update RRT_2D

Sampling-based Planning/rrt_2D/rrt_star.py

@@ -0,0 +1,169 @@
+from rrt_2D import env
+from rrt_2D import plotting
+
+import numpy as np
+import math
+
+
+class Node:
+    def __init__(self, n):
+        self.x = n[0]
+        self.y = n[1]
+        self.cost = 0.0
+        self.parent = None
+
+
+class RrtStar:
+    def __init__(self, x_start, x_goal, expand_len,
+                 goal_sample_rate, search_radius, iter_limit):
+        self.xI = Node(x_start)
+        self.xG = Node(x_goal)
+        self.expand_len = expand_len
+        self.goal_sample_rate = goal_sample_rate
+        self.search_radius = search_radius
+        self.iter_limit = iter_limit
+        self.vertex = [self.xI]
+
+        self.env = env.Env()
+        self.plotting = plotting.Plotting(x_start, x_goal)
+
+        self.x_range = self.env.x_range
+        self.y_range = self.env.y_range
+        self.obs_circle = self.env.obs_circle
+        self.obs_rectangle = self.env.obs_rectangle
+        self.obs_boundary = self.env.obs_boundary
+
+    def planning(self):
+        for k in range(self.iter_limit):
+            node_rand = self.random_state(self.goal_sample_rate)
+            node_near = self.nearest_neighbor(self.vertex, node_rand)
+            node_new = self.new_state(node_near, node_rand)
+
+            if node_new and not self.check_collision(node_new):
+                neighbor_index = self.find_near_neighbor(node_new)
+                if neighbor_index:
+                    node_new = self.choose_parent(node_new, neighbor_index)
+                    self.vertex.append(node_new)
+                    self.rewire(node_new, neighbor_index)
+
+        index = self.search_goal_parent()
+        return self.extract_path(self.vertex[index])
+
+    def random_state(self, goal_sample_rate):
+        if np.random.random() > goal_sample_rate:
+            return Node((np.random.uniform(self.x_range[0], self.x_range[1]),
+                         np.random.uniform(self.y_range[0], self.y_range[1])))
+        return self.xG
+
+    def nearest_neighbor(self, node_list, n):
+        return self.vertex[int(np.argmin([math.hypot(nd.x - n.x, nd.y - n.y)
+                                          for nd in node_list]))]
+
+    def new_state(self, node_start, node_goal):
+        node_new = Node((node_start.x, node_start.y))
+        dist, theta = self.get_distance_and_angle(node_new, node_goal)
+        dist = min(self.expand_len, dist)
+
+        node_new.x += dist * math.cos(theta)
+        node_new.y += dist * math.sin(theta)
+        node_new.parent = node_start
+
+        return node_new
+
+    def find_near_neighbor(self, node_new):
+        n = len(self.vertex) + 1
+        r = min(self.search_radius * math.sqrt((math.log(n) / n)), self.expand_len)
+
+        dist_table = [math.hypot(nd.x - node_new.x, nd.y - node_new.y) for nd in self.vertex]
+
+        return [dist_table.index(d) for d in dist_table if d <= r]
+
+    def choose_parent(self, node_new, neighbor_index):
+        cost = []
+
+        for i in neighbor_index:
+            node_neighbor = self.vertex[i]
+            cost.append(self.get_new_cost(node_neighbor, node_new))
+
+        cost_min_index = neighbor_index[int(np.argmin(cost))]
+        node_new = self.new_state(self.vertex[cost_min_index], node_new)
+        node_new.cost = min(cost)
+
+        return node_new
+
+    def search_goal_parent(self):
+        dist_list = [math.hypot(n.x - self.xG.x, n.y - self.xG.y) for n in self.vertex]
+        node_index = [dist_list.index(i) for i in dist_list if i <= self.expand_len]
+
+        if node_index:
+            cost_list = [dist_list[i] + self.vertex[i].cost for i in node_index]
+            return node_index[int(np.argmin(cost_list))]
+
+        return None
+
+    def rewire(self, node_new, neighbor_index):
+        for i in neighbor_index:
+            node_neighbor = self.vertex[i]
+            new_cost = self.get_new_cost(node_new, node_neighbor)
+
+            if node_neighbor.cost > new_cost:
+                self.vertex[i] = self.new_state(node_new, node_neighbor)
+                self.propagate_cost_to_leaves(node_new)
+
+    def get_new_cost(self, node_start, node_end):
+        dist, _ = self.get_distance_and_angle(node_start, node_end)
+        return node_start.cost + dist
+
+    def propagate_cost_to_leaves(self, parent_node):
+        for node in self.vertex:
+            if node.parent == parent_node:
+                node.cost = self.get_new_cost(parent_node, node)
+                self.propagate_cost_to_leaves(node)
+
+    def extract_path(self, node_end):
+        path = [[self.xG.x, self.xG.y]]
+        node = node_end
+        while node.parent is not None:
+            path.append([node.x, node.y])
+            node = node.parent
+        path.append([node.x, node.y])
+
+        return path
+
+    def check_collision(self, node_end):
+        for (ox, oy, r) in self.obs_circle:
+            if math.hypot(node_end.x - ox, node_end.y - oy) <= r:
+                return True
+
+        for (ox, oy, w, h) in self.obs_rectangle:
+            if 0 <= (node_end.x - ox) <= w and 0 <= (node_end.y - oy) <= h:
+                return True
+
+        for (ox, oy, w, h) in self.obs_boundary:
+            if 0 <= (node_end.x - ox) <= w and 0 <= (node_end.y - oy) <= h:
+                return True
+
+        return False
+
+    @staticmethod
+    def get_distance_and_angle(node_start, node_end):
+        dx = node_end.x - node_start.x
+        dy = node_end.y - node_start.y
+        return math.hypot(dx, dy), math.atan2(dy, dx)
+
+
+def main():
+    x_start = (2, 2)  # Starting node
+    x_goal = (49, 28)  # Goal node
+
+    rrt_star = RrtStar(x_start, x_goal, 1, 0.1, 10, 5000)
+    path = rrt_star.planning()
+
+    if path:
+        rrt_star.plotting.animation(rrt_star.vertex, path)
+    else:
+        print("No Path Found!")
+
+
+if __name__ == '__main__':
+    main()