vertex]
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dist_list = [math.hypot(n.x - self.xG.x, n.y - self.xG.y) for n in self.vertex]
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- node_index = [dist_list.index(i) for i in dist_list if i <= self.expand_len]
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+ node_index = [dist_list.index(i) for i in dist_list if i <= self.step_len]
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if node_index:
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if node_index:
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cost_list = [dist_list[i] + self.vertex[i].cost for i in node_index]
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cost_list = [dist_list[i] + self.vertex[i].cost for i in node_index]
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@@ -140,21 +159,6 @@ class RrtStar:
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return path
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return path
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- def check_collision(self, node_end):
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- for (ox, oy, r) in self.obs_circle:
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- if math.hypot(node_end.x - ox, node_end.y - oy) <= r:
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- return True
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-
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- for (ox, oy, w, h) in self.obs_rectangle:
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- if 0 <= (node_end.x - ox) <= w and 0 <= (node_end.y - oy) <= h:
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- return True
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-
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- for (ox, oy, w, h) in self.obs_boundary:
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- if 0 <= (node_end.x - ox) <= w and 0 <= (node_end.y - oy) <= h:
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- return True
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-
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- return False
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-
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@staticmethod
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@staticmethod
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def get_distance_and_angle(node_start, node_end):
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def get_distance_and_angle(node_start, node_end):
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dx = node_end.x - node_start.x
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dx = node_end.x - node_start.x
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@@ -166,7 +170,7 @@ def main():
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x_start = (2, 2) # Starting node
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x_start = (2, 2) # Starting node
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x_goal = (49, 28) # Goal node
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x_goal = (49, 28) # Goal node
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- rrt_star = RrtStar(x_start, x_goal, 1, 0.1, 10, 5000)
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+ rrt_star = RrtStar(x_start, x_goal, 1, 0.1, 10, 20000)
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path = rrt_star.planning()
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path = rrt_star.planning()
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if path:
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if path:
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